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 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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

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) T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

Categories for segmentation methods 7

T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

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) T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

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) T-61.182 Parviainen, Hlushchuk March 3rd 2005

Method #1: Region growing using an additive tolerance 13

T-61.182 Parviainen, Hlushchuk March 3rd 2005

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...

T-61.182 Parviainen, Hlushchuk March 3rd 2005

Method #2: Hough transform 15

T-61.182 Parviainen, Hlushchuk March 3rd 2005

Detection of objects of known geometry – Hough transform 16

 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) T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

Application: Detection of pectoral muscle in mammograms 2

 result:

20

T-61.182 Parviainen, Hlushchuk March 3rd 2005

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 T-61.182 Parviainen, Hlushchuk March 3rd 2005

22 Matlab Image Processing Toolbox

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