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Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images.

Muqaddas Abid¹, Muhammad Suzuri Hitam¹, Rozniza Ali¹

¹Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Malaysia.

Entropy (Basel, Switzerland)

|January 24, 2025

Summary

This summary is machine-generated.

We introduce two-dimensional increment entropy (IncrEn2D) for image texture analysis. This novel method outperforms existing techniques in classifying textures and medical images.

Keywords:

biomedical imaging multiscale increment entropy texture analysis texture irregularity two-dimensional increment entropy

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Area of Science:

Image analysis
Texture characterization
Entropy measures

Background:

Entropy algorithms quantify data irregularity and are vital in 2D image analysis.
One-dimensional increment entropy effectively detects signal changes.
Existing 2D entropy measures have limitations in texture analysis.

Purpose of the Study:

To introduce a novel two-dimensional increment entropy (IncrEn2D) for image texture analysis.
To encode image increments using two-letter words representing magnitude and direction.
To evaluate IncrEn2D's performance against established 2D entropy methods.

Main Methods:

Developed IncrEn2D by translating image increments into two-letter words.
Applied IncrEn2D to MIX(p) processes, synthetic textures, Kylberg dataset, and medical images.
Utilized a support vector machine with multiscale entropy features for validation.

Main Results:

IncrEn2D effectively analyzes image textures and medical images.
Experimental validation on diverse datasets demonstrated IncrEn2D's capabilities.
Comparative analysis showed IncrEn2D achieved higher classification accuracy than SampEn2 and DispEn2.

Conclusions:

IncrEn2D is a powerful new tool for image analysis and texture characterization.
IncrEn2D offers superior performance compared to existing bidimensional entropy measures.
The method shows promise for applications in medical image analysis and beyond.