CA2274363A1 - System and method for segmentation of two-dimensional magnetic resonance images - Google Patents
System and method for segmentation of two-dimensional magnetic resonance images Download PDFInfo
- Publication number
- CA2274363A1 CA2274363A1 CA002274363A CA2274363A CA2274363A1 CA 2274363 A1 CA2274363 A1 CA 2274363A1 CA 002274363 A CA002274363 A CA 002274363A CA 2274363 A CA2274363 A CA 2274363A CA 2274363 A1 CA2274363 A1 CA 2274363A1
- Authority
- CA
- Canada
- Prior art keywords
- substructure
- contour
- images
- segmentation
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
- G01R33/5608—Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels
-
- 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
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
Abstract
A computer system and a method to segment two dimensional Magnetic Resonance Images that include a substructure is disclosed herein. The method consists of first receiving a set of MRI.
The segmentation is then performed by the user on the first image of the set. Using this information, the system proceeds with the segmentation of the other images in the set, by first, estimating the center position of the substructure. The system then find the pixel of the substructure's contour for different angular positions about the center of the substructure. This is achieved by the system by predicting the contour position and using that predicted position to find the position of the pixel belonging to the contour that corresponds to the current angular position.
Having connected the pixels of the contour, the system fills the substructure.
The segmentation is then performed by the user on the first image of the set. Using this information, the system proceeds with the segmentation of the other images in the set, by first, estimating the center position of the substructure. The system then find the pixel of the substructure's contour for different angular positions about the center of the substructure. This is achieved by the system by predicting the contour position and using that predicted position to find the position of the pixel belonging to the contour that corresponds to the current angular position.
Having connected the pixels of the contour, the system fills the substructure.
Description
TITLE OF THE INVENTION
SYSTEM AND METHOD FOR SEGMENTATION OF
TWO-DIMENSIONAL MAGNETIC RESONANCE IMAGES
FIELD OF THE INVENTION
The present invention relates to magnetic resonance imaging. More specifically, the present invention is concerned with system and method for segmentation of two-dimensional magnetic resonance images .
BACKGROUND OF THE INVENTION
As it is generally known, Magnetic Resonance Imaging (MRI) is a non-ionizing process that can produce high contrast images of anatomical structures. In the case of MRI, such structures can include soft tissue. Using appropriate MR protocols, images of a selected structure can thus be obtained with very high accuracy. The resulting images can then be used to create a three-dimensional model of the structure of interest.
However, since many other structures such as, for example, synovial fluid, ligaments, and muscles are usually visible on the images, the detection of a selected structure needs appropriate contour selection. Conventional contour detection methods allow only the detection of the contour of the complete image without any discrimination between the different structures available on the image.
A method to accurately delineate tissues available on MR images is thus desirable.
OBJECTS OF THE INVENTION
An object of the present invention is therefore to provide an improved segmentation method for two-dimensional magnetic resonance images.
BRIEF DESCRIPTION OF THE DRAWINGS
In the appended drawings:
Figure 1 is a schematic bloc diagram of a segmentation system according to an embodiment of the present invention; and Figure 2 is a flow chart of a method for segmentation of magnetic resonance images according to an embodiment of the present invention.
SYSTEM AND METHOD FOR SEGMENTATION OF
TWO-DIMENSIONAL MAGNETIC RESONANCE IMAGES
FIELD OF THE INVENTION
The present invention relates to magnetic resonance imaging. More specifically, the present invention is concerned with system and method for segmentation of two-dimensional magnetic resonance images .
BACKGROUND OF THE INVENTION
As it is generally known, Magnetic Resonance Imaging (MRI) is a non-ionizing process that can produce high contrast images of anatomical structures. In the case of MRI, such structures can include soft tissue. Using appropriate MR protocols, images of a selected structure can thus be obtained with very high accuracy. The resulting images can then be used to create a three-dimensional model of the structure of interest.
However, since many other structures such as, for example, synovial fluid, ligaments, and muscles are usually visible on the images, the detection of a selected structure needs appropriate contour selection. Conventional contour detection methods allow only the detection of the contour of the complete image without any discrimination between the different structures available on the image.
A method to accurately delineate tissues available on MR images is thus desirable.
OBJECTS OF THE INVENTION
An object of the present invention is therefore to provide an improved segmentation method for two-dimensional magnetic resonance images.
BRIEF DESCRIPTION OF THE DRAWINGS
In the appended drawings:
Figure 1 is a schematic bloc diagram of a segmentation system according to an embodiment of the present invention; and Figure 2 is a flow chart of a method for segmentation of magnetic resonance images according to an embodiment of the present invention.
DESCRIPTION OF THE PREFERRED EMBODIMENT
Referring to Figure 1 of the appended drawings, a segmentation system 10, according to an embodiment of the present invention, will be described.
The segmentation system 10 includes a computer 12, a storing device 14, an output device in the form of a display monitor 16, and an input device 18. The storing device 14, the display monitor 16 and the input device 18 are all connected to the computer 12 via standard connection means, such as, for example, wires.
The computer 12 can be a conventional personal computer or any processing machine that includes a processor, a memory and input/output ports (not shown). The input/output ports may include network connectivity to transfer the images to and from the storing device 14.
The storing device 14 can be, for example, a hard drive, a cd-rom drive or other well known storing means. It can be directly connected to the computer 12 or remotely via a computer network, such as, for example the Internet. According to this embodiment of the invention, the storing device 14 is used to store both the non-segmented medical images as well as the resulting segmented images as computer files. Those files can be stored in any format and resolution that can be read by the computer 12.
Referring to Figure 1 of the appended drawings, a segmentation system 10, according to an embodiment of the present invention, will be described.
The segmentation system 10 includes a computer 12, a storing device 14, an output device in the form of a display monitor 16, and an input device 18. The storing device 14, the display monitor 16 and the input device 18 are all connected to the computer 12 via standard connection means, such as, for example, wires.
The computer 12 can be a conventional personal computer or any processing machine that includes a processor, a memory and input/output ports (not shown). The input/output ports may include network connectivity to transfer the images to and from the storing device 14.
The storing device 14 can be, for example, a hard drive, a cd-rom drive or other well known storing means. It can be directly connected to the computer 12 or remotely via a computer network, such as, for example the Internet. According to this embodiment of the invention, the storing device 14 is used to store both the non-segmented medical images as well as the resulting segmented images as computer files. Those files can be stored in any format and resolution that can be read by the computer 12.
The display monitor 16 is used to visualize the medical images both before and after the segmentation process. With the input device 18, the display monitor 16 also allows the input of guidance points by the user as will be described hereinbelow. The display monitor 16 is finally used to display a user interface, to facilitate the interaction between the user and the computer 12. It is believed within the reach of a person of ordinary skills in the art to provide another output device that allows for the visualization of the medical images.
The input device 18 can be a conventional mouse, a keyboard or any other well known input devices or combinations thereof.
Of course, the computer 12 runs a software that embodies the method of the present invention thereof.
Other aspects and characteristics of the system 10 will become more apparent upon reading of the following description of a segmentation method according to an embodiment of the present invention.
Referring now to Figure 2 of the appended drawings, generally stated, the segmentation method of the present invention consist in performing the following steps, in sequence:
100- Starting the system 10;
The input device 18 can be a conventional mouse, a keyboard or any other well known input devices or combinations thereof.
Of course, the computer 12 runs a software that embodies the method of the present invention thereof.
Other aspects and characteristics of the system 10 will become more apparent upon reading of the following description of a segmentation method according to an embodiment of the present invention.
Referring now to Figure 2 of the appended drawings, generally stated, the segmentation method of the present invention consist in performing the following steps, in sequence:
100- Starting the system 10;
102- Receiving a set of MRI images including a substructure;
for the first image of the set (stews 104 to 108 104- Selection by the user of the substructure contour;
106- Filling of the substructure contour on the first image of the set;
108- Cleaning of the first image;
for all the other images of the set~steps 110 to 120) 110- Estimating the center of the substructure of the previous image;
for all the angle positions s anning the substructure (steps 112 and 114) 112- Predicting the contour position;
114- Finding the pixel position belonging to the contour;
116- Connecting the pixels found;
118- Filling the substructure contour; and 120- Stopping the system 10.
These general steps will now be described in further details by way of an example where two substructures are to be segmented on MRI images: a bone and its cartilage.
Before describing these general steps in more details, it is to be noted that steps 104 to 118 must be performed for every substructure to be identified in the images. Hence, for this example, steps 104 to 118 are performed first to identify the bone, and then to identify the cartilage.
After the segmentation process 10 has been started (step 100), the step 102 consists in receiving, by the computer 12, a set of MRI images of a structure that includes the bone and the cartilage. It is to be noted that the two substructures are not perfectly defined in the images received. Also, in other application, the substructure can be any part of a structure that can be identified by different gray levels so that they can be extracted from the structure.
The images received are two dimensional arrays of pixels that has been previously produced by a MRI scanner. It is to be noted that the set of images is provided sequentially in the order that they appear in the three-dimensional object. In other words, successive images come from adjacent slices of the three dimensional object.
As it will become apparent upon reading the following description, the segmentation of the bone on an image is based on the shape of the bone detected on the previous image which consists of the a priory information. Hence, the system has to be initialized by the user.
The first image has to be segmented manually. The remaining of the images are segmented automatically.
In step 104, the contour of the bone is selected by the user. Once the contour of the bone is closed, the computer 12 then fill the selected contour (step 106) and clean everything on the image, except the bone (step 108).
After the segmentation of the bone has been performed by the user on the first image of the set, the computer 12 can proceed with the segmentation on all the other images of the set.
Knowing the position of the bone contour on the previous image, the computer 12 determines the center of the substructure in the current image (step 110). This is achieved by determining the position of each of the four sides of a rectangle that closely includes the bone contour on the previous image and then by determining the center position of that rectangle.
The computer 12 then predicts, in step 112, the position of the contour along a radius spanning form the center position in the current image. This is achieved for different angular positions around the center point determined in step 110. The predicted position is the corresponding position in the previous image, for the current angular position value.
Using the predicted position along the current radius as a starting point, the computer 12 finds, in step 114, the exact position of the contour along that radius.
The computer achieves this by verifying if the label (color) of the pixel at the current predicted position corresponds to the label of the pixel at the previous angle position of the current image. If so, the pixel is accepted as belonging to the contour, after verifying if the distance from the center is within a predetermined range. If not, the pixel position is stored as a possible candidate, and the verification is performed with the pixels at the right and at the left of the predicted pixel, each time storing the position as a possible candidate if the pixel does not belong to the contour.
If the computer 12 found that none of the three pixels belong to the contour, the computer 12 calculates parameters to verify if one of the possible candidates can be choosen as a good candidate for the studied angle. These parameters are based on the continuity between the studied pixel and the previous pixel found on the contour, the label of the current pixel and its position. More precisely, the computer 12 uses the parameters to find the best match among the candidates and verifies if this best match responds to predetermined criteria.
If, according to the parameters values, none of the possible candidates belongs to the contour, the computer 12 tags the current angular position and repeat steps 112 and 114 with the remaining angular positions.
When all the angular position that spans the substructure have been processed by the computer 12, the pixel positions corresponding to the previously tagged angular positions are interpolated between the pixel positions corresponding to the adjacent angular positions. A weight factor, corresponding to these pixel position, is dynamically adjusted depending on the position of the missing pixel.
In step 116, the computer so connects the pixels found as to provide a smooth closed contour.
Knowing the bone contour, the computer 12 fills the bone on the image.
The computer 12 repeat steps 110 to 118 for all the images.
To segment the cartilage on each of the images of the set, steps 104 to 118 are repeated.
Since the segmentation of the cartilage is very similar to the segmentation of the bone, only the differences between the two process will be described herein.
It is to be noted that, although the above described method can be used to segment most substructures, it has been found advantageous to adapt the method according to the substructure to be segmented.
With the segmentation of the cartilage, it is assumed that a segmentation of the bone has been performed before. Since the cartilage is close to the bone, the predicted pixel point of the contour of the cartilage for each angular position is the pixel on the contour of the bone for corresponding to the same angular position.
It has been found advantageous to pre-identified the substructures among a pre-determined group of substructures. This information helps the determination of the contour of the substructure of interest since the computer can use more appropriate criteria in step 114 to determined if the distance of a pixel is at a correct position from the center and to calculate the parameters allowing to verify if one of the possible candidates belongs to the contour.
Although the present invention has been described for the segmentation of bone and cartilage images, it can also be used to segment other structures.
It is finally to be noted that, although the present invention has been described hereinabove by way of preferred embodiments thereof, it can be modified, without departing from the spirit and nature of the subject invention as defined in the appended claims.
for the first image of the set (stews 104 to 108 104- Selection by the user of the substructure contour;
106- Filling of the substructure contour on the first image of the set;
108- Cleaning of the first image;
for all the other images of the set~steps 110 to 120) 110- Estimating the center of the substructure of the previous image;
for all the angle positions s anning the substructure (steps 112 and 114) 112- Predicting the contour position;
114- Finding the pixel position belonging to the contour;
116- Connecting the pixels found;
118- Filling the substructure contour; and 120- Stopping the system 10.
These general steps will now be described in further details by way of an example where two substructures are to be segmented on MRI images: a bone and its cartilage.
Before describing these general steps in more details, it is to be noted that steps 104 to 118 must be performed for every substructure to be identified in the images. Hence, for this example, steps 104 to 118 are performed first to identify the bone, and then to identify the cartilage.
After the segmentation process 10 has been started (step 100), the step 102 consists in receiving, by the computer 12, a set of MRI images of a structure that includes the bone and the cartilage. It is to be noted that the two substructures are not perfectly defined in the images received. Also, in other application, the substructure can be any part of a structure that can be identified by different gray levels so that they can be extracted from the structure.
The images received are two dimensional arrays of pixels that has been previously produced by a MRI scanner. It is to be noted that the set of images is provided sequentially in the order that they appear in the three-dimensional object. In other words, successive images come from adjacent slices of the three dimensional object.
As it will become apparent upon reading the following description, the segmentation of the bone on an image is based on the shape of the bone detected on the previous image which consists of the a priory information. Hence, the system has to be initialized by the user.
The first image has to be segmented manually. The remaining of the images are segmented automatically.
In step 104, the contour of the bone is selected by the user. Once the contour of the bone is closed, the computer 12 then fill the selected contour (step 106) and clean everything on the image, except the bone (step 108).
After the segmentation of the bone has been performed by the user on the first image of the set, the computer 12 can proceed with the segmentation on all the other images of the set.
Knowing the position of the bone contour on the previous image, the computer 12 determines the center of the substructure in the current image (step 110). This is achieved by determining the position of each of the four sides of a rectangle that closely includes the bone contour on the previous image and then by determining the center position of that rectangle.
The computer 12 then predicts, in step 112, the position of the contour along a radius spanning form the center position in the current image. This is achieved for different angular positions around the center point determined in step 110. The predicted position is the corresponding position in the previous image, for the current angular position value.
Using the predicted position along the current radius as a starting point, the computer 12 finds, in step 114, the exact position of the contour along that radius.
The computer achieves this by verifying if the label (color) of the pixel at the current predicted position corresponds to the label of the pixel at the previous angle position of the current image. If so, the pixel is accepted as belonging to the contour, after verifying if the distance from the center is within a predetermined range. If not, the pixel position is stored as a possible candidate, and the verification is performed with the pixels at the right and at the left of the predicted pixel, each time storing the position as a possible candidate if the pixel does not belong to the contour.
If the computer 12 found that none of the three pixels belong to the contour, the computer 12 calculates parameters to verify if one of the possible candidates can be choosen as a good candidate for the studied angle. These parameters are based on the continuity between the studied pixel and the previous pixel found on the contour, the label of the current pixel and its position. More precisely, the computer 12 uses the parameters to find the best match among the candidates and verifies if this best match responds to predetermined criteria.
If, according to the parameters values, none of the possible candidates belongs to the contour, the computer 12 tags the current angular position and repeat steps 112 and 114 with the remaining angular positions.
When all the angular position that spans the substructure have been processed by the computer 12, the pixel positions corresponding to the previously tagged angular positions are interpolated between the pixel positions corresponding to the adjacent angular positions. A weight factor, corresponding to these pixel position, is dynamically adjusted depending on the position of the missing pixel.
In step 116, the computer so connects the pixels found as to provide a smooth closed contour.
Knowing the bone contour, the computer 12 fills the bone on the image.
The computer 12 repeat steps 110 to 118 for all the images.
To segment the cartilage on each of the images of the set, steps 104 to 118 are repeated.
Since the segmentation of the cartilage is very similar to the segmentation of the bone, only the differences between the two process will be described herein.
It is to be noted that, although the above described method can be used to segment most substructures, it has been found advantageous to adapt the method according to the substructure to be segmented.
With the segmentation of the cartilage, it is assumed that a segmentation of the bone has been performed before. Since the cartilage is close to the bone, the predicted pixel point of the contour of the cartilage for each angular position is the pixel on the contour of the bone for corresponding to the same angular position.
It has been found advantageous to pre-identified the substructures among a pre-determined group of substructures. This information helps the determination of the contour of the substructure of interest since the computer can use more appropriate criteria in step 114 to determined if the distance of a pixel is at a correct position from the center and to calculate the parameters allowing to verify if one of the possible candidates belongs to the contour.
Although the present invention has been described for the segmentation of bone and cartilage images, it can also be used to segment other structures.
It is finally to be noted that, although the present invention has been described hereinabove by way of preferred embodiments thereof, it can be modified, without departing from the spirit and nature of the subject invention as defined in the appended claims.
Claims
1. A method in a computer system to segment a set of ordered two-dimensional magnetic resonance image that includes a substructure, said method comprising:
receiving the set of magnetic resonance images;
manually selecting the substructure contour on the first images of the set;
filling the substructure on the first image of the set of images;
cleaning the first image of the set;
for each of the other images of the set, a) estimating the center of the substructure on the previous image;
b) for predetermined angular positions about the estimated center position, b1) predicting the contour position of the substructure along a radius corresponding to said angular position;
b2) using said center of the substructure on the previous image and said predicted contour position to find the pixel corresponding to the contour of the substructure along said radius;
c) connecting said pixels; and d) filling said substructure.
receiving the set of magnetic resonance images;
manually selecting the substructure contour on the first images of the set;
filling the substructure on the first image of the set of images;
cleaning the first image of the set;
for each of the other images of the set, a) estimating the center of the substructure on the previous image;
b) for predetermined angular positions about the estimated center position, b1) predicting the contour position of the substructure along a radius corresponding to said angular position;
b2) using said center of the substructure on the previous image and said predicted contour position to find the pixel corresponding to the contour of the substructure along said radius;
c) connecting said pixels; and d) filling said substructure.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA002274363A CA2274363A1 (en) | 1999-06-14 | 1999-06-14 | System and method for segmentation of two-dimensional magnetic resonance images |
| CA002311595A CA2311595A1 (en) | 1999-06-14 | 2000-06-14 | System and method for the segmentation of two-dimensional magnetic resonance images |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA002274363A CA2274363A1 (en) | 1999-06-14 | 1999-06-14 | System and method for segmentation of two-dimensional magnetic resonance images |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2274363A1 true CA2274363A1 (en) | 2000-12-14 |
Family
ID=30774320
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002274363A Abandoned CA2274363A1 (en) | 1999-06-14 | 1999-06-14 | System and method for segmentation of two-dimensional magnetic resonance images |
Country Status (1)
| Country | Link |
|---|---|
| CA (1) | CA2274363A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015130231A1 (en) * | 2014-02-27 | 2015-09-03 | Agency For Science, Technology And Research | Segmentation of cardiac magnetic resonance (cmr) images using a memory persistence approach |
| US9192459B2 (en) | 2000-01-14 | 2015-11-24 | Bonutti Skeletal Innovations Llc | Method of performing total knee arthroplasty |
-
1999
- 1999-06-14 CA CA002274363A patent/CA2274363A1/en not_active Abandoned
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9192459B2 (en) | 2000-01-14 | 2015-11-24 | Bonutti Skeletal Innovations Llc | Method of performing total knee arthroplasty |
| WO2015130231A1 (en) * | 2014-02-27 | 2015-09-03 | Agency For Science, Technology And Research | Segmentation of cardiac magnetic resonance (cmr) images using a memory persistence approach |
| US10235750B2 (en) | 2014-02-27 | 2019-03-19 | Agency For Science, Technology And Research | Segmentation of cardiac magnetic resonance (CMR) images using a memory persistence approach |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106056537B (en) | A kind of medical image joining method and device | |
| US6396939B1 (en) | Method and system for segmentation of medical images | |
| CN1765330B (en) | Ultrasonic blood vessel measurement apparatus and method | |
| US8509502B2 (en) | Spine labeling | |
| US5568811A (en) | Method for motion encoding of tissue structures in ultrasonic imaging | |
| US8423124B2 (en) | Method and system for spine visualization in 3D medical images | |
| US7822246B2 (en) | Method, a system and a computer program for integration of medical diagnostic information and a geometric model of a movable body | |
| US6608916B1 (en) | Automatic detection of spine axis and spine boundary in digital radiography | |
| US7155042B1 (en) | Method and system of measuring characteristics of an organ | |
| US6608917B1 (en) | Detection of vertebra endplates in digital radiography | |
| JP2004508856A (en) | Image registration system and method using likelihood maximization | |
| JP5399225B2 (en) | Image processing apparatus and method, and program | |
| JP2002306483A (en) | Medical diagnosis image processing equipment and method thereof | |
| KR20120086084A (en) | Apparatus and method for creating optimal 2-dimensional medical image automatically from 3-dimensional medical image | |
| JP2004105737A (en) | Integrated image recording method for heart magnetism resonance perfusion data | |
| CN106991697A (en) | The ambition ratio measuring method of chest digitized video | |
| US20060078184A1 (en) | Intelligent splitting of volume data | |
| WO2005009242A1 (en) | Medical image processing device and method | |
| CA2274363A1 (en) | System and method for segmentation of two-dimensional magnetic resonance images | |
| Noble et al. | Myocardial delineation via registration in a polar coordinate system1 | |
| US8165375B2 (en) | Method and system for registering CT data sets | |
| US6724924B1 (en) | Brightness and contrast invariant detection of vertebra pedicles | |
| Zhang et al. | Performance analysis of active shape reconstruction of fractured, incomplete skulls | |
| Neubauer et al. | Analysis of four-dimensional cardiac data sets using skeleton-based segmentation | |
| US11449995B2 (en) | Method of providing diagnosis assistance information and method of performing the same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FZDE | Dead |