Biomedical%20Image%20Analysis%20Rangaraj%20M.%20Rangayyan

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Biomedical Image AnalysisRangaraj 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
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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

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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

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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

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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)

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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

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Categories for segmentation methods
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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

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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

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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)

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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

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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)

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Method 1 Region growing using an additive
tolerance
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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 lt T
• what is a good seed?
• add pixel f(m,n) if f(m,n)-mu_R lt T where mu_R
  running-mean...

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Method 2 Hough transform
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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)

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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

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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 rho0, theta -45 deg
  B similarly rho0.707D, theta 45 deg
• edge linking compute gradient subdivide rho and
  theta into bins count

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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

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Application Detection of pectoral muscle in
mammograms 2

• result

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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

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Matlab Image Processing Toolbox

• help images
• version Matlab 5.3 - 7, IPT 2.2 - 4
• roidemo (enhancement)
• qtdemo (quadtree), edgedemo
• help iptdemos