A Combined Method Of Fractal And GLCM Features For MRI And CT Scan Images Classification

TL;DR

This paper introduces a combined fractal and GLCM feature extraction method for classifying MRI and CT scan images, aiming to enhance medical image analysis and diagnosis of osteoporosis.

Contribution

It proposes a novel combination of fractal and GLCM features for improved classification of medical images, particularly in detecting osteoporosis-related textures.

Findings

Effective classification of MRI and CT images achieved

Improved accuracy in osteoporosis diagnosis

Potential for clinical diagnostic enhancement

Abstract

Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of irregularity of the medical images. This descriptor property does not give ownership of the local image structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM Features. This powerful combination has proved good results especially in classification of medical texture from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical diagnostics tests for osteoporosis pathologies.