CN113538251B - Method and device for determining medical image splicing abnormity - Google Patents
Method and device for determining medical image splicing abnormity Download PDFInfo
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Abstract
The application provides a method for determining medical image splicing abnormity, which comprises the following steps: acquiring a first image sequence scanned at a first subsection bed and a second image sequence scanned at a second subsection bed, wherein the first image sequence and the second image sequence are image sequences to be spliced; determining a spliced sheet, wherein the spliced sheet is an image in the second image sequence and is used for splicing the first image sequence and the second image sequence; analyzing the continuity of at least one first image closest to the spliced sheet in the first image sequence and at least one second image closest to the spliced sheet in the second image sequence to obtain a continuity analysis result; and determining whether the first image sequence and the second image sequence are abnormally spliced or not according to the continuity analysis result. According to the method for determining the medical image splicing abnormity, the continuity of the multi-bed splicing images is judged, so that the automatic reminding of the medical image splicing abnormity is realized, a doctor and an operator are helped to quickly confirm the correctness of the multi-bed image splicing, and the efficiency and the quality of image processing are improved.
Description
Technical Field
The present application relates to the field of medical image technology, and more particularly, to a method and an apparatus for determining medical image stitching abnormality.
Background
DICOM (Digital Imaging and Communications in Medicine) is an international standard for medical images and related information (ISO 12052). It defines a medical image format that can be used for data exchange with a quality that meets clinical needs.
In the process of scanning human body images, because the axial length of a human body is greater than the length of a scanner, the bed positions need to be scanned one by one, and therefore the images scanned by a plurality of bed positions need to be spliced. The mode of current many beds image concatenation is mostly artifical concatenation or is accomplished by scanning apparatus, and doctor and operating personnel's work load is big, probably because the concatenation position relation of paying attention to the image causes the wrong concatenation of image at the in-process of concatenation.
Disclosure of Invention
In view of this, the embodiment of the present application provides a method and an apparatus for determining a medical image stitching abnormality, which can give a prompt for a situation that a wrong stitching occurs in a multi-bed stitching process of a medical image.
In a first aspect, an embodiment of the present application provides a method for determining a medical image stitching abnormality, including: acquiring a first image sequence scanned at a first subsection bed and a second image sequence scanned at a second subsection bed, wherein the first image sequence and the second image sequence are image sequences to be spliced; determining a spliced sheet, wherein the spliced sheet is an image in the second image sequence and is used for splicing the first image sequence and the second image sequence; analyzing the continuity of at least one first image closest to the spliced sheet in the first image sequence and at least one second image closest to the spliced sheet in the second image sequence to obtain a continuity analysis result; and determining whether the first image sequence and the second image sequence are abnormally spliced or not according to the continuity analysis result.
In some embodiments of the present application, determining the splice comprises: if the first image sequence and the second image sequence are not overlapped, selecting a second image which is closest to the first image sequence in the second image sequence as a spliced sheet; and if the first image sequence and the second image sequence are overlapped, selecting a second image which is closest to the first image sequence in the images which are not overlapped with the first image sequence in the second image sequence as a spliced sheet.
In some embodiments of the present application, analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result includes: determining the distance difference of the spliced sheets according to the first distance from the spliced sheets to the first images adjacent to the spliced sheets in the first image sequence and the second distance from the spliced sheets to the second images adjacent to the spliced sheets in the second image sequence; and if the distance difference is larger than a first preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not achieved.
In some embodiments of the present application, analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result includes: fitting the positions of the spliced sheets by adopting an interpolation method to obtain fitted spliced sheets; determining the difference of the first image of the spliced sheet and the fitted spliced sheet; and if the first image difference is larger than a second preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not available.
In some embodiments of the present application, fitting the positions of the spliced sheets by using an interpolation method to obtain fitted spliced sheets includes: and fitting the n first images adjacent to the spliced sheet and the n second images adjacent to the spliced sheet according to a cubic algorithm to obtain a fitted spliced sheet, wherein n is an integer greater than 0.
In some embodiments of the present application, analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result, further includes: determining the distance difference of the spliced sheets according to the first distance from the spliced sheets to the first images adjacent to the spliced sheets in the first image sequence and the second distance from the spliced sheets to the second images adjacent to the spliced sheets in the second image sequence; weighting the distance difference and the first image difference to obtain a first continuity difference; if the first continuity difference is greater than a third predetermined threshold, it is determined that there is no continuity between the first image sequence and the second image sequence.
In some embodiments of the present application, analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result includes: determining a second image difference between the spliced sheet and at least one first image adjacent to the spliced sheet in the first image sequence according to a cubic algorithm; determining a third image difference between the spliced sheet and at least one second image adjacent to the spliced sheet in the second image sequence according to a cubic algorithm; determining a second continuity difference according to the second image difference and the third image difference; and if the second continuity difference is larger than a preset fourth threshold, determining that the continuity between the first image sequence and the second image sequence is not achieved.
In a second aspect, an embodiment of the present application provides an apparatus for determining medical image stitching abnormality, including: the acquisition module is used for acquiring a first image sequence scanned at a first subsection bed and a second image sequence scanned at a second subsection bed, wherein the first image sequence and the second image sequence are image sequences to be spliced; the first determining module is used for determining a spliced sheet, wherein the spliced sheet is an image in the second image sequence and is used for splicing the first image sequence and the second image sequence; the analysis module is used for analyzing the continuity of at least one first image closest to the spliced sheet in the first image sequence and at least one second image closest to the spliced sheet in the second image sequence to obtain a continuity analysis result; and the second determining module is used for determining whether the first image sequence and the second image sequence are abnormally spliced or not according to the continuity analysis result.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where the storage medium stores a computer program, and the computer program is used to execute the method for determining a medical image stitching abnormality according to the first aspect.
In a fourth aspect, an embodiment of the present application provides an electronic device, including: a processor; a memory for storing processor executable instructions, wherein the processor is configured to perform the method for determining medical image stitching abnormalities of the first aspect.
According to the method for determining the medical image splicing abnormity, the continuity of the multi-bed splicing images is judged, so that the automatic reminding of the medical image splicing abnormity is realized, a doctor and an operator are helped to quickly confirm the correctness of the multi-bed image splicing, and the efficiency and the quality of image processing are improved.
Drawings
Fig. 1 is a schematic diagram of an implementation environment for determining medical image stitching abnormality according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a method for determining medical image stitching abnormality according to an embodiment of the present application.
Fig. 3 is a schematic flowchart of determining a spliced sheet according to an embodiment of the present application.
Fig. 4 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
Fig. 5 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
Fig. 6 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
Fig. 7 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
Fig. 8 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
Fig. 9a is a schematic diagram illustrating a medical image multi-bed scanning process according to an exemplary embodiment of the present application.
Fig. 9b is a schematic diagram of an image with continuity according to an exemplary embodiment of the present application.
Fig. 9c is a schematic diagram illustrating different overlapping situations of multi-bed images according to an exemplary embodiment of the present application.
FIG. 9d is a schematic illustration of a spliced sheet determined under different overlapping conditions as provided by an exemplary embodiment of the present application.
Fig. 10 is a schematic structural diagram of an apparatus for determining a medical image stitching abnormality according to an embodiment of the present application.
Fig. 11 is a block diagram illustrating an electronic device for executing a method for determining medical image stitching abnormalities according to an exemplary embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustration purposes only and are not intended to limit the scope of the present application.
The term "include" and its variants, as used herein, are intended to be inclusive in an open-ended manner, i.e., "including but not limited to". The term "according to" is "at least partially according to". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment". Relevant definitions for other terms will be given in the following description.
The embodiment of the application can be used in a Medical Imaging Reading System (MIRS). In the aspect of image picture management, the medical image film reading system supports multi-center image uploading, supports image query, and performs auditing and quality control management on the uploaded images. In the aspect of reading management, the reading flow design, the distribution, the tracking and the query of multi-level reading are supported, and multiple reading is supported. In the whole business process, the uploading, the examination and the film reading of the images are intelligently counted and managed, and the image state and the film reading progress are followed in real time.
The medical image may be referred to as a medical image, and may be a medical image such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and the like, and the embodiment of the present invention is not particularly limited thereto.
The DICOM image file structure mainly comprises a file header and a data set. The header is used to indicate whether the file is a DICOM image file. The data set includes: data related to the imaging instance, such as the name of the subject (e.g., patient), the imaging modality, the image size, and image pixel data of the imaging instance. The embodiment of the present application does not limit the specific form of the medical image, and may be an original medical image, a preprocessed medical image, or a part of the original medical image.
When a patient is scanned, the patient generally lies on the back, on the side or on the front on a bed board, and transverse scanning is performed through the access aperture of the scanner. Fig. 9a is a schematic diagram illustrating a medical image multi-bed scanning process according to an exemplary embodiment of the present application. As shown in fig. 9a, since the axial length of the aperture of the scanner is often smaller than the axial length of the human body, the sampling region cannot cover the entire axial range of the human body at one time, and therefore a multi-bed scanning mode is required.
After the patient is scanned by multiple beds, the scanning images of the beds need to be spliced. The applicant finds that the existing multi-bed image stitching mode is mostly finished by manual stitching or scanning equipment in the process of researching multi-bed image stitching, for example, doctors or operators input complex parameters such as status information and sequence information of images into the equipment and then the equipment performs stitching. For the splicing mode, on one hand, the workload of doctors and operators is large, the generated spliced images cannot be carefully checked, and the images are likely to be spliced wrongly because the splicing position relation of the images is not noticed in the splicing process; on the other hand, in the splicing process, analysis is not performed according to the slicing continuity of the image, and a splicing abnormality prompt cannot be given when the splicing is abnormal. Therefore, the image splicing process often occurs with wrong splicing.
In order to solve the above problem, the present application provides a method for determining medical image stitching abnormality.
FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present application. The implementation environment includes a CT scanner 110 and a computer device 120.
The computer device 120 may acquire medical images from the CT scanner 110. For example, the computer device 120 may communicate with the CT scanner 110 over a wired or wireless network.
The CT scanner 110 is used for performing X-ray scanning on human tissue to obtain a CT medical image of the human tissue. In an embodiment, the CT scanner 110 performs multi-bed scanning on the head, the trunk, and the legs of the human body, respectively, and performs stitching to obtain a stitched CT medical image of the human body.
The computer device 120 may be a general-purpose computer or a computer device composed of an application-specific integrated circuit, and the like, which is not limited in this embodiment. For example, the Computer device 120 may be a mobile terminal device such as a tablet Computer, or may be a Personal Computer (PC), such as a laptop portable Computer and a desktop Computer.
One skilled in the art will appreciate that the number of CT scanners 110 may be one or more, and the types may be the same or different. For example, there may be one CT scanner 110, or tens or hundreds of CT scanners 110, or more. Further, the number of the above-described computer devices 120 may be one or more, and the types thereof may be the same or different. For example, the number of the computer devices 120 may be one, or the number of the computer devices 120 may be several tens or hundreds, or more. The number and the type of the CT scanners 110 and the number and the type of the computer devices 120 are not limited in the embodiments of the present application.
In some alternative embodiments, the computer device 120 acquires medical images from the CT scanner 110, acquires a first image sequence scanned at the first segmentation bed and acquires a second image sequence scanned at the second segmentation bed, determines a splice sheet, analyzes continuity of at least one first image in the first image sequence closest to the splice sheet and at least one second image in the second image sequence closest to the splice sheet, obtains a continuity analysis result, and determines whether the first image sequence and the second image sequence are abnormally spliced according to the continuity analysis result.
Fig. 2 schematically shows a flowchart of a method for determining medical image stitching abnormality according to an embodiment of the present invention. The method described in fig. 2 is performed by a computing device (e.g., a server), but the embodiments of the present application are not limited thereto. The server may be one server, or may be composed of a plurality of servers, or may be a virtualization platform, or a cloud computing service center, which is not limited in this embodiment of the present application. As shown in fig. 2, the method includes the following steps:
s210: the method comprises the steps of obtaining a first image sequence scanned at a first subsection bed and obtaining a second image sequence scanned at a second subsection bed, wherein the first image sequence and the second image sequence are image sequences to be spliced.
Since the axial length of the aperture of the scanner is smaller than the length of the human body, the scanning needs to be performed in a bed-by-bed segmented manner when the human body is scanned, as shown in fig. 9 a. For example, the first segmented bed may be a position where the head of a certain patient is located, and the second segmented bed may be a position where the chest of the patient is located, and accordingly, the first image sequence is a plurality of first images scanned for the head region of the patient, the second image sequence is a plurality of second images scanned for the chest region of the patient, and after the scanning is completed, the first image sequence and the second image sequence need to be stitched, that is, the first image sequence and the second image sequence are image sequences to be stitched.
The first image sequence and the second image sequence of the present application may also be segmented scanned images of other human body parts, for example, the first image sequence is a segmented scanned image of a chest, and the second image sequence is a scanned image of a head, or the first image sequence is a segmented scanned image of a leg, and the second image sequence is a scanned image of a trunk.
S220: and determining a spliced sheet, wherein the spliced sheet is an image in the second image sequence, and the spliced sheet is used for splicing the first image sequence and the second image sequence.
Because the first image sequence and the second image sequence are only images for distinguishing different segmented bed scans, the images in the first image sequence can be used as a spliced sheet for splicing the first image sequence and the second image sequence.
In one embodiment, determining the splice sheet comprises: if the first image sequence and the second image sequence are not overlapped, selecting a second image which is closest to the first image sequence in the second image sequence as a spliced sheet; and if the first image sequence and the second image sequence are overlapped, selecting a second image which is closest to the first image sequence in the images which are not overlapped with the first image sequence in the second image sequence as a spliced sheet.
For correctly spliced medical images, the scanning range of the next bed is matched with that of the previous bed, namely the positions are in seamless butt joint. However, in the actual scanning process, there may be a case where two adjacent beds overlap or a case where there is a gap. In this case, by selecting an appropriate patch, some of the vacant images can be discarded, and the amount of calculation for the subsequent processing can be reduced.
Fig. 9c is a schematic diagram illustrating different overlapping situations of multi-bed images according to an exemplary embodiment of the present application, and fig. 9d is a schematic diagram illustrating a determined spliced sheet according to the different overlapping situations according to the exemplary embodiment of the present application.
As shown in fig. 9c and 9d, the present embodiment can determine the spliced sheets according to different overlapping situations: 1) when the first image sequence and the second image sequence are not overlapped and have no gap, selecting a second image which is closest to the first image sequence in the second image sequence as a spliced sheet; 2) when the first image sequence and the second image sequence are not overlapped and have a gap, selecting a second image closest to the first image sequence in the second image sequence as a spliced sheet; 3) when the first image sequence and the second image sequence are overlapped, selecting a second image which is closest to the first image sequence in the images which are not overlapped with the first image sequence in the second image sequence as a spliced sheet. The distance between the images can be calculated from the slice coordinates (slice location) of each image.
The embodiment selects the spliced sheets under the two conditions of no overlapping and overlapping, so that the spliced sheets are more comprehensively selected.
S230: and analyzing the continuity of at least one first image closest to the spliced sheet in the first image sequence and at least one second image closest to the spliced sheet in the second image sequence to obtain a continuity analysis result.
Fig. 9b is a schematic diagram of an image with continuity according to an exemplary embodiment of the present application. As can be seen from fig. 9b, the human body images when correctly stitched have continuity, and the difference between adjacent images is small, so that a continuity analysis result can be obtained by analyzing the continuity of the stitched sheet and the images adjacent to the stitched sheet.
In one embodiment, analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result includes: determining the distance difference of the spliced sheets according to the first distance from the spliced sheets to the first images adjacent to the spliced sheets in the first image sequence and the second distance from the spliced sheets to the second images adjacent to the spliced sheets in the second image sequence; and if the distance difference is larger than a first preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not achieved.
Specifically, let L be a first distance between the first images adjacent to the stitched sheet in the first image sequence from the stitched sheet, L be a second distance between the second images adjacent to the stitched sheet in the second image sequence from the stitched sheet, and T be a distance difference between the stitched sheets1The calculation formula of (a) is as follows:
distance difference T of spliced sheets1Evaluation is carried out in the ratio of L and L, the distance difference T1The size of (a) affects the continuity analysis of the neighboring images. When L is less than 2L, the distance difference T1Almost has no influence on the continuity analysis of adjacent images, and the distance difference T is considered to be negligible1Influence on the continuity analysis of adjacent images; when L is greater than 4L, the distance difference T1The influence on the continuity analysis of adjacent images is large. Therefore, the first preset threshold can be set to 4l if the distance difference T1If the first threshold is larger than the first preset threshold, the continuity between the first image sequence and the second image sequence is determined not to exist.
In the embodiment, the distance between the images is used as a factor for analyzing the continuity of the images, so that the continuity analysis result is more comprehensive and accurate.
In one embodiment, analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result includes: fitting the positions of the spliced sheets by adopting an interpolation method to obtain fitted spliced sheets; determining the difference of the first image of the spliced sheet and the fitted spliced sheet; and if the first image difference is larger than a second preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not available.
And the continuity analysis can also adopt an interpolation method to fit the positions of the spliced sheets to obtain the fitted spliced sheets, and if the first image sequence and the second image sequence have continuity, the difference between the first images of the fitted spliced sheets and the spliced sheets is not too large. Therefore, a second preset threshold may be preset, and when the first image difference is greater than the second preset threshold, it is determined that there is no continuity between the first image sequence and the second image sequence. The first image difference may be calculated according to the DICOM value of the stitched sheet, the DICOM value of at least one first image nearest to the stitched sheet in the first image sequence, and the DICOM value of at least one second image nearest to the stitched sheet in the second image sequence, for example, a possible interval of the DICOM value of each pixel of the stitched sheet is determined, and the first image difference is calculated according to a proportion of pixels of which the DICOM value of each pixel of the stitched sheet exceeds the possible interval. The DICOM value is a CT value in a CT image, and the DICOM value is a PET value in a PET image. The interpolation method may be a cubic algorithm, or may be a bilinear interpolation method or a nearest neighbor interpolation method.
In the embodiment, the difference between the spliced sheets and the fitted spliced sheets is used as a factor for analyzing the continuity of the images, so that the continuity analysis result is more comprehensive and accurate.
In an embodiment, fitting the positions of the spliced sheets by using an interpolation method to obtain the fitted spliced sheets includes: and fitting the n first images adjacent to the spliced sheet and the n second images adjacent to the spliced sheet according to a cubic algorithm to obtain a fitted spliced sheet, wherein n is an integer greater than 0.
The present embodiment will be described with n = 2. And setting the serial numbers of two first images adjacent to the spliced sheet as k-2 and k-1, the serial number of the spliced sheet as k, the serial numbers of two second images adjacent to the spliced sheet as k +1 and k +2, and the serial number of the fitted spliced sheet as k'.
Fitting the first two sheets and the second two sheets by a cubic algorithm on each pixel of the fitting splice sheet k', wherein the algorithm is
Wherein m and n are fitting value directions, if both are 0, fitting is performed in the forward direction, if one is not 0, fitting is performed in the oblique direction, and V isk'mn(i, j) to fit the fitted DICOM value of the (i, j) th pixel of the patch, the possible value will be evaluated by calculation through the area of the diagonal around it. Because m and n have 3 values respectively, V can be obtained in totalk'mn9 possible values of (i, j) according to 9Vk'mn(i, j) possible values establishing possible intervals of DICOM values, e.g. 9V's are chosenk'mnExtreme values of possible values (i, j) max [ V ]k'mn(i,j)]And min [ V ]k'mn(i,j)]And obtaining an extreme value interval. Calculated as a normal distribution according to the distribution probability, and can be calculated according to the DICOM value V of the (i, j) th pixel of the splice kkmn(i, j) the probability of the proportion falling within the possible interval yields a loss function, calculated according to the following formula:
carrying out weighted average on LOSS values of all pixel points of the spliced sheet k and the fitted spliced sheet k' to obtain a first image difference LOSS, namely:
the difference between the spliced sheets and the fitted spliced sheets can be accurately calculated by adopting the cubic algorithm.
In an embodiment, analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result, further includes: determining the distance difference of the spliced sheets according to the first distance from the spliced sheets to the first images adjacent to the spliced sheets in the first image sequence and the second distance from the spliced sheets to the second images adjacent to the spliced sheets in the second image sequence; weighting the distance difference and the first image difference to obtain a first continuity difference; if the first continuity difference is greater than a third predetermined threshold, it is determined that there is no continuity between the first image sequence and the second image sequence.
The continuity analysis result can also comprehensively consider the distance difference T1The distance and the first image difference LOSS are determined, for example, the distance difference T may be given according to different modalities of the video1The distance and the first image difference LOSS are different in weight to obtain a first continuity difference ERRORCOST, and the algorithm is as follows:
wherein alpha and beta are weight coefficients, and are different according to different modes of the image, and the distance difference T1For the calculation method of the first image difference LOSS, reference may be made to the above embodiments, and details are not repeated here. When the first continuity difference error is greater than a third predetermined threshold, it is determined that there is no continuity between the first image sequence and the second image sequence.
The embodiment comprehensively considers the weight of the distance difference and the first image difference, so that the continuity analysis result is more comprehensive and accurate.
In one embodiment, analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result includes: determining a second image difference between the spliced sheet and at least one first image adjacent to the spliced sheet in the first image sequence according to a cubic algorithm; determining a third image difference between the spliced sheet and at least one second image adjacent to the spliced sheet in the second image sequence according to a cubic algorithm; determining a second continuity difference according to the second image difference and the third image difference; and if the second continuity difference is larger than a preset fourth threshold, determining that the continuity between the first image sequence and the second image sequence is not achieved.
The present embodiment will be described with respect to two sheets adjacent to each other before and after the spliced sheet. If the numbers of two first images adjacent to the spliced sheet are k-2, k-1, the number of the spliced sheet is k, and the numbers of two second images adjacent to the spliced sheet are k +1, k +2, the following steps are performed:
calculating a second image difference V according to a cubic algorithmk'mn(i,j)betweenThe value of (c):
wherein, Vk'mn(i,j)betweenThe spliced sheet k is the second image difference between the two first images (i.e. k-2, k-1) adjacent to the spliced sheet in the first image sequence.
Calculating a third image difference V according to a cubic algorithmk'mn(i,j)inThe value of (c):
wherein, Vk'mn(i,j)inIs the third image difference between the spliced sheet k and two second images (i.e. k +1, k + 2) adjacent to the spliced sheet in the second image sequence.
If the first image sequence and the first image sequence are normally spliced, the second image difference Vk'mn(i,j)betweenAnd a third image difference Vk'mn(i,j)inThere should be no major difference. The second continuity difference DIFF is calculated by:
first according to the second image difference Vk'mn(i,j)betweenAnd a third image difference Vk'mn(i,j)inCalculating difference value:
then, the difference value of each pixel point is weighted to obtain a second continuity difference DIFF, namely:
a different fourth preset threshold may be set for the pictures of different modalities, and if the value of the second continuity difference DIFF is greater than the preset fourth threshold, it is determined that there is no continuity between the first picture sequence and the second picture sequence.
In the embodiment, the continuity difference between the adjacent images of the same bed and the continuity difference between the adjacent images of different beds are used as the factors for analyzing the continuity of the images, so that the continuity analysis result is more comprehensive and accurate.
S240: and determining whether the first image sequence and the second image sequence are abnormally spliced or not according to the continuity analysis result.
And if the first image sequence and the second image sequence are determined to have no continuity, determining that the first image sequence and the second image sequence are abnormally spliced, and sending out a splicing abnormality prompt.
According to the method for determining the medical image splicing abnormity, the continuity of the multi-bed splicing images is judged, so that the automatic reminding of the medical image splicing abnormity is realized, a doctor and an operator are helped to quickly confirm the correctness of the multi-bed image splicing, and the efficiency and the quality of image processing are improved.
Fig. 3 is a schematic flowchart of determining a spliced sheet according to an embodiment of the present application.
S310: and if the first image sequence is not overlapped with the second image sequence, selecting a second image which is closest to the first image sequence in the second image sequence as a spliced sheet.
S320: and if the first image sequence and the second image sequence are overlapped, selecting a second image which is closest to the first image sequence in the images which are not overlapped with the first image sequence in the second image sequence as a spliced sheet.
For specific contents of S310 to S320, reference may be made to the description in the above embodiments, and details are not repeated here to avoid repetition.
The embodiment selects the spliced sheets under the two conditions of no overlapping and overlapping, so that the spliced sheets are more comprehensively selected.
Fig. 4 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
S410: and determining the distance difference of the spliced sheets according to the first distance from the spliced sheets to the first images adjacent to the spliced sheets in the first image sequence and the second distance from the spliced sheets to the second images adjacent to the spliced sheets in the second image sequence.
S420: and if the distance difference is larger than a first preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not achieved.
For specific contents of S410 to S420, reference may be made to the description in the above embodiments, and details are not repeated here to avoid repetition.
In the embodiment, the distance between the images is used as a factor for analyzing the continuity of the images, so that the continuity analysis result is more comprehensive and accurate.
Fig. 5 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
S510: fitting the positions of the spliced sheets by adopting an interpolation method to obtain fitted spliced sheets;
s520: determining the difference of the first image of the spliced sheet and the fitted spliced sheet;
s530: and if the first image difference is larger than a second preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not available.
For specific contents of S510 to S530, reference may be made to the description in the above embodiments, and details are not repeated here to avoid repetition.
In the embodiment, the difference between the spliced sheets and the fitted spliced sheets is used as a factor for analyzing the continuity of the images, so that the continuity analysis result is more comprehensive and accurate.
Fig. 6 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
S610: and fitting the n first images adjacent to the spliced sheet and the n second images adjacent to the spliced sheet according to a cubic algorithm to obtain a fitted spliced sheet, wherein n is an integer greater than 0.
S620: determining the difference of the first image of the spliced sheet and the fitted spliced sheet;
s630: and if the first image difference is larger than a second preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not available.
For specific contents of S610 to S630, reference may be made to the description in the above embodiments, and details are not repeated here to avoid repetition.
The difference between the spliced sheets and the fitted spliced sheets can be accurately calculated by adopting the cubic algorithm.
Fig. 7 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
S710: determining the distance difference of the spliced sheets according to the first distance from the spliced sheets to the first images adjacent to the spliced sheets in the first image sequence and the second distance from the spliced sheets to the second images adjacent to the spliced sheets in the second image sequence;
s720: weighting the distance difference and the first image difference to obtain a first continuity difference;
s730: if the first continuity difference is greater than a third predetermined threshold, it is determined that there is no continuity between the first image sequence and the second image sequence.
For specific contents of S710 to S730, reference may be made to the description in the above embodiments, and details are not repeated here to avoid repetition.
The embodiment comprehensively considers the weight of the distance difference and the first image difference, so that the continuity analysis result is more comprehensive and accurate.
Fig. 8 is a schematic flow chart illustrating a continuity analysis result obtained by analyzing continuity according to another embodiment of the present application.
S810: determining a second image difference between the spliced sheet and at least one first image adjacent to the spliced sheet in the first image sequence according to a cubic algorithm;
s820: determining a third image difference between the spliced sheet and at least one second image adjacent to the spliced sheet in the second image sequence according to a cubic algorithm;
s830: determining a second continuity difference according to the second image difference and the third image difference;
s840: and if the second continuity difference is larger than a preset fourth threshold, determining that the continuity between the first image sequence and the second image sequence is not achieved.
For specific contents of S810 to S840, reference may be made to the description in the above embodiments, and details are not repeated here to avoid repetition.
In the embodiment, the continuity difference between the adjacent images of the same bed and the continuity difference between the adjacent images of different beds are used as the factors for analyzing the continuity of the images, so that the continuity analysis result is more comprehensive and accurate.
Fig. 10 is a schematic structural diagram of an apparatus for determining a medical image stitching abnormality according to an embodiment of the present application, including:
an obtaining module 1010, configured to obtain a first image sequence scanned at a first split bed and obtain a second image sequence scanned at a second split bed, where the first image sequence and the second image sequence are image sequences to be spliced;
a first determining module 1020, configured to determine a spliced sheet, where the spliced sheet is an image in the second image sequence and is used to splice the first image sequence and the second image sequence;
the analysis module 1030 is configured to analyze the continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet, so as to obtain a continuity analysis result;
the second determining module 1040 is configured to determine whether the first image sequence and the second image sequence are abnormally spliced according to the result of the continuity analysis.
The abnormal device of confirming medical image concatenation of this application has realized reminding the unusual automation of medical image concatenation through the continuity of judging many beds concatenation image, helps doctor and operating personnel to confirm the exactness of many beds image concatenation fast, improves the efficiency and the quality of image processing.
According to an embodiment of the present application, the first determining module 1020 includes: if the first image sequence and the second image sequence are not overlapped, selecting a second image which is closest to the first image sequence in the second image sequence as a spliced sheet; and if the first image sequence and the second image sequence are overlapped, selecting a second image which is closest to the first image sequence in the images which are not overlapped with the first image sequence in the second image sequence as a spliced sheet.
According to an embodiment of the application, the analysis module 1030 determines a distance difference between the spliced sheets according to a first distance between the spliced sheets and a first image adjacent to the spliced sheets in the first image sequence and a second distance between the spliced sheets and a second image adjacent to the spliced sheets in the second image sequence; and if the distance difference is larger than a first preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not achieved.
According to the embodiment of the application, the analysis module 1030 fits the positions of the spliced sheets by adopting an interpolation method to obtain the fitted spliced sheets; determining the difference of the first image of the spliced sheet and the fitted spliced sheet; and if the first image difference is larger than a second preset threshold value, determining that the continuity between the first image sequence and the second image sequence is not available.
According to the embodiment of the application, the analysis module 1030 adopts an interpolation method to fit the position of the spliced sheet to obtain a fitted spliced sheet, and the method comprises the following steps: and fitting the n first images adjacent to the spliced sheet and the n second images adjacent to the spliced sheet according to a cubic algorithm to obtain a fitted spliced sheet, wherein n is an integer greater than 0.
According to an embodiment of the application, the analysis module 1030 determines a distance difference between the spliced sheets according to a first distance between the spliced sheets and a first image adjacent to the spliced sheets in the first image sequence and a second distance between the spliced sheets and a second image adjacent to the spliced sheets in the second image sequence; weighting the distance difference and the first image difference to obtain a first continuity difference; if the first continuity difference is greater than a third predetermined threshold, it is determined that there is no continuity between the first image sequence and the second image sequence.
According to an embodiment of the application, the analysis module 1030 determines a second image difference between the spliced sheet and at least one first image adjacent to the spliced sheet in the first image sequence according to a cubic algorithm; determining a third image difference between the spliced sheet and at least one second image adjacent to the spliced sheet in the second image sequence according to a cubic algorithm; determining a second continuity difference according to the second image difference and the third image difference; and if the second continuity difference is larger than a preset fourth threshold, determining that the continuity between the first image sequence and the second image sequence is not achieved.
For specific limitations of the apparatus for determining a medical image stitching abnormality, reference may be made to the above limitations of the method for determining a medical image stitching abnormality, and details thereof are not repeated here.
Fig. 11 is a block diagram of an electronic device for performing a method for determining medical image stitching abnormalities according to an exemplary embodiment of the present application, which includes a processor 1110 and a memory 1120.
The memory 1120 is used to store the processor-executable instructions. The processor is used for executing the executable instructions to execute the method for determining the medical image stitching abnormity in any one of the above embodiments.
The present application further provides a computer-readable storage medium, which stores a computer program for executing the method for determining medical image stitching abnormality according to any one of the above embodiments.
According to the method and the device for determining the medical image splicing abnormity, the continuity of the multi-bed splicing images is judged, so that the medical image splicing abnormity can be automatically reminded, a doctor and an operator can be helped to quickly confirm the correctness of the multi-bed image splicing, and the image processing efficiency and quality are improved.
In the above embodiments, all or part of the implementation may be realized by software, hardware, firmware or any other combination. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server-side, data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Video Disk (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A method for determining medical image stitching abnormity is characterized by comprising the following steps:
acquiring a first image sequence scanned at a first subsection bed and a second image sequence scanned at a second subsection bed, wherein the first image sequence and the second image sequence are image sequences to be spliced;
determining a spliced sheet, wherein the spliced sheet is an image in the second image sequence and is used for splicing the first image sequence and the second image sequence;
analyzing the continuity of at least one first image closest to the spliced sheet in the first image sequence and at least one second image closest to the spliced sheet in the second image sequence to obtain a continuity analysis result;
determining whether the first image sequence and the second image sequence are abnormally spliced or not according to the continuity analysis result, wherein the determining of the spliced sheets comprises the following steps:
if the first image sequence and the second image sequence are not overlapped, selecting a second image which is closest to the first image sequence in the second image sequence as the spliced sheet;
and if the first image sequence and the second image sequence are overlapped, selecting a second image which is closest to the first image sequence from the images which are not overlapped with the first image sequence in the second image sequence as the spliced sheet.
2. The method according to claim 1, wherein the analyzing continuity of at least one first image in the first image sequence closest to the spliced sheet and at least one second image in the second image sequence closest to the spliced sheet to obtain a continuity analysis result comprises:
determining the distance difference of the spliced sheets according to the first distance between the spliced sheets and a first image adjacent to the spliced sheets in the first image sequence and the second distance between the spliced sheets and a second image adjacent to the spliced sheets in the second image sequence;
and if the distance difference is larger than a first preset threshold value, determining that the continuity does not exist between the first image sequence and the second image sequence.
3. The method according to claim 1, wherein the analyzing continuity of at least one first image in the first image sequence closest to the spliced sheet and at least one second image in the second image sequence closest to the spliced sheet to obtain a continuity analysis result comprises:
fitting the positions of the spliced sheets by adopting an interpolation method to obtain fitted spliced sheets;
determining a first image difference of the stitched sheet and the fitted stitched sheet;
and if the first image difference is larger than a second preset threshold value, determining that the continuity between the first image sequence and the second image sequence does not exist.
4. The method according to claim 3, wherein the fitting the position of the spliced sheet by interpolation to obtain a fitted spliced sheet comprises:
and fitting the n first images adjacent to the spliced sheet and the n second images adjacent to the spliced sheet according to a cubic algorithm to obtain a fitted spliced sheet, wherein n is an integer greater than 0.
5. The method of claim 3, wherein the analyzing continuity of at least one first image in the first image sequence closest to the stitched sheet and at least one second image in the second image sequence closest to the stitched sheet to obtain a continuity analysis result further comprises:
determining the distance difference of the spliced sheets according to the first distance between the spliced sheets and a first image adjacent to the spliced sheets in the first image sequence and the second distance between the spliced sheets and a second image adjacent to the spliced sheets in the second image sequence;
weighting the distance difference and the first image difference to obtain a first continuity difference;
and if the first continuity difference is larger than a third preset threshold value, determining that the continuity does not exist between the first image sequence and the second image sequence.
6. The method according to claim 1, wherein the analyzing continuity of at least one first image in the first image sequence closest to the spliced sheet and at least one second image in the second image sequence closest to the spliced sheet to obtain a continuity analysis result comprises:
determining a second image difference between the spliced sheet and at least one first image adjacent to the spliced sheet in the first image sequence according to a cubic algorithm;
determining a third image difference between the spliced sheet and at least one second image adjacent to the spliced sheet in the second image sequence according to a cubic algorithm;
determining a second continuity difference from the second image difference and the third image difference;
and if the second continuity difference is larger than a preset fourth threshold, determining that the continuity does not exist between the first image sequence and the second image sequence.
7. An apparatus for determining medical image stitching anomalies, comprising:
the device comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a first image sequence scanned at a first subsection bed and acquiring a second image sequence scanned at a second subsection bed, and the first image sequence and the second image sequence are image sequences to be spliced;
a first determining module, configured to determine a spliced sheet, where the spliced sheet is an image in the second image sequence and is used to splice the first image sequence and the second image sequence;
the analysis module is used for analyzing the continuity of at least one first image which is closest to the spliced sheet in the first image sequence and at least one second image which is closest to the spliced sheet in the second image sequence to obtain a continuity analysis result;
a second determining module, configured to determine whether the first image sequence and the second image sequence are abnormally spliced according to the continuity analysis result, where the first determining module includes:
if the first image sequence and the second image sequence are not overlapped, selecting a second image which is closest to the first image sequence in the second image sequence as the spliced sheet;
and if the first image sequence and the second image sequence are overlapped, selecting a second image which is closest to the first image sequence from the images which are not overlapped with the first image sequence in the second image sequence as the spliced sheet.
8. A computer-readable storage medium storing a computer program for executing the method for determining medical image stitching abnormality according to any one of claims 1 to 6.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions,
wherein the processor is used for executing the method for determining medical image stitching abnormity of any one of the claims 1 to 6.
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