Medical image segmentation using exchange market algorithm

Abstract

Color medical images hold noteworthy impact in clinical conclusion and treatment. Medical image segmentation reduces the uncertainty by providing detailed information about the shape, size, or location characteristics. However, traditional methods suffer from low accuracy, high complexity, and are less robust. To overcome these drawbacks, this paper proposes an efficient metaheuristic algorithm, exchange market algorithm (EMA) for multilevel thresholding (MLT) of distinct medical images. Optimal threshold is effectively obtained through the most promising objective functions such as Kapur, Otsu and minimum cross entropy (MCE) aided with EMA. The EMA involves exchange of shares among the investors in stable and unstable market situations to achieve profit. Exploration and exploitation are achieved by second and third groups of stable and unstable modes of EMA. Moreover, the execution time is reduced by the highly competent shareholders retaining their top rank without any changes in their shares. The efficacy of the proposed paper is evaluated on three distinct medical images at 4, 5, 6 and 7th threshold levels and compared with the recent algorithms such as Krill herd (KHA), Teaching-learning based optimization (TLBO) and Cuckoo search algorithm (CSA). Quantitative and qualitative validation by metrics such as computational time, Peak signal to noise ratio (PSNR), Structural similarity index (SSIM) and Wilcoxon rank sum test affirm that the EMA is superior to other algorithms. On the other hand, Otsu based EMA method is found to be more accurate and robust for improved clinical decision making and diagnosis.

Keywords

• Kapur
• Otsu
• Minimum cross entropy
• Exchange market algorithm
• Krill herd
• Teaching-learning based optimization