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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Discriminative shape alignment.

Marco Loog¹, Marleen de Bruijne

¹Pattern Recognition Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, The Netherlands. m.loog@tudelft.nl

Information Processing in Medical Imaging : Proceedings of the ... Conference

|August 22, 2009

Summary

This summary is machine-generated.

This study introduces a discriminative Procrustes alignment method for shape analysis. This new approach improves shape classification by considering class information during alignment, outperforming traditional unsupervised methods.

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Area of Science:

Computer Vision
Pattern Recognition
Geometric Data Analysis

Background:

Shape analysis commonly involves aligning data to a common mean.
Existing unsupervised alignment methods neglect class information crucial for classification tasks.
This gap limits performance in supervised shape classification.

Purpose of the Study:

To introduce a discriminative alignment method for shape analysis.
To enhance shape classification accuracy by incorporating class information.
To demonstrate the advantages of discriminative alignment over classical methods.

Main Methods:

Developed a discriminative variation of the Procrustes alignment algorithm.
Applied the method to two-dimensional shape data.
Evaluated performance in a two-class recognition problem.

Main Results:

The proposed discriminative alignment method shows significant benefits in shape classification tasks.
Outperformed the classical unsupervised Procrustes alignment.
Demonstrated improved accuracy in distinguishing between two classes of shapes.

Conclusions:

Discriminative alignment is a superior approach for shape classification compared to unsupervised methods.
Incorporating class information during alignment enhances recognition accuracy.
This method offers a valuable advancement for shape analysis and classification applications.