computer aided detection and diagnosis in medical...

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)

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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

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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)

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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

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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



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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

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Mimkit Main Window

Orthogonal axis Coronal

Sagittal

Computer Aided Intracranial Aneurysms Detection in CTA

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Annotation of imageComputer Aided Intracranial Aneurysms Detection in CTA


(CAD2Mi)


• 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

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Inside the CAD - Lung Texture

Normal lung texture Fine dots & lines Grapes texture

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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

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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

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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


(CAD2Mi)


• Mathematical operations

• Pre-processing: improve image quality

• Computerized image features

• Imagination

• the ability to form new world to make knowledge

applicable in solving problems

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Inside the CAD - Detection Ribs

Inspiration Chest Radiograph Expiration Chest Radiograph

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Inside the CAD - Detection Ribs - 4

Expiration radiographInspiration radiograph

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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

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Inside the CAD - Detection Ribs



(a) (b) (c)

(e) (f) (g)


(a) (b)

(c)

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Method - segmentation

(b) 2D orthogonal slices and 3D CT colon (c) Inside view by fly through tool

Enema hose

Polyp


(a) 3D CT colon (b) 2D orthogonal slices and 3D CT colon (c) Inside view by fly through tool

Enema hose

Polyp



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Method – surface normal vector


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Method –

sphere

container

and

endpoints

of normals


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


Thank you

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