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Visual Features for Content-based Medical Image Retrieval. Peter Howarth, Alexei Yavlinsky, Daniel Heesch, Stefan Rüger. http://km.doc.ic.ac.uk. Outline of presentation. CBIR Feature selection Texture Results and implications Conclusion. Relevance. 4521785. 3. feedback. Query.

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visual features for content based medical image retrieval

Visual Features for Content-based Medical Image Retrieval

Peter Howarth, Alexei Yavlinsky, Daniel Heesch, Stefan Rüger

http://km.doc.ic.ac.uk

outline of presentation
Outline of presentation
  • CBIR
  • Feature selection
  • Texture
  • Results and implications
  • Conclusion
a cbir system
Relevance

4521785

3

.

feedback

.

.

Query

4521785

3

k

-

nn

Feature

(boost, VSM)

Σ

wD

generation

Retrieved

Weighted

Distances

Test

4521785

3

results

sum of

.

.

data

.

distances

4521785

3

Feature

vectors

A CBIR system
the task
The task
  • Medical image collection, 8725 images, 25 single image queries
  • No training data
  • 1st stage, automatic visual query
feature choice
Feature choice
  • Dataset
    • High proportion monochrome
    • Precisely composed
    • Textures and structural elements
  • Layout
    • Thumbnail
  • Structural features
    • Convolution
    • Colour structure descriptor
  • Texture features
    • Co-occurrence
    • Gabor
  • Tiling
what is texture
What is texture?
  • Can it be defined?
    • Contrast, coarseness, fineness, direction, line-likeness, polarization, scale…
    • Regional property
    • How can these visual characteristics be captured in a feature?
grey level co occurrence matrices
Grey Level Co-occurrence Matrices
  • Haralick 1979
  • GLCM is a matrix of frequencies at which 2 pixels separated by a vector occur in the image
  • Generate the GLCM and then extract features
    • Energy
    • Contrast
    • Entropy
    • Homogeneity
co occurrence
Co-occurrence

Horizontal

vector

Vertical

vector

Query

gabor features
Gabor Features
  • Turner 1986, Manjunath 1996 2000
  • Gabor filters are defined by harmonic functions modulated by a Gaussian distribution
  • By varying the orientation and scale can detect edge and line features that characterize texture
gabor
Gabor

Query

Scale

Orientation

fusing features
Fusing features
  • Convex combination
  • W is the plasticity of the retrieval system
approaches to feature weighting
Approaches to feature weighting
  • Relevance feedback
  • SVM metaclassifier
    • Find optimum weights for retrieving an image class
    • [Yavlinsky et al ICASSP 04]
  • NNk
    • Find the nearest neighbour for for a given weight set
    • [Heesch et al ECIR 04]
conclusions
Conclusions
  • Gabor wavelet feature gave best retrieval performance for this test collection
  • Browsing approach is useful for image search
  • Next year…
    • Training data?
visual features for content based medical image retrieval1

Visual Features for Content-based Medical Image Retrieval

Peter Howarth, Alexei Yavlinsky, Daniel Heesch, Stefan Rüger

http://km.doc.ic.ac.uk

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