1. Biomedical Image Analysis Rangaraj M. Rangayyan Ch. 5 Detection of Regions of Interest: Sections 5.4-5.7, 5.10-5.11 Presentation March 3rd 2005 Jukka Parviainen Yevhen Hlushchuk

2. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 2 Outline segmentation – an ideal example problems in biomedical context categories for segmentation methods two methods explained with more details – detection of pectoral muscle in mammograms using Hough transform (section 5.10.1) summary & discussion

3. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 3 Books at table Sonka, Hlavac, Boyle: ”Image processing, analysis and machine vision” – chapter 5: Segmentation – similar terminology, examples from Rangayyan Gonzalez, Woods: ”Digital image processing” – chapter 9: Morphological image processing – chapter 10: Image segmentation – introduction: lots of biomedical applications Rangayyan includes some advanced methods

4. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 4 What is region of interest (ROI)? divide image into regions that correspond to structural units examples in mammograms: – tumors and masses – pectoral muscle – calcifications ROIs are detected using properties of – discontinuity = edges – similarity = regions

5. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 5 What is process ”segmentation”? segmentation reduces pixel data to region- based information highly application dependent simpliest case: thresholding gray-scale pixel values (Fig. 5.1)

6. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 6 Practical problems which make it a tough job! noise, noise, noise – derivatives are sensitive to noise, LoG especially low dynamic range – no exact borders in images (Fig. 5.32a, etc) stochastic algorithms: – need for a proper seed for region growing

7. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 7 Categories for segmentation methods

8. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 8 Categories for segmentation methods thresholding (M1) – problem: global, neglates all spatial information boundary-based (M2) – problem: edge segments to boundaries region-based (M3) – problem: selection of homogeneity criterion

9. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 9 M1 Thresholding class: all pixels whose values within a certain range determined by valleys in the image histogram – background and objects not always having bimodal histogram (Fig. 5.4/Sonka) optimal thresholding may fail due to illumination

10. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 10 M2 Boundary-based methods disjoint edge segments to closed-loop boundaries is a difficult job edge detection using gradient masks – gradient magnitude and direction – edge-flow propagation (p. 493) global Hough transform (section 5.6)

11. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 11 M3 Region-based methods region growing – pixel aggregation using additive tolerance / multiplicative tolerance region splitting/merging – split region into a non-overlapping set of subregions which all fulfill conditions or predicates P – usually quadtrees – adjacent similar subregions can be merged

12. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 12 M4 Other advanced methods and techniques morphological watershed fuzzy-set-based region growing (section 5.5) – fuzzy membership, crisp boundaries linear prediction for proper seeds (section 5.4.10) improvement of contour or region estimates (section 5.7)

13. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 13 Method #1: Region growing using an additive tolerance

14. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 14 Pixel aggregation using additive tolerance (section 5.4.4) compare properties of spatially neighboring pixels with those of seed pixel (Fig. 5.17) add pixel f(m,n) if |f(m,n)-seed| <= T what is a good seed? add pixel f(m,n) if |f(m,n)-mu_R| <= T where mu_R running- mean...

15. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 15 Method #2: Hough transform

16. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 16 Detection of objects of known geometry – Hough transform objects in images may sometimes be represented in an analytical form, such as straight-lines, circles, ellipses, parabolas Hough transform converts images to parametric plane, where analytical forms may be found easier (section 5.6) study of Hough transform and Gabor wavelet- based methods in mammogram data (5.10)

17. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 17 Hough: mapping to parameter space points at straight line yi = k xi + b, where k is slope and b (Fig 10.17/G) k and b are not limited – use rho and theta instead now each point corresponds a sinusoidal – line in original figure can be found as intersection of curves

18. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 18 Hough: example with 5 points five labeled points {1,...,5} (Fig. 10.20/G) top-right: five sinusoidals in parameter space bottom-left: [A] intersection of curves corresponding {1,3,5} at rho=0, theta = -45 deg; [B] similarly rho=0.707D, theta = +45 deg edge linking: compute gradient; subdivide rho and theta into bins; count

19. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 19 Application: Detection of pectoral muscle in mammograms (5.10.1) identify points {N1,..., N6} and ROI N1-N2-N3-N4 (Fig 5.64) geometric and anatomical constraints – p. muscle theta = {120.. 170} deg, intersects N1-N2,... LP + Sobel gradients in ROI count Hough accumulator cells eliminate impossible lines choose most likely (max) line

20. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 20 Application: Detection of pectoral muscle in mammograms 2 result:

21. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 21 Summary & discussion computer analysis starts with segmentation regions of interest (ROI) highly application dependent methods several large studies in the book comparing different segmentation methods

22. March 3rd 2005 T-61.182 Parviainen, Hlushchuk 22 Matlab Image Processing Toolbox help images version Matlab 5.3 - 7, IPT 2.2 - 4 – roidemo (enhancement) – qtdemo* (quadtree), edgedemo* – help iptdemos