Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm. (February 2021) - Aranguren, Itzel; Valdivia, Arturo; Morales-Castañeda, Bernardo; Oliva, Diego; Abd Elaziz, Mohamed; Perez-Cisneros, Marco
Highlights: The LSHADE algorithm is proposed for accurate MRBI segmentation. The minimum cross entropy is used by the LSHADE as objective function. The proposed segmentation method is tested over different datasets of MRBI. Segmentation metrics verify the quality of the LSHADE results in MRBI. By using LSHADE segmentation, it is possible to identify tumors in MRBI. Abstract: Segmentation is an essential preprocessing step in techniques for image analysis. The automatic segmentation of brain magnetic resonance imaging has been exhaustively investigated since the accurate use of this kind of methods permits the diagnosis and identification of several diseases. Thresholding is a straightforward and efficient technique for image segmentation. Nonetheless, thresholding based approaches tend to increase the computational cost based on the number of thresholds used for the segmentation. Therefore, metaheuristic algorithms are an important tool that helps to find the optimal values in multilevel thresholding. The adaptive differential evolution, based in numerous successes through history, with linear population size reduction (LSHADE) is a robust metaheuristic algorithm that efficiently solves numerical optimization problems. The main advantage of LSHADE is its capability to adapt its internal parameters according to prior knowledge acquired along the evolutionary process. Meanwhile, the continuous reduction of the population improves the exploitation process. This article presents a multilevel thresholding approach based on the LSHADE method for the segmentation of magnetic resonance brain imaging. The proposed method has been tested using three groups of reference images— the first group consists of grayscale standard benchmark images, the second group consists of magnetic resonance T2-weighted brain images, and the third group is formed by images of unhealthy brains affected by tumors. In turn, the performance of the intended approach was compared with distinct metaheuristic algorithms and machine learning methods. The statistically verified results demonstrate that the suggested approach improves consistency and segmentation quality.