Visual features for content based medical image retrieval
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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

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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


Results

Results


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]


Nn k browsing

NNk Browsing


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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