Salp Swarm Algorithm with Multilevel Thresholding Based Brain Tumor Segmentation Model

Abstract

Biomedical image processing acts as an essential part of several medical applications in supporting computer aided disease diagnosis. Magnetic Resonance Image (MRI) is a commonly utilized imaging tool used to save glioma for clinical examination. Biomedical image segmentation plays a vital role in healthcare decision making process which also helps to identify the affected regions in the MRI. Though numerous segmentation models are available in the literature, it is still needed to develop effective segmentation models for BT. This study develops a salp swarm algorithm with multi-level thresholding based brain tumor segmentation (SSAMLT-BTS) model. The presented SSAMLT-BTS model initially employs bilateral filtering based on noise removal and skull stripping as a pre-processing phase. In addition, Otsu thresholding approach is applied to segment the biomedical images and the optimum threshold values are chosen by the use of SSA. Finally, active contour (AC) technique is used to identify the suspicious regions in the medical image. A comprehensive experimental analysis of the SSAMLT-BTS model is performed using benchmark dataset and the outcomes are inspected in many aspects. The simulation outcomes reported the improved outcomes of the SSAMLT-BTS model over recent approaches with maximum accuracy of 95.95%.