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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

A new approach for subset 2-D AR model identification for describing textures.

A Sarkar¹, K S Sharma, R V Sonak

¹Dept. of Math., Indian Inst. of Technol., Kharagpur.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1997

Summary

This study introduces a new method for identifying autoregressive (AR) components in digital image textures using 2-D AR models. The technique effectively selects optimal AR components for texture analysis and classification.

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Area of Science:

Digital Image Processing
Pattern Recognition
Statistical Modeling

Background:

Accurate autoregressive (AR) component identification is crucial for describing textural regions in digital images.
Existing methods may not fully capture the complexities of 2-D image textures.

Purpose of the Study:

To develop a robust technique for identifying appropriate autoregressive (AR) components for 2-D AR models of image textures.
To enhance texture classification accuracy by optimizing AR model selection.

Main Methods:

Utilizes a neighborhood set selection based on significant partial auto-correlation coefficients.
Employs Singular Value Decomposition (SVD) and QRcp factorization for matrix analysis.
Applies Schwarz's Information Criterion (SIG) to determine optimal 2-D lag variables.

Main Results:

Successfully identifies optimal autoregressive components for textural image analysis.
Demonstrates a four-class texture classification scheme using the developed models.
Provides a comparative analysis against established texture classification techniques.

Conclusions:

The proposed method offers an effective approach for selecting 2-D AR model components for image textures.
This technique improves the representation and classification of textural patterns.
The findings contribute to advancements in digital image texture analysis.