Brain tumor segmentation on Multimodal MRI scans using EMAP Algorithm.

The utilization of digital images is becoming popular in multiple areas such as clinical applications. There are multiple diagnostic and machine vision-based applications, where image processing plays a vital role in analyzing, interpreting, and solving the problem. Digital image processing techniques are used to increase the quality of images for human interpretation and machine perception. Tumor segmentation in brain magnetic resonance (MRI) volumes is considered as a complex task because of tumor shape, location, and texture. Manual segmentation is a time-consuming task that can be inaccurate due to an increasing volume of MR scanning performed. The goal of this research is to propose an automated method that can identify the whole tumor in each slice in volumetric MRI brain images, and find out the sub-tumor (core tumor, enhancing and non-enhancing) regions. The proposed algorithm is fully automated to segment out both high-grade glioma (HGG) and low-grade glioma (LGG), using the information provided by a sequence of MRI volumes. The designed algorithm does not require any training database and estimates the tumor regions independently using image processing techniques based on expectation maximization and K-mean clustering. The method is evaluated on BRATS 2015 dataset of LGG and HGG MR volumes. The average DICE score achieved by using the proposed technique is 0.92 and is comparable to state-of-the-art techniques which rely on computationally expensive algorithms.