model based radiographic image classification this method implements an object oriented knowledge...
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Model Based Radiographic Image Classification
• This method implements an object oriented knowledge representation to automatically determines the body part class of a radiographic image. The reasoning unit estimates the most probable body part class based on class knowledge model. The model includes both objects and their spatial relationships.
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Object Oriented Knowledge Model
The main object
Component objects
Description attributesComponent objectsSemantic relations
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Pelvis Radiograph and Model
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Key Components of Pelvis Model
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Pelvis Class ModelA t t r i b u t e s : D e s c r i p t i o n : S i z e ,s h a p e ,o r i e n ta t io n , g r a y l e v e l
A t t r i b u t e s : C o m p o n e n t:
1 ) t h e h ip m o d e l2 ) t h e i l i a c m o d e3 ) t h e p u b ic m o d e l4 ) t h e f e m u r m o d e
S e m a n t i c G r a p h
P e l v i s M o d e lC l a s s
M e t h o d : C o n tr o l S t r a t e g y ( ) ;
M a t c h S e m a n t i c G r a p h ( ); F u z z y I n fe r e n c e ( ) ;
H i p M o d e l C l a s s
M e t h o d : M a t c h H i p S h a p e ( )
A t t r i b u t e s : D e s c r i p t i o n : S i z e , s h a p e , o r i e n ta t io n C o m p o n e n t
1 ) t h e l e f t i l i a c b o n e2 ) t h e r ig h t i l i a c b o n e
S e m a n t i c G r a p h
I l i a c M o d e lC l a s s
M e t h o d : M a t c h I l ia c S h a p e ( )
A t t r i b u t e s : D e s c r i p t i o n : S i z e , s h a p e ,o r ie n ta t io n g r a y l e v e l
P u b i c M o d e lC l a s s
M e t h o d : M a t c h P u b i c S h a p e ( )
A t t r i b u t e s : D e s c r i p t i o n : S i z e , s h a p e ,o r ie n ta t io n C o m p o n e n t 1 ) t h e l e f t i l i a c b o n e 2 ) th e r ig h t i l i a c b o n e S e m a n t i c G r a p h
F e m u r M o d e l C la s s
M e t h o d : M a t c h F e m u r S h a p e ( )
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Experimental Results for Pelvis Model
Green Bones Best Match to Model
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Experimental Results for Pelvis Model
Green Bones Best Match to Model
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Chest Radiograph and Typical Edge Map
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Semantic Network and Chest Model
Chest body
Spine
Left lung Right lung
insideinside
right ofleft of
Medialpart of
The semantic networks
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Diagram of All Model Objects
Attributes: Component:
1) the body model2) the spine
model3) the lung model
Semantic GraphMethod:
Schedule()
Chest Model Class
Attributes: Description:Size, Shape,Graylevel
Methods:BinaryImage ()MatchBodyShape ()
Body Model Class
Attributes: Description:Size, Shape,Graylevel
Methods:SpineDetection ()RefineSpineCour ()
Spine Model Class
Attributes: Description:Size, Shape,Graylevel
Methods:GenerateInitCour ()DeformCour ()
Lung Model Class
1 1
2 3 2 3
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Results -Initial Model
A
1
2
xy
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Lung Location Refined
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Spine Detection Based on Model
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Body Part Classification Based on Knee Model
Match: Red Bone modelsReject Green model
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Body Part Classification Based on Elbow Model
Match: Red Bone modelsReject Green model
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Patents and Publications for Model Based Classification
• Gaborski, R., US 5,943,435: Body part recognition in radiographic images.
• Gaborski, R., US6,018,590: Technique for finding the histogram region of interest based on landmark detection for improved tonescale reproduction of digital radiographic images.
• Jang, B. and Gaborski, R., US 5,862,249: Automated method and system for determination of positional orientation of digital radiographic images.
• Gaborski, R., et al, US 5,696,805:Apparatus and method for identifying specific bone regions in digital X-ray images
•
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Patents and Publications for Model Based Classification, continued
• Luo, H., Gaborski, R. and Acharya, R., "Knowledge Representation for Image Content Analysis in Medical Image Databases", SPIE International Symposium on Medical Imaging 2001.
• Luo,H., Gaborski,R. and Acharya, R., "Automatic Segmentation of Lung Regions in Chest Radiographs: A Model Guided Approach", IEEE International Conference on Image Processing (ICIP2000), Vancouver, Canada, 2000.
• Luo, H., Gaborski, R. and Acharya, R., "Robust Snake Model", Computer Vision and Pattern Recognition 2000, CVPR2000, Hilton Head Island, SC., 2000.
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Patents and Publications for Model Based Classification, continued
• Sun, Y., Gaborski, R. and Acharya, R., "A Practical Approach for Locating Bone Structures in Radiographic Pelvis Images", 1999 Western New York Image Processing Conference Workshop, Rochester, New York, 1999.
• Luo, H., Acharya, R. and Gaborski,R. , " Fully Automatic Detection of Spine in Chest Radiographs using Fuzzy Logic Approach, " Soft computing in Biomedicine, Rochester, New York, 1999.
• Luo, H., Acharya, R., Gaborski, R., " A New Fully Automatic Approach to Detect the Spine from X-ray Image", IEEE Western New York Image Processing Workshop, Rochester, New York, 1998.
• Luo, H., Acharya, R. and Gaborski,R. , " A Knowledge-based Method for Automatic Segmentation of Lung Regions in Digital Chest Radiographs", 1999 Western New York Image Processing Conference Workshop, Rochester, New York, 1999.