A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images. (December 2022)

• Record Type: Journal Article
• Authors: Jena, Bibekananda; Naik, Manoj Kumar; Panda, Rutuparna; Abraham, Ajith
• Is Part Of: Computers in biology and medicine. Volume 151:Part A(2022)
• Publication Date: 2022-12
• Subjects: Biomedical engineering -- Machine learning -- Multilevel thresholding -- Generalized cross-entropy -- Optimization algorithm; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106214
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: One of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a life-threatening disease. So, an efficient automatic image segmentation approach is required at the initial stage of medical image analysis. Purpose: To make a segmentation process efficient and reliable, it is essential to use an appropriate objective function and an efficient optimization algorithm to produce optimal results. Method: The above problem is resolved in this paper by introducing a new minimum generalized cross entropy (MGCE) as an objective function, with the inclusion of the degree of divergence. Another key contribution is the development of a new optimizer called opposition African vulture optimization algorithm (OAVOA). The proposed optimizer boosted the exploration, skill by inheriting the opposition-based learning. The results: The experimental work in this study starts with a performance evaluation of the optimizer over a set of standards (23 numbers) and IEEE CEC14 (8 numbers) Benchmark functions. The comparative analysis of test results shows that the OAVOA outperforms different state-of-the-art optimizers. The suggested OAVOA-MGCE based multilevel thresholding approach is carried out on two different types of medical images – Brain MRI Images (AANLIB dataset), and dermoscopic images (ISIC 2016 dataset) and found superior than other entropy-based thresholding methods. Highlights: An opposition African vulture optimization algorithm is proposed and validated. A novel multilevel thresholding method for medical image analysis is fostered. A novel objective function is investigated for minimization of generalized cross-entropy. The quantitative and qualitative analysis using statistical methods is highlighted. Experimental results on medical images are presented.