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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

New texture shape feature coding-based computer aided diagnostic methods for classification of masses on mammograms.

Yuan Chen¹, Chein-I Chang

¹Dept. of Comput. Sci. & Electr. Eng., Maryland Univ., Baltimore, MD, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 3, 2007

Summary

This study introduces novel texture shape feature coding (TSFC) methods for classifying mammogram masses. These computer-aided diagnostic (CAD) techniques enhance mass feature extraction and classification accuracy using texture shape histograms.

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Image Analysis

Background:

Mammography is crucial for breast cancer screening.
Accurate classification of masses on mammograms is essential for diagnosis.
Existing computer-aided diagnostic (CAD) methods require improved feature extraction techniques.

Purpose of the Study:

To introduce new texture shape feature coding (TSFC) based CAD classification methods for mammogram masses.
To propose novel approaches for converting texture shape features into usable data for classification.
To evaluate the effectiveness of the proposed TSFC methods on a standard mammographic database.

Main Methods:

Developed a new 1.5-order 3-neighbor 3x3 connectivity for texture shape feature extraction.
Introduced two methods, TFNq (quaternary expansion) and TFNx (product), to convert features into texture feature numbers (TFNs).
Generated texture shape histograms from TFNq and TFNx for mass feature generation and classification.

Main Results:

The proposed TSFC methods effectively extract and encode texture shape features from mammograms.
Texture shape histograms derived from TFNq and TFNx provide a basis for mass classification.
Experimental results on the MIAS database demonstrate the potential of the TSFC-based CAD methods.

Conclusions:

The novel TSFC approach offers a promising new direction for computer-aided diagnosis in mammography.
The proposed feature coding and histogram generation methods can improve the classification of masses.
Further research can explore the application of these methods to larger datasets and other imaging modalities.