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Medical image classification using spatial adjacent histogram based on adaptive local binary patterns.

Dong Liu¹, Shengsheng Wang¹, Dezhi Huang¹

¹Jilin University, College of Computer Science and Technology, Changchun 130012, China; Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China.

Computers in Biology and Medicine

|April 9, 2016

Summary

This summary is machine-generated.

This study introduces an improved local binary pattern (LBP) method for medical image analysis. By adapting neighborhood radius and encoding spatial relationships, it enhances feature extraction for complex microscopic images.

Keywords:

Feature extraction Image classification Local binary patterns Medical images Microscope images

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Area of Science:

Medical Image Analysis
Computer Vision
Computational Biology

Background:

Local Binary Patterns (LBP) are popular for medical image classification.
Existing LBP methods use fixed neighborhood radii, ignoring spatial relationships.
This limitation hinders feature extraction for complex medical images like microscopy samples.

Purpose of the Study:

To develop an improved Local Binary Pattern (LBP) method for medical image representation.
To address the limitations of fixed neighborhood radii in standard LBP.
To enhance the capture of discriminative features in complex medical images.

Main Methods:

Proposed a novel method assigning an adaptive neighborhood radius for each pixel.
Developed a spatial adjacent histogram strategy to encode micro-structures.
Evaluated the method on four diverse medical image datasets.

Main Results:

The proposed adaptive LBP method significantly improved performance over standard LBP.
The spatial adjacent histogram strategy effectively encoded micro-structural information.
The method demonstrated favorable comparisons against other prevailing image representation approaches.

Conclusions:

Adaptive neighborhood radius and spatial encoding enhance LBP for medical image analysis.
The novel method offers improved feature representation for complex microscopic medical images.
This approach shows significant potential for advancing medical image classification tasks.