Biomedical Signal Processing and Control 64 (2021) 102259 Available online 4 November 2020 1746-8094/© 2020 Elsevier Ltd. All rights reserved. Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm Itzel Aranguren a , Arturo Valdivia a , Bernardo Morales-Casta˜ neda a , Diego Oliva a, b, c, *, Mohamed Abd Elaziz d , Marco Perez-Cisneros a, * a Divisi´ on de Electr´ onica y Computaci´ on, Universidad de Guadalajara, CUCEI, Av. Revoluci´ on 1500, Guadalajara, Jal, Mexico b IN3 - Computer Science Dept., Universitat Oberta de Catalunya, Castelldefels, Spain c School of Computer Science & Robotics, Tomsk Polytechnic University, Tomsk, Russia d Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt A R T I C L E INFO Keywords: Magnetic resonance images Metaheuristic algorithms Minimum cross entropy Multilevel thresholding 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 identifcation of several diseases. Thresholding is a straightforward and effcient 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 al- gorithms are an important tool that helps to fnd 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 effciently 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 seg- mentation of magnetic resonance brain imaging. The proposed method has been tested using three groups of reference images— the frst 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 verifed results demonstrate that the suggested approach improves consistency and segmentation quality. 1. Introduction Medical imaging has become essential in the feld of medical diag- nosis, treatment assessment, and surgical planning. Different modalities are used to acquire medical images, for example, Positron Emission Tomography, Ultrasonography, Magnetic Resonance, and Computed Tomography. Magnetic Resonance (MR) is a non-invasive system that provides high spatial resolution and detailed information of anatomical structures. Nevertheless, the analysis of MR images is complex since they are affected by artifacts due to the non-uniformity intensity, the voluntary or involuntary movements of the patient, and the partial volume effect [1]. On the other hand, segmentation plays an important role in pre- processing techniques for medical imaging [2–4]. Segmentation ensues through the division into non-overlapped consistent areas of an image that shares specifc attributes such as texture, shape, or intensity. The segmentation of Magnetic Resonance Brain Images (MRBIs) has been broadly studied, considering that, with accurate segmentation of the brain, they can identify several brain illnesses such as multiple sclerosis, schizophrenia, Alzheimer’s disease and dementia [5,6]. MRBIs are frequently analyzed based on the experience and visual capacity of the expert professional. However, it is a time-consuming, complex task limited by the human vision that cannot distinguish most of the gray levels in an MR image [5,6]. Therefore, computer-aided techniques are * Corresponding author at: Dpto. Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Mexico. E-mail addresses: niasandiu.aranguren@alumno.udg.mx (I. Aranguren), arturo.valdivia@academicos.udg.mx (A. Valdivia), jb.moralescastaneda@gmail.com (B. Morales-Casta˜ neda), diego.oliva@cucei.udg.mx (D. Oliva), abd_el_aziz_m@yahoo.com (M. Abd Elaziz), marco.perez@cucei.udg.mx (M. Perez-Cisneros). Contents lists available at ScienceDirect Biomedical Signal Processing and Control journal homepage: www.elsevier.com/locate/bspc https://doi.org/10.1016/j.bspc.2020.102259 Received 30 December 2019; Received in revised form 29 September 2020; Accepted 4 October 2020