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Computer Aided Detection and
Diagnosis in Medical ImagingThe world through the computer’s eyes
Mira Park
School of Electronical Engineering and Computer Science
University of Newcastle
Computer’s eyes
It is extraordinarily difficult to develop computer
algorithms that analyse images with a performance level
comparable to that of human.
What a computer sees $
• Image processing
• Medical image processing
• Computer Aided Detection/Diagnosis system
Image Modalities• 2D images
• Chest Radiographs
• Microscopic image
• Retinal image (color fundus image)
• 3D images
• CT Colonography (Virtual Colonoscopy)
• Intracranial CT angiography
Computer Aided Detection/Diagnosis in Medical Imaging
(CAD2Mi)
9/2/2016Mira Park
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9/2/2016Mira Park
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9/2/2016Mira Park
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9/2/2016Mira Park
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Inside the CAD - References1. Park M., Wilson L., and Jin J., Automatic Extraction of Lung Boundaries by a Knowledge-Based Method, Visual Information Processing, 2:14-19,
2001
2. Park M., Jin J. and Wilson L., Detection and Measurement of Hilar Region in Chest Radiograph, Visual Information Processing, 83-87, Sydney
Australia, Nov 2002
3. Park M., Jin J. and Wilson L., Fast Content-Based Image Retrieval Using Quasi-Gabor Filter and Reduction of Image Feature, 5th IEEE
Southwest Symposium on Image Analysis and Interpretation, 18-21, 2002
4. Park M., Jin J. and Wilson L., Hierarchical Indexing Images using Weighted Low Dimensional Texture Features, 15th IAPR Vision Interface, 39-
44, 2002
5. Park M., Jin J. and Wilson L., A New Texture Analysis Method for Classification of Interstitial Lung Abnormalities in Chest Radiography, 7th
International Conference on Control, Automation, Robotics and Vision, 1636-1640, 2002
6. Park, M., Jin, J., and Wilson, L., Detection and labelling ribs on expiration chest radiographs, The Physics of Medical Imaging conference at
SPIE, 4(21):1021-1031, San Diego USA, Feb 2003
7. Park M., Jin J., and Wilson L., Detection of Abnormal Texture in Chest Radiographs with Reduction of Ribs, Visual Information Processing,
Sydney Australia, 71-74, Dec 2003
8. Park M., Jin J., and Wilson L., Texture Classification using Multi-scale Scheme, Visual Information Processing, Sydney Australia, 67-70, Dec
2003
9. Park M., Jin J., and Wilson L., Lung Texture Analysis using 3D Structure Classification, The Image Processing of Medical Imaging conference at
SPIE, San Diego USA, Feb 2004, (Abstract)
10. Park M., Robust 3D Texture Classifier using Score Block Operations, International Journal of Computer Science and Network Security,
6(9A):175-184, September 2006
11. Park M., Jin S.J. and Lro S., A Novel Approach for Enhancing the Visual Perception of Ribs in Chest Radiography, IEEE/ICME International
Conference on Complex Medical Engineering-CME, 856-859, 2007
12. Park M., Cao T., Jin J. and Wilson L., Multi Classification Ripple Down Rule and Fuzzy Set in Computer Aided Diagnosis, International
Federation of Automatic Control (IFAC) on modelling and control in biomedical systems, 433-438, Melbourne Australia, Aug 2003
13. Park M., Jin J. and Wilson L., Intelligent Computer Aided Diagnosis for Chest Radiography, Computer Assisted Radiology and
Surgery(CARS), 1256(2003):1005-1010, London England, Jun 2003
14. Park M. and Kotagiri R., Automatic Extraction of Semantic Concepts in Medical Images, IEEE International Conference on Image
Processing (ICIP04), 1157-1160, Singapore, Oct 2004
15. Park M., Kotagiri R. and Hofstetter R., Medical Image Database Construction for Computer Aided Diagnosis, Computer Assisted Radiology
and Surgery (CARS05), 1281(2005):1408, ICC Berlin, Germany, July 2005
16. Park M. and Kotagiri R., Content-Based Image Retrieval for Texture Queries in Medical Image, Visual Information Processing, Beijing,
China, 109-114, Nov 2006
17. Park M, Kang B., Jin S.J. Luo S., Computer Aided Diagnosis System of Medical Images using Incremental Learning Method, Expert
Systems with Applications, Volume 36, Issue 3, Part 2, pages 7242-7251, 2009
The retina as a window to the brain
Eye-phone that could help prevent
blindness
University of St Andrews: Visionary scientists have developed an adapted
smartphone that can carry out eye tests and diagnose problems with vision
15
Locating the Optic Disc in Retinal Images
References
• Park M, Jin JS, Luo S, 'Locating the Optic Disc in Retinal Images',
Locating the Optic Disc in Retinal Images (2006)
Diabetic Retinopathy
(a) (b) (c)
(e) (f) (g)
19
Intro – Why colonoscopy?
• colon cancer is the second leading form of cancer.
• the odds of a cure are higher than 90 percent when tumors in the colon are found and treated early.
Automatic polyp detection in CTC
Microscopic Image analysiMicroscopic Image segmentation
Mira Park 21
Microscopic image - References
• Peng Y., Park M., Xu M., Luo S., Jin S.J., Cui Y., Wong F., Santos L., Clustering Nuclei
Using Machine Learning Techniques, The 2010 IEEE/ICME International Conference on
Complex Medical Engineering, 52-57, 2010
• Cui Y., Jin S.J., Park M., Luo S., Xu M., Peng Y., Wong F., Santos L., Computer Aided
Abnormality Detection for Microscopy Images of Cervical Tissue, The 2010 IEEE/ICME
International Conference on Complex Medical Engineering, 63-68, 2010
• Peng Y., Park M., Xu M., Luo S., Jin S.J., Cui Y., Wong F., Detection of Nuclei Clusters
from Cervical Cancer Microscopic Image using C4.5, The International Conference on
Computer Engineering and Technology, 3:593-597, 2010
• Yu D., Jin J., Luo S., Lai W., Park M., Shape Analysis and Recognition Based on
Skeleton and Morphological Structure, International Conference Computer Graphics,
Imaging and Visualization: CGIV, 118-123, 2010
• Park M., Jin S.J., Xu M., Wong F., Luo S., Cui Y., Microscopic Image Segmentation
Based on Color Pixels Classification, The International ACM Conference on Internet
Multimedia Computing and Service (ACMICIMCS 2009), 61-77, 2009
22
Method - segmentation
(b) 2D orthogonal slices and 3D CT colon (c) Inside view by fly through tool
Enema hose
Polyp
Figure 1.3D CT Colonography
(a) 3D CT colon (b) 2D orthogonal slices and 3D CT colon (c) Inside view by fly through tool
Enema hose
Polyp
Figure 1.3D CT Colonography
Automatic polyp detection in CTC
23
Automatic polyp detection in CTC - References
1. Park M., Jin S.J., Xu M., Kang B.H., Automatic Colonic Polyp Detection by
the Mapping using Regional Unit Sphere, International Journal of Software
Engineering and Its Applications, 3(1):11-18, 2009
2. Park M., Jin S.J, Hofstatter R., Luo S., Summon S., Classification of Colonic
Polyp using Hidden Markov Models, International Conference Image and
Vision Computing New Zealand: IVCNZ2008, , 131-138, 2008
3. Park M., Jin S.J., Xu M., Kang B.H., Automatic Colonic Polyp Detection by
the Mapping using Regional Unit Sphere, Proceedings of the 2nd
International Conference on Multimedia and Ubiquitous Engineering, 114-
149, 2008
4. Park M., Hofstetter R. and Luo S., Automatic Polyp detection in CT
Colonography, Visual Information Processing, Beijing, China, 135-141, Nov
2006
24
Mimkit Main Window
Orthogonal axis Coronal
Sagittal
Computer Aided Intracranial Aneurysms Detection in CTA
25
Annotation of imageComputer Aided Intracranial Aneurysms Detection in CTA
Computer Aided Detection/Diagnosis in Medical Imaging
(CAD2Mi)
• Image processing
• Mathematical operations
• Pre-processing: improve image quality
• Computerized image features
• Imagination
• the ability to form new world to make knowledge applicable in solving problems
CAD2Mi: Image Processing
RedGreenBlue
Red band Green band Blue band
Microscopic Image segmentation
Pre-processingMicroscopic Image segmentation
Anisotropic DiffusionMicroscopic Image segmentation
Anisotropic Diffusion
))((),,( IIgdivtyxIt
∇∇=∂
∂
∇−=
2),,(
exp),,(κ
tyxItyxg
Microscopic Image segmentation
Test ImageMicroscopic Image segmentation
33
Inside the CAD - Lung Texture
Normal lung texture Fine dots & lines Grapes texture
34
Inside the CAD - Lung Texture Analysis
AP
Class 1
Class 2
Class 3
Class 4
Class n
NP
AP: Abnormal Partition image database
NP: Normal Partition image database
classification
35
Inside the CAD - FFT
• Remove mean : To avoid the positive peaks of the waveforms exceed the maximum level
• Hamming windowing : to reduce FFT leakage and the side lobes
• 2DFFT 2d Discrete Fast Fourier Transform
36
Inside the CAD - Quasi-Gabor Filter
Get
vector
0’: a1,a2,a3,a4,a5,a6
36’: b1,b2,b3,b4,b5,b6
72’: c1,c2,c3,c4,c5,c6
108’:d1,d2,d3,d4,d5,d6
144’:e1,e2,e3,e4,e5,e6
45’: f1,f2,f3,f4,f5,f6
135’:g1,g2,g3,g4,g5,g6
Get
vector
0º: a1,a2,a3,a4,a5,a6
36º: b1,b2,b3,b4,b5,b6
72º: c1,c2,c3,c4,c5,c6
108º:d1,d2,d3,d4,d5,d6
144º:e1,e2,e3,e4,e5,e6
45º: f1,f2,f3,f4,f5,f6
135º:g1,g2,g3,g4,g5,g6
• 7 orientations : 0°, 36°, 72°, 108°, 144°, 45° and 135°
• f = 1, 2, 4, 8, 16, and 32
• Square size = f2 * 2n / 27 where the image is 2n x 2n
• 42 features
b6
a4 a5a6
c6d6
e6
f6g6
Computer Aided Detection/Diagnosis in Medical Imaging
(CAD2Mi)
• Image processing
• Mathematical operations
• Pre-processing: improve image quality
• Computerized image features
• Imagination
• the ability to form new world to make knowledge
applicable in solving problems
38
Inside the CAD - Detection Ribs
Inspiration Chest Radiograph Expiration Chest Radiograph
39
Inside the CAD - Detection Ribs - 4
Expiration radiographInspiration radiograph
40
Inside the CAD - Detection Ribs - 5
Inspiration Chest Radiograph Expiration Chest Radiograph
41
Inside the CAD - Detection Ribs - 1
v(xi,yj) = (f(x0,y0)-((xi-x0)^2/rx)-((yj-y0)^2/ry));
(x0,y0)
ry
rx
if v(xi,yj) >= 0
f’(xi,yj) = f(xi,yj) + v(xi,yj);
else
f’(xi,yj) = f(xi,yj);
v(xi,yj)Hemi_spherical cavity
42
Inside the CAD - Detection Ribs
Inspiration Chest Radiograph Expiration Chest Radiograph
Diabetic Retinopathy
(a) (b) (c)
(e) (f) (g)
Diabetic Retinopathy
(a) (b)
(c)
45
Method - segmentation
(b) 2D orthogonal slices and 3D CT colon (c) Inside view by fly through tool
Enema hose
Polyp
Figure 1.3D CT Colonography
(a) 3D CT colon (b) 2D orthogonal slices and 3D CT colon (c) Inside view by fly through tool
Enema hose
Polyp
Figure 1.3D CT Colonography
Automatic polyp detection in CTC
46
Method – surface normal vector
Automatic polyp detection in CTC
47
Method –
sphere
container
and
endpoints
of normals
Automatic polyp detection in CTC
Figure 1. 1a. A polyp in WRAMC VC-386, 1b. the segmented polyp (a(1)) by the CoLN method,
1c. 3D illustration of a results by the MuRUS method, 1d. 2D projection illustration of a(3)
2a. A polyp in WRAMC VC-386, 2b. the segmented polyp (a(1)) by the CoLN method,
2c. 3D illustration of a results by the MuRUS method, 2d. 2D projection illustration of a(3)
1a 1b 1c 1d
2d2c2b2a
Automatic polyp detection in CTC
Thank you