Brain Tumor Detection Based on Multilevel 2D Histogram Image Segmentation Using DEWO Optimization Algorithm

Sumit Kumar,Garima Vig,Sapna Varshney,Priti Bansal

doi:10.4018/ijehmc.2020070105

Abstract

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners. Multilevel image segmentation is a computationally simple and efficient approach for segmenting brain MR images. Conventional image segmentation does not consider the spatial correlation of image pixels and lacks better post-filtering efficiency. This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors. Further, to validate the efficiency of the proposed hybrid algorithm, it is compared with some prominent metaheuristic algorithms in recent past using between-class variance and the Tsallis entropy functions. The proposed hybrid algorithm for image segmentation is able to achieve better results than all the other metaheuristic algorithms in every entropy-based segmentation performed on brain MR images.

Highlights

In modern era, clinical experts take help of e-healthcare and automated systems to provide better diagnosis to the patients
This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors
The optimal threshold values obtained for brain magnetic resonance (MR) images using the proposed hybrid algorithm, DEWO, were found to give better results for all entropy functions considered in comparison to other metaheuristic algorithms like Differential Evolution (DE), Whale Optimization (WO), Artificial Bee Colony (ABC) and Cuckoo Search (CS)

Summary

Introduction

Clinical experts take help of e-healthcare and automated systems to provide better diagnosis to the patients. Inspection of abnormalities in internal organs is a tedious job that require invasive/non-invasive imaging approach. Medical image segmentation is the widely adopted technique in many real time applications as the pixel gray level value for objects in an image and the pixel gray level value for the image background are substantially different. This can be exploited to get various homogeneous regions in a medical image for synthesis and analysis (Heimann et al, 2009).

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