CN114219792B - Method and system for processing images before craniocerebral puncture - Google Patents
Method and system for processing images before craniocerebral puncture Download PDFInfo
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- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
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Abstract
The invention discloses a method and a system for processing images before craniocerebral puncture, wherein the method comprises the following steps: s101, acquiring a first image of a patient needing to receive a craniocerebral puncture operation; step S103, selecting a region of interest in the first image; step S105, arbitrarily selecting a pixel p in the region of interest; step S107, creating a square area with p as the center, and searching a minimum direct correlation block in the square area; step S109, initializing the first image based on the minimum direct correlation block to obtain a second image; and S111, reconstructing the first image based on the second image. According to the method, the interesting area is selected in the first image, so that the interference of an irrelevant area is reduced, a pixel is randomly selected in the interesting area, the maximum relevant area is searched, the first image is initialized and reconstructed, the processing time of image data is reduced, and the real-time performance of acquiring the brain image is improved.
Description
Technical Field
The invention belongs to the technical field of medical image processing, and particularly relates to a method and a system for processing images before craniocerebral puncture.
Background
Medical image segmentation is a core technology in the medical image processing and clinical fields, and has important significance for lesion region extraction, specific tissue measurement, clinical diagnosis, pathological analysis, operation planning and the like. The mainstream method for segmenting medical images at present mainly comprises: an image segmentation method based on edge detection, an image segmentation method based on regions, an image segmentation method based on clustering, an image segmentation method based on moving contours, an image segmentation method based on graph theory, and the like. Due to the influence of the imaging mechanism, the external environment and the complex structure of the internal tissue of the human body, the phenomena of noise, blurring, uneven gray scale and the like often occur in the nuclear magnetic resonance image, and the segmentation of the nuclear magnetic resonance image is difficult.
In addition, with the intensive research on 4D-CT, 4D-CT can better solve the respiratory movement problem and reflect the movement law of organs, such as craniocerebral puncture. However, 4D-CT is limited to a single average respiratory cycle, often contains imaging artifacts, and is high in imaging dose; furthermore, due to the low soft tissue resolution of CT, no effective information can be provided for soft tissue motion. Compared with other imaging devices, the nuclear magnetic resonance imaging device has multiple advantages. The three-dimensional stereo dynamic image can be formed, and the contrast of soft tissues is very clear. Furthermore, magnetic resonance is not only morphological but also functional, and can form molecular images.
The craniocerebral puncture operation is a type of minimally invasive cranial neurosurgery operation which is a specific operation performed by extending a puncture instrument to an intracranial target position from a micropore drilled at a needle insertion position on the surface of a skull according to a puncture path planned before an operation. Compared with the traditional craniotomy, the craniocerebral puncture operation has the obvious advantages of small wound, low infection probability, quick postoperative recovery and the like.
The suitable scenes of the craniocerebral puncture operation include: 1) needle biopsy procedures performed to extract samples of intracranial diseased tissue can be used to diagnose the malignancy or malignancy of a tumor; 2) hematoma puncture drainage operation performed on patients with hemorrhagic stroke is commonly used for treating acute crisis; 3) the heat energy emitted by the electrode causes the skull tumor cell coagulation necrosis, and the like.
In the preoperative planning stage, a doctor needs to determine the starting position, direction and depth of puncture from two-dimensional fault section information presented by image scanning data of a patient, the requirements on clinical experience and capability of the doctor are high, and a computer-assisted surgery planning and navigation system carries out three-dimensional model reconstruction and visualization on a patient affected part, so that preoperative planning can be assisted in a three-dimensional orientation mode, virtual interaction of a model scene and real-time information feedback of the position of a puncture instrument are provided in the operation process, and the computer-assisted surgery planning and navigation system has great clinical application value.
In addition, the current computer-assisted craniocerebral puncture operation system mainly takes image information presented by a two-dimensional fault section as a main part, and combines a part of three-dimensional models, such as a skull structure of a patient, to jointly assist in planning a puncture path, so that the real-time performance of images is poor. Therefore, how to effectively improve the real-time performance of the craniocerebral image becomes one of the important topics.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for processing images before craniocerebral puncture, which comprises the following steps:
s101, acquiring a first image of a patient needing to receive a craniocerebral puncture operation;
step S103, selecting a region of interest in the first image;
step S105, arbitrarily selecting a pixel p in the region of interest;
step S107, creating a square area with p as the center, and searching a minimum direct correlation block in the square area;
step S109, initializing the first image based on the minimum direct correlation block to obtain a second image;
and S111, reconstructing the first image based on the second image.
Wherein, the step S107 specifically includes:
step S1071, centering on p, creating size 2 n ×2 n Wherein n is an integer, and n ═ n>3；
Step S1073, in the said nth square area, look for the minimum direct correlation block;
step S1075, if the minimum direct association block is found, executing step S109;
in step S1077, if the minimum direct correlation block is not found, the process proceeds to step S1071 so that n becomes n + 1.
Assuming that a 3 × 3 region a (p) is formed by any pixel p and its directly adjacent pixels in the image I, the adjacent pixel n (B) of any region B in the image I can be represented by the following formula (1): n (B) { y ∈ (I-B) | a (y) # B ≠ Φ };
the minimum direct association block satisfies the following condition:
condition 1:
I(x)＝I(y)＝c；
condition 2:
I(z)<c, where x, y, z are pixels in image I, c is a constant.
Wherein the step S109 includes:
setting the pixel in the minimum direct correlation block as a reference pixel, and keeping the value unchanged;
setting the pixels excluding the minimum directly related block in the first image as variable pixels, and assigning zero to the values;
a second image is obtained.
Wherein the step S111 includes:
summing all pixels which are not equal to 0 in the second image to obtain a first maximum pixel reference value F of the pixels in the second image;
creating F queues which are respectively represented as a 1 … a F, and storing the reference pixels in the second image into the queues corresponding to the values of the reference pixels;
sequentially judging whether variable pixels exist in direct adjacent pixels of each pixel in the queue a [ F ] … a [1] in the first image;
forming new F queues based on the judgment result;
reconstructing the first image based on the pixels in the new F queues.
Wherein, the sequentially judging whether the direct adjacent pixel of each pixel in the queue a [ F ] … a [1] in the first image has a variable pixel specifically includes:
for the queue aj, j is more than or equal to 1 and less than or equal to F, when the queue aj is not empty, sequentially judging the direct adjacent pixels of each pixel in the queue aj in the first image;
if the direct adjacent pixel is a variable pixel, the pixel is taken as the smaller of the value of the pixel or the value of j;
and storing the pixel into a queue corresponding to the value of the pixel.
Wherein the queue builds one column or one row of a matrix from all pixels in the first image.
Wherein the reconstruction of the first image is performed using morphological reconstruction.
Wherein the reconstruction follows the following rules:
for any pixel p in the image I, the value of a certain pixel p ' in its immediate neighborhood is I [ p ' ] ═ max (I (p), I (p ')).
After step S111, the method further includes: and comparing the reconstructed image with the first image to judge whether the region of interest is abnormal or not.
The invention also provides a craniocerebral puncture pre-image processing system, which comprises:
the first acquisition module is used for acquiring a first image of a patient needing to receive a craniocerebral puncture operation;
a first selection module for selecting a region of interest in the first image;
a second selection module for arbitrarily selecting a pixel p in the region of interest;
the device comprises a first searching module, a second searching module and a third searching module, wherein the first searching module is used for creating a square area taking p as a center and searching the minimum direct correlation block in the square area;
the first initialization module is used for initializing the first image based on the minimum direct correlation block to obtain a second image;
and the first reconstruction module is used for reconstructing the first image based on the second image.
According to the method and the system for processing the images before the craniocerebral puncture, the interested area is selected from the first image of the craniocerebral puncture operation patient, the interference of irrelevant images is reduced, one pixel is arbitrarily selected from the interested area, the smallest direct relevant block is searched by taking the pixel as the center, the first image is initialized and reconstructed, the processing time of image data is reduced, and the real-time property of obtaining the craniocerebral image is improved.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present disclosure will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to like or corresponding parts and in which:
FIG. 1 is a flow chart illustrating a method for pre-craniocerebral puncture image processing according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a pre-craniocerebral puncture image processing system according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and "a plurality" typically includes at least two.
Alternative embodiments of the present invention are described in detail below with reference to the accompanying drawings.
As shown in FIG. 1, the invention discloses a method for processing images before craniocerebral puncture, which comprises the following steps:
s101, acquiring a first image of a patient needing to receive a craniocerebral puncture operation;
step S103, selecting a region of interest in the first image;
step S105, arbitrarily selecting a pixel p in the region of interest;
step S107, creating a square area with p as the center, and searching a minimum direct correlation block in the square area;
step S109, initializing the first image based on the minimum direct correlation block to obtain a second image;
and S111, reconstructing the first image based on the second image.
In one embodiment, the invention provides a method for processing images before craniocerebral puncture, which comprises the following steps:
s101, acquiring a first image of a patient needing to receive a craniocerebral puncture operation;
step S103, selecting a region of interest in the first image;
step S105, arbitrarily selecting a pixel p in the region of interest;
step S107, creating a square area with p as the center, and searching a minimum direct correlation block in the square area;
step S109, initializing the first image based on the minimum direct correlation block to obtain a second image;
and S111, reconstructing the first image based on the second image.
Wherein, the step S107 specifically includes:
step S1071, centering on p, creating size 2 n ×2 n Wherein n is an integer, and n ═ n>3；
Step S1073, in the said nth square area, look for the minimum direct correlation block;
step S1075, if the minimum direct association block is found, executing step S109;
in step S1077, if the minimum direct correlation block is not found, the process proceeds to step S1071 so that n is n + 1.
Assuming that a 3 × 3 region a (p) is formed by any pixel p and its directly adjacent pixels in the image I, the adjacent pixel n (B) of any region B in the image I can be represented by the following formula (1): n (B) { y ∈ (I-B) | a (y) # B ≠ Φ };
the minimum direct association block satisfies the following condition:
condition 1:
I(x)＝I(y)＝c；
condition 2:
I(z)<c, where x, y, z are pixels in image I, c is a constant.
Wherein the step S109 includes:
setting the pixel in the minimum direct correlation block as a reference pixel, and keeping the value unchanged;
setting the pixels excluding the minimum directly related block in the first image as variable pixels, and assigning zero to the values;
a second image is obtained.
Wherein the step S111 includes:
summing all pixels which are not equal to 0 in the second image to obtain a first maximum pixel reference value F of the pixels in the second image;
creating queues with the quantity of F, respectively representing as a [1] … a [ F ], and storing the reference pixels in the second image into the queues corresponding to the values of the reference pixels;
sequentially judging whether variable pixels exist in direct adjacent pixels of each pixel in the queue a [ F ] … a [1] in the first image;
forming new F queues based on the judgment result;
reconstructing the first image based on the pixels in the new F queues.
Where a [ j ] represents the j-th column of pixels in the second image.
Wherein, the sequentially judging whether there is a variable pixel in the direct neighboring pixel of each pixel in the queue a [ F ] … a [1] in the first image specifically includes:
for the queue aj, j is more than or equal to 1 and less than or equal to F, when the queue aj is not empty, sequentially judging the direct adjacent pixels of each pixel in the queue aj in the first image;
if the direct adjacent pixel is a variable pixel, the pixel is taken as the smaller of the value of the pixel or the value of j;
and storing the pixel into a queue corresponding to the value of the pixel.
Wherein the queue builds one column or one row of a matrix from all pixels in the first image.
Wherein the reconstruction of the first image is performed using morphological reconstruction.
Wherein the reconstruction follows the following rules:
for any pixel p in the image I, the value of a certain pixel p ' in its immediate neighborhood is I [ p ' ] ═ max (I (p), I (p ')).
After step S111, the method further includes: and comparing the reconstructed image with the first image to judge whether the region of interest is abnormal or not.
According to the image processing method before craniocerebral puncture, the interested area is selected from the first image of the craniocerebral puncture operation patient, the interference of irrelevant images is reduced, one pixel is arbitrarily selected from the interested area, the smallest direct correlation block is searched by taking the pixel as the center, the first image is initialized and reconstructed, the processing time of image data is reduced, and the real-time property of the obtained craniocerebral image is improved.
As shown in fig. 2, the present invention further provides a craniocerebral puncture image processing system, which includes:
a first obtaining module 201, configured to obtain a first image of a patient needing to undergo a craniocerebral puncture operation;
a first selection module 203 for selecting a region of interest in the first image;
a second selection module 205 for arbitrarily selecting a pixel p in the region of interest;
a first finding module 207, configured to create a square area with p as a center, and find a minimum direct association block in the square area;
a first initialization module 209, configured to initialize the first image based on the minimum direct association block to obtain a second image;
a first reconstruction module 211, configured to reconstruct the first image based on the second image.
According to the image processing system before craniocerebral puncture, the interested area is selected from the first image of the patient subjected to craniocerebral puncture operation, the interference of irrelevant images is reduced, one pixel is arbitrarily selected from the interested area, the smallest direct correlation block is searched by taking the pixel as the center, the first image is initialized and reconstructed, the processing time of image data is reduced, and the real-time property of the obtained craniocerebral image is improved.
In one embodiment, the present invention provides a non-volatile computer storage medium having stored thereon computer-executable instructions that may perform the method steps as described in the above embodiments.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The foregoing describes preferred embodiments of the present invention, and is intended to provide a clear and concise description of the spirit and scope of the invention, and not to limit the same, but to include all modifications, substitutions, and alterations falling within the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A craniocerebral puncture pre-image processing method is characterized by comprising the following steps:
s101, acquiring a first image of a patient needing to receive a craniocerebral puncture operation;
step S103, selecting a region of interest in the first image;
step S105, arbitrarily selecting a pixel p in the region of interest;
step S107, creating a square area with p as the center, and searching a minimum direct correlation block in the square area;
step S109, initializing the first image based on the minimum direct correlation block to obtain a second image;
step S111, reconstructing the first image based on the second image;
the step S107 specifically includes:
step S1071, creating an nth square region having a size of 2n × 2n with p as a center, where n is an integer and n = > 3;
step S1073, in the said nth square area, look for the minimum direct correlation block;
step S1075, if the minimum direct association block is found, executing step S109;
step S1077, if the minimum direct correlation block is not found, go to step S1071 so that n = n + 1;
assuming that a 3 × 3 region a (p) is formed by any pixel p and its directly adjacent pixels in an image I, the adjacent pixels n (B) of any region B in the image I can be represented by the following formula (1): n (B) = { y ∈ (I-B) | a (y) Ç B ≠ Φ };
the minimum direct association block satisfies the following condition:
condition 1: ∀ x, y ∈ a, i (x) = i (y) = c;
condition 2: ∀ z ∈ N (B), I (z) < c, where x, y, z are pixels in image I, and c is a constant.
2. The method for processing images before craniocerebral puncture as set forth in claim 1, wherein the step S109 comprises:
setting the pixel in the minimum direct correlation block as a reference pixel, and keeping the value unchanged;
setting the pixels excluding the minimum directly related block in the first image as variable pixels, and assigning zero to the values;
a second image is obtained.
3. The method of claim 2, wherein the step S111 comprises:
summing all pixels which are not equal to 0 in the second image to obtain a first maximum pixel reference value F of the pixels in the second image;
creating queues with the quantity of F, respectively representing as a < 1 > … a < F >, and storing each reference pixel in the second image into the queue corresponding to the value of the reference pixel;
sequentially judging whether variable pixels exist in direct adjacent pixels of each pixel in the queue a [ F ] … a [1] in the first image;
forming new F queues based on the judgment result;
reconstructing the first image based on the pixels in the new F queues.
4. The method according to claim 3, wherein the sequentially determining whether there is a variable pixel in the first image of each pixel in the queue a [ F ] … a [1], specifically comprises:
for the queue aj, j is more than or equal to 1 and less than or equal to F, when the queue aj is not empty, sequentially judging the direct adjacent pixels of each pixel in the queue aj in the first image;
if the direct adjacent pixel is a variable pixel, the pixel is taken as the smaller of the value of the pixel or the value of j;
and storing the pixel into a queue corresponding to the value of the pixel.
5. The method of claim 3, wherein the queue is one row or one column of a matrix constructed by all pixels in the first image.
6. The method of claim 1, wherein the reconstruction of the first image is performed using morphological reconstruction.
7. The method of claim 1, wherein the reconstruction follows the following rules:
for any pixel p in the image I, the value of a certain pixel p ' in its immediate vicinity is I [ p ' ] = max (I (p), I (p ')).
8. A craniocerebral pre-puncture image processing system, comprising:
the first acquisition module is used for acquiring a first image of a patient needing to receive a craniocerebral puncture operation;
a first selection module for selecting a region of interest in the first image;
a second selection module for arbitrarily selecting a pixel p in the region of interest;
the device comprises a first searching module, a second searching module and a third searching module, wherein the first searching module is used for creating a square area with p as a center and searching a minimum direct correlation block in the square area; the method comprises the following steps:
step S1071, creating an nth square region having a size of 2n × 2n with p as a center, where n is an integer and n = > 3;
step S1073, in the said nth square area, look for the minimum direct correlation block;
step S1075, if the minimum direct association block is found, executing step S109;
step S1077, if the minimum direct correlation block is not found, go to step S1071 so that n = n + 1;
assuming that a 3 × 3 region a (p) is formed by any pixel p and its directly adjacent pixels in an image I, the adjacent pixels n (B) of any region B in the image I can be represented by the following formula (1): n (B) = { y ∈ (I-B) | a (y) Ç B ≠ Φ };
the minimum direct association block satisfies the following condition:
condition 1: ∀ x, y ∈ a, i (x) = i (y) = c;
condition 2: ∀ z ∈ N (B), I (z) < c, where x, y, z are pixels in image I, and c is a constant;
the first initialization module is used for initializing the first image based on the minimum direct correlation block to obtain a second image;
and the first reconstruction module is used for reconstructing the first image based on the second image.
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| CN110675464A (en) * | 2019-09-20 | 2020-01-10 | 和宇健康科技股份有限公司 | Medical image processing method and device, server and storage medium |
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