WO2013131420A1 - 医学影像的目标区域边界确定装置和方法 - Google Patents
医学影像的目标区域边界确定装置和方法 Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
- G06T2207/10136—3D ultrasound image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06T2207/20101—Interactive definition of point of interest, landmark or seed
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30048—Heart; Cardiac
Definitions
- the present invention relates to a method and apparatus for determining a target region boundary of a medical image, and determining a physiological parameter using the determined boundary of the target region. More specifically, the present invention relates to the determination of cardiac physiology parameters based on real ultrasound image data. Background technique
- Medical imaging has become an integral part of modern medicine, and its application throughout clinical work is not only widely used for disease diagnosis, but also plays an important role in planning design, program implementation, and efficacy evaluation of surgery and radiation therapy.
- medical images can be divided into two parts: an anatomical image and a functional image.
- Anatomical images primarily describe human morphology information, including X-ray transmission imaging, CT, MRI, US, and others.
- the use of computer technology for quantitative analysis of medical images has become an important technical improvement direction, to increase the objectivity of diagnosis, and easier to grasp the operation, can reduce the experience of readers.
- Dependency exclude differences in judgment between different readers.
- the art desires to more accurately quantify the physiological parameters of the heart based on cardiac image photographic sequences, such as ventricular volume, myocardial mass, cardiac wall thickening, cardiac ejection fraction (EF value), and the like. Accurate acquisition of cardiac ejection fraction is of great significance. According to the cardiac ejection fraction, the ejection ability of the heart can be estimated, which is an important parameter for judging cardiac function.
- 3D ultrasound is a non-invasive imaging technique. It has the characteristics of fast imaging speed and low cost in the exploration of heart diseases. Therefore, it is the most widely used in the diagnosis and treatment of heart disease.
- Analysis of cardiac chamber volume, ejection fraction, myocardial volume and mass in 3D ultrasound images is an important basis for diagnosis.
- echocardiography contains a lot of noise, and the intimal and myocardial edges of the heart chamber are irregular (especially the diseased heart chamber and myocardium), it is difficult to calculate the relevant quantitative calculations.
- One of the difficulties is how to accurately obtain the boundaries of the endocardium and how to accurately measure the irregular changes in the heart.
- the field has long been committed to improving the accuracy and operability of ultrasound images to acquire physiological parameters.
- the current cardiac ejection fraction (EF value) method which is currently used clinically, defines some control points in an interactive manner and uses mathematical modeling to approximate a cardiac cavity using a series of simulated geometries. inaccurate.
- JP2002085404 entitled “ultrasonic imaging processor” » teaches to divide the heart chamber
- EP 123617 teaches the use of segmented curves to describe the heart chamber.
- JP2008073423 teaches interpolating a reference contour of more than 50 image sets to obtain an approximate cavity.
- EP1998671 (A1) teaches the use of the mouse to point out several control points and match a template to achieve automatic segmentation.
- EP 2030042 (A1) teaches a manual marking of a small number of control points, combined with a trained template to obtain an endocardium.
- the prior model is a model based on statistics, which means that the data set to be analyzed obeys an unknown probability distribution and has a definite relationship with the data set of a known sample. In order to find this unknown distribution, it is necessary to calculate the probability distribution of the observed sample data set. This previously calculated probability distribution or parameter is called the prior model.
- the diseased heart is generally no longer a heart chamber that can be estimated using the above model, compared to the normal heart chamber.
- the heart chamber of the diseased heart has an unpredictable shape change, and the endocardium is irregular (eg, tumor occupying ventricular aneurysm, thickened heart wall). Changes in the shape of the heart chamber lead to reduced ejection function, heart valve insufficiency and other symptoms.
- the prior shape model of the heart cavity is obtained by pre-calculating the multi-frame image, and the heart cavity on the current image is corrected by comparing with the approximate geometric model of the heart cavity on the current image.
- this type of prior model is calculated from normal cardiac calculations. In practical clinical applications, it is difficult to ensure accurate results for diseased hearts.
- Paragios N A level set approach for shape-driven segmentation and tracking of the left ventricle. Medical Imaging, 2003, Page(s): 773 776 uses the level set algorithm as the main body of the left ventricular segmentation algorithm, plus the use of a large number of Knowledge, that is, the correct left ventricular segmentation results. Use a priori experience combined with the characteristics of the image itself to develop a speed function for the level set and a restricted area. Thereby achieving the purpose of left ventricular segmentation.
- CN101404931A (Application No. CN200780009898.7), entitled “Quantitative Ultrasound Diagnosis by Myocardial Function", teaches manually setting control points first, and then connecting control points by curves according to image gradients, thereby achieving the purpose of approximate tracing.
- CN101454688A (Application No. C ⁇ 07 18854.0), entitled “Quantification and Display of Cardiac Wall Thickness”, discloses the distance, wall thickness variation and strain of the designated myocardial location point by speckle tracking. No single myocardium was obtained. This technique uses image gradients to determine the endocardial border, which is inaccurate if the image noise increases. Because the epicardium has no clear gradient, it is often missing and inaccurate when it is automatically determined.
- the patent provides a tool to manually adjust the two boundaries at the beginning and end of the cardiac cycle, and then automatically set the points to be tracked between the two boundaries, they are located on the myocardium, and then record the pixels around each point as The speckle pattern, the speckle pattern between different frames for maximum correlation block matching, so that the motion of each point can be tracked.
- speckle tracking is susceptible to noise
- the diseased myocardium Compared with normal myocardium, the diseased myocardium has lesions such as dilatation, contractility, and hypertrophy, which ultimately affects its ability to contract, specifically in the change of its elastic deformation parameters. In terms of geometry, it will change with normal myocardium, and thus may have irregular boundaries.
- the present invention is directed to an apparatus and method for more efficient and accurate image processing and calculation based on existing medical imaging techniques to improve and improve the volume and ejection fraction of the heart chamber.
- the accuracy of related physiological parameters such as myocardial volume and quality, thus helping to make correct and timely diagnosis during clinical treatment.
- a first aspect of the invention relates to a target region boundary determining device for a medical image And configured to distinguish a target region boundary according to a corresponding physical quantitative feature reflected by a tissue distribution in the medical image, the device comprising:
- the sub-area is set as follows: dividing the area to be analyzed into a plurality of adjacent sub-areas overlapping each other, and each adjacent sub-area The stacked or non-overlapping regions collectively and completely cover the target area.
- the device wherein the position of the selected target area is centered, the radius r is set, a circular area is defined, and the physical quantitative feature distribution in the circular area is analyzed to determine Threshold.
- the medical image is a medical image of the heart, selecting a position point of the heart chamber as a center, defining a circular area with a radius of 5 mm, and calculating a pixel gray in the circular area
- the average value of the degree values is taken as the threshold value; and, if the pixel gray level in a sub-area is smaller than the threshold value, the sub-area is marked as the target area, that is, the area of the heart chamber.
- the medical image is a medical image of the heart
- the area to be analyzed is divided into a series of overlapping circular regions, wherein the circular region is the sub-region,
- the radius of the circle is lmm
- the distance between the centers of the circles is also 1 mm
- the average value of the gray areas of each of the sub-areas is calculated. If the average value is greater than the threshold, the pixels in the sub-area are marked. It is a myocardial area, otherwise it is marked as a non-myocardial area.
- the device according to the eighth aspect of the present invention wherein the medical image is a cardiac image, and the target region is any one of the heart chambers or the myocardium.
- the gradation of the pixel or voxel is preferred.
- the average gray scale is a feature measurement method, and its measurement speed is faster.
- the gradient distribution of the region can also be examined, which is another simple and efficient feature measurement.
- the above object of the invention is to determine the boundary of a medical image target area by adopting a more accurate and effective method.
- the invention can be used to process more accurate quantitative physiological parameters when processing real 3D ultrasonic medical images.
- Real 3D ultrasound medical images are 3D images directly generated by 3D ultrasound probes. In ultrasound 3D images, boundary determination of endocardial borders, etc., is of great importance in determining cardiac-related physiological parameters.
- the inventors of the present invention have been inspired by tissue perfusion methods commonly employed in medical imaging.
- the contrast agent diffuses in the cavity and the gap of the human tissue, and when the digitized image is formed, the contrast between the anatomical tissue and the contrast agent data is generated, which makes the image operator easy. Identify the boundaries of various anatomical structures to provide a reliable basis for further quantitative diagnosis.
- the inventor proposed a virtual tissue perfusion model by means of computer technology.
- the sp using the physical quantitative characteristics reflected by the pixels contained in the image, helps the person skilled in the art to effectively identify the boundaries of the tissue to obtain a more accurate tissue boundary; further, to obtain the quantitative physiological parameters of the heart based on the accurate boundary.
- the present invention utilizes computer technology to extract interest tissue boundaries from digitized images. There is a clear contrast between the pixels or voxels around the boundaries of the tissue of interest, but the boundaries are unclear due to the effects of granular noise.
- the inventors have specifically examined the characteristics of the pixels in the image, and set a cell or sub-region in the region to be analyzed, which is filled with pixels inherent to the padding sub-region of the minimum basic unit, and therefore, is assumed to be a "pixel padding unit".
- a point investigation point is set on the area to be analyzed, and a circular or elliptical sub-area around the point is a unit or a pixel filling unit, and the sub-areas overlap each other, and the pixel value or the voxel value in the sub-area is analyzed.
- the distribution feature from which a fixed or non-fixed threshold is derived, and according to this threshold, each pixel or voxel in the area around each inspection point is marked to obtain the tissue region of interest, its boundary It is the boundary of the organization of interest.
- the design algorithm can be used to set the inspection point, and a plurality of circular or elliptical regions of different scales or sizes can be used to further analyze the distribution of pixel values or voxel values. Further refine the boundaries of the tissue area of interest.
- the present invention utilizes computer technology to directly utilize the regional characteristics of different related images by utilizing the tissue in the image, for example, the difference in physical characteristics between the heart chambers of the heart, and the differences in tissue characteristics reflected in the medical images.
- the operator selects the approximate middle position of the area according to experience, and uses computer technology to determine the physical characteristics of the area, for example, the average value of the gray scale, the gradient value, etc., and divides the area and the boundary into two categories by threshold comparison, BP, The effect of image binarization is achieved, thereby distinguishing the boundaries.
- This distinction is more objective and accurate, avoiding the limitations of the prior model segmentation of the heart chamber and myocardial methods.
- FIG. 1 is a schematic diagram of approximate heart cavity segmentation results on a typical conventional image processing device
- FIG. 2 is a schematic illustration of an embodiment of the present invention employing interactive selection of a target heart chamber
- Figure 3A illustrates the boundary of the heart chamber marked by the method of the present invention
- FIG. 3B is a schematic diagram of a heart cavity volume change curve of each frame of a time series, from which the maximum volume TM « and the minimum volume «TM of each frame image in each cardiac cycle can be seen ;
- Figure 4 is a flow chart of a particular embodiment of the invention. detailed description
- boundary processing proposed by the present invention for an organization of interest or region can have many different applications.
- the description of the specific embodiments is intended to assist those skilled in the art to understand the invention and should not be construed as limiting.
- the analysis is mainly made by taking the pixel gradation as a physical quantitative feature as an example.
- Other suitable physical quantitative features can also be applied to the present invention.
- the boundary processing of the present invention includes the following steps:
- the preliminary markup obtains one or more connected areas, and then integrates the area of interest (ROI), or the target area, for further processing, that is, only the connected area containing the operator's mouse click is retained, and other areas are Discard, or say, unmark other areas. This gives the result of the initial split.
- ROI area of interest
- the boundary refinement process ⁇ first marks the boundary of the segmented region separately, and then arranges the pixel padding cells on the boundary, and sets the pixel padding cells to cover smaller regions. , can be half the size of the pixel-filled cells in the first step, they still need to overlap each other. Also on these areas Quantitative features, such as average grayscale or gradient, are calculated, and a threshold is obtained. Each pixel filling unit is marked according to the threshold, and an OR operation is performed with the initial divided region result, and the refined regional result is obtained.
- further refinement can be performed, for example:
- the operator repeats step 3 according to clinical needs, and can further refine the boundaries by further reducing the size of the pixel fill unit until a satisfactory result is obtained.
- the so-called three-dimensional data is made up of the previous sliced images.
- the stacking of the boundaries of the previously obtained slice is represented as a surface in the 3D data.
- the voxel fill unit has the same settings as when you performed the third step last time, that is, with the same radius setting, they still need to overlap each other.
- quantitative features are calculated based on the pixel gray value on these regions, and a threshold is obtained, each pixel filling unit is marked according to the threshold value, and an OR operation is performed with the regional result obtained in the last third step, and the combined regional result is obtained.
- Regarding the processing of the heart chamber boundary it is basically the same as the above description, and further, it is necessary to add the following processing in step 2 thereof:
- step 2 On the area obtained in step 2, mark the boundary separately, and then divide the boundary into a series of overlapping circular areas, the center of the circle is the point on the boundary, and the radius is the radius of the circular area in the first step. Half of it. The average value of the pixel gray value on each circular area and the average value of the pixel gray gradient mode are calculated. Then calculate the average of these values to get two thresholds:
- n is the number of circular regions.
- the grayscale average reflects the mean of the grayscale mean;
- the mean value of the mode reflects the mean of the mean of the gradient mode, and the change of the number of pixels in the analysis area. It reflects the size of the pixel change in this area. As the boundary, the value will become larger, and less than this value indicates that it is still within the boundary.
- It is marked that the condition is that the gray level mean of a sub-area is less than the threshold of the gray mean, and the gradient mode mean is also smaller than the W value of the gradient mode mean.
- the pixels in the circular area are marked as heart chamber regions, otherwise marked as non-cardiac chamber regions.
- the heart chamber area marked in this step and the heart chamber area marked in the second step are ORed together to obtain a refined heart chamber area.
- the operator repeats step 3 according to clinical needs.
- the radius of the circular area used each time is half of the radius of the last used circular area, to further refine the boundary until the result on the satisfactory 2D slice is obtained. until.
- the 2D slice map is stacked into 3D data
- the fourth step is that the heart cavity regions on each 2D slice map are simultaneously stacked into 3D regions.
- the center of the sphere is the point on the boundary surface, and the radius is the last used circular area in the fourth step. radius.
- the average value of the voxel gray value on each spherical region and the average value of the voxel gray gradient mode are calculated.
- the average of the gray scale and the gradient mode average of the pixels are obtained by calculating the average of these values.
- n is the number of spherical regions.
- the grayscale average and the gradient mode average of each spherical region are examined, and the pixels in the spherical region are marked as the cardiac cavity region, otherwise marked as the non-cardiac cavity region.
- the heart chamber area marked in this step and the heart chamber area marked in the fourth step are ORed, and the refined 3D area of the heart chamber is obtained.
- the present invention is applied to real three-dimensional (3D) ultrasound image data processing for a patient's palpitations, which is used in this embodiment to acquire a cardiac chamber volume and an ejection fraction.
- Step 1 Obtain medical image data of the patient by using an ultrasound imaging device.
- the heart region is scanned using a real 3D ultrasound probe to obtain multiple time series of 3D ultrasound images.
- Each time series contains a series of frames, and one or more complete cardiac cycles are recorded, each frame containing There are 3D voxel data composed of multiple slices.
- the imaging devices used are, for example, the Siemens SC2000 echocardiograph and the Philips IE33.
- Step 2 Extract the contour of the heart cavity in all slice images of all frames in the real 3D ultrasound image time series.
- 5 8 time series are scanned, one time sequence has ⁇ 44 frames, one frame has 256 slice images, and each image has a size of 256*256 pixels.
- the slice image is selected based on the heart chamber containing the most interesting and exposed.
- the location of the mouse click can be clearly determined and is clearly within the heart chamber.
- the operator clicks on the slice map with the mouse, and the position of the click is required to be inside the heart cavity of interest.
- the width direction is the X axis
- the positive direction is the right direction
- the height direction is the y axis
- the positive direction is the downward direction
- the X, y coordinates thus obtained.
- the purpose of setting coordinates is to describe the position of each pixel or voxel in space, which is uniquely determined by the coordinates (x, y) or (x, y, z).
- the purpose of using coordinates is mainly used to determine the adjacency relationship between pixels or voxels (8 neighborhoods or 4 neighborhoods on 2D images, 6 neighborhoods and 26 neighborhoods on 3D images), for setting The determination of the extent of the filling unit, as well as the marking of the heart chamber of interest (the perfusion area covering the heart chamber of interest forms a connected abutment relationship between them after being labeled, so that a single heart chamber can be separated).
- an automatic association processing unit may be additionally provided.
- a slice When a slice is clicked, all slices of the 3D image of the frame are automatically associated, and only one slice is required for each frame, and other slices are automatically processed.
- the image range of an ultrasound contains the region of interest and noise (non-interest zone), the only region that is not ideal. Due to the limitations of the actual effect, the operator asks to confirm (click) the region of interest as the entire technology.
- the center of the heart chamber as the center, define a circular area with a radius of 5 mm, according to the voxel resolution of the 3D ultrasound image (ie the distance between the voxel center points in the three directions of x, y, z) , in mm, converted to the range of the circular area in pixels, and the average value of the pixel gray value in the circular area is calculated as a model parameter, that is, the threshold parameter t.
- each circular area can be regarded as a sub-area of pixel fill of an image.
- the distribution of pixel values in each circular region is analyzed, and the cardiac cavity is marked by the threshold segmentation method according to the threshold parameter t, that is, each circular region is marked as a cardiac cavity region and a non-cardiac cavity region, respectively.
- thresholds are divided for all pixels of the slice using the threshold calculated according to step b). Since the pixel gray value of the region where the heart chamber is located is low, it is necessary to mark the pixel smaller than the threshold in the slice map as the cardiac cavity region.
- the slice map is first divided into a series of overlapping circular regions as sub-regions or pixel-filled regions, the radius of the circle is 5 mm, and the distance between the centers of the circles is also 5 mm, according to step b) The method in the method is converted to a circular area range in pixels. Then, the average value of the gray scales of all the pixels in the area is calculated.
- the pixels in the circular area are marked as the cardiac cavity area, otherwise they are marked as non-cardiac cavity areas.
- the connected graph is checked in the 8-neighbor manner of the marker map, and the connected domain containing the position of the cardiac chamber marked by the operator is taken as the segmentation result of the cardiac chamber of interest. . Finally, the same threshold segmentation is performed for all slices on all frames of an image time series.
- Step 3 Calculate the volume of the heart chamber and the EF value based on the marked area of the heart chamber.
- each pixel On the marked heart cavity area, use the neighborhood check method to determine whether each pixel is an inner point or a boundary point. If it is a boundary point, it is marked as white, and other points are marked as black, thereby obtaining an irregular endocardial boundary. .
- N is the total number of pixels on the boundary
- miller is the maximum value of the grayscale gradient mode of the pixel on the boundary
- / min is the minimum value of the grayscale gradient mode of the pixel on the boundary
- Heart chamber volume Where S is the total number of slices on the image of the frame, « « « ⁇ ,. is the number of pixels in the endocardial border of each slice, ⁇ 2, is the number of pixels on the endocardial border of each slice, and Is the distance between the voxel center points in one of the x, y, and z directions of a frame of images, in mm.
- Steps 1 and 2 of Embodiment 4 are the same as Embodiment 1 described above, and thus will not be specifically described.
- steps a), b), c) in step 2 are repeated to identify other cardiac chamber regions on the slice map for use in the cardiac exclusion step in subsequent myocardial segmentation.
- Other areas of the heart chamber refer to similar segmentation operations on other incompletely exposed, unclear heart chambers, with the goal of marking all heart chambers so as not to affect the division of the heart muscle.
- This step is an additional pre-treatment step before myocardial segmentation, with the goal of eliminating all heart chambers.
- Step 3 Extract the myocardial contour from all slice images of all frames in the real 3D ultrasound image time series.
- a) Use the mouse to click on multiple myocardial locations of interest.
- the operator clicks on the slice map with the mouse, and the position of the click is required to be near the edge inside the myocardial (target myocardial) of interest.
- the X-coordinate and y-coordinate of the position point are recorded with the upper left corner of the image as the origin. There may be more than one myocardial location point of interest.
- the pixel at the point of the myocardial position clicked with the mouse does not reflect the distribution range of the pixel gray value in the myocardium, and the pixel average value in a neighborhood around the selected position point can be used to obtain a more accurate gray value.
- Estimated distribution Therefore, with the center of the myocardial position as the center, with a radius of 1 mm, define a circular area, according to the voxel resolution of the 3D ultrasound image (ie, the distance between the voxel center points in the three directions of x, y, z, In mm, converted to the range of the circular area in pixels, the average value of the pixel gray value in the circular area is calculated as a model parameter, that is, the threshold parameter t.
- the myocardium is marked by the method of threshold segmentation.
- This step is based on the threshold parameter t calculated in step b, threshold-dividing all the pixels of the slice, and excluding the pixels of all the regions where the heart chamber is obtained in step 2 and the additional step.
- the slice map is first divided into a series of overlapping circular regions, that is, pixel filling units (cells).
- the radius of the circle is lmm, and the distance between the centers of the circles is also lmm, which is converted into the area of the circular area in pixels according to the method in step b.
- the average value of the gray scales of all the pixels in the region is calculated. If the average value is larger than the threshold value t, the pixel points in the circular region are marked as myocardial regions, otherwise they are marked as non-myocardial regions.
- the connected map is examined in a manner of 8 neighborhoods, and the connected domain containing the myocardial position points marked by the operator is used as the segmentation result of the myocardial of interest. Finally, the same threshold segmentation is performed for all slices on all frames of an image time series.
- Step 4 Calculate myocardial volume and mass based on the marked myocardial area. a) The boundaries of the individual myocardium are obtained from each of the marked myocardial regions.
- the neighborhood check method is used to determine whether each pixel is an inner point or a boundary point. If it is a boundary point, it is marked as white, and other points are marked as black, thereby obtaining an irregular myocardial boundary.
- Each pixel on the myocardial boundary calculates a weight that affects the number of pixels on the border of the myocardium.
- ⁇ is the total number of pixels on the border of the myocardium
- ⁇ is the maximum value of the gray scale gradient mode of the pixel on the myocardial boundary
- / min is the minimum value of the gray scale gradient model of the pixel on the myocardial boundary
- / is the each on the myocardial boundary Pixel grayscale gradient mode.
- p is the average density of the myocardium obtained from clinical trials and is the volume of a certain myocardial of interest on the frame image.
- the above formula for calculating the volume takes into account the uncertainty of the boundary voxels on the precise description of the boundary. Therefore, these voxels are not directly used as a volume element to participate in the volume calculation, but are multiplied by a weighted value and then participate.
- the accumulation of volume reflects its ambiguity and can more accurately reflect the actual volume of the heart chamber or myocardium.
- the volume parameter in the formula for calculating EF is obtained using the method of the present invention.
- a formula for calculating myocardial mass wherein the volume parameter is obtained using the method of the present invention can be performed on 2D slices or on 3D voxel data, and can be extended to any high-dimensional data processing.
- the geometry of the filling unit is a circle, and the pixel intensity data in the circular area is examined.
- the geometry of the filling sub-area is a sphere, and the voxel intensity data in the sphere is examined.
- the processing on 2D is a preliminary process and is further refined/optimized on 3D.
- the division of the neighborhoods is overlapped by the principle of full coverage.
- the circular area around each set point is one of the elements of the invention. You can use different shapes flexibly; Pixels Filled areas (sub-areas) are the total collection of circular sub-areas around each set point.
- the boundary processing of the present invention can also be processed on three-dimensional data, with reference to the above-described two-dimensional processing embodiment. For example, you can change the geometry of the segmentation processing area from a circle to a sphere.
- the invention can also be applied to other types of image data processing, such as CT, MRI, PET, SPECT, etc., to segment and identify the anatomical tissue of interest therein, and to calculate relevant physiological parameters.
- the anatomical tissue of interest in the image has a certain degree of contrast with the surrounding tissue, and is irregular, and is suitable for segmentation by applying the present invention.
- the present invention is applicable to both normal tissue conditions and tissue lesions. It will be apparent to those skilled in the art that ⁇ RTIgt; ⁇ / RTI> ⁇ RTIgt; ⁇ / RTI> ⁇ RTIgt; ⁇ / RTI> ⁇ RTIgt; Accordingly, the present invention is intended to cover various modifications and alternatives
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CN102871686B (zh) | 2015-08-19 |
US20150023578A1 (en) | 2015-01-22 |
US20150023577A1 (en) | 2015-01-22 |
CN102920477B (zh) | 2015-05-20 |
WO2013131421A1 (zh) | 2013-09-12 |
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