Development of a combinational framework to concurrently perform tissue segmentation and tumor identification in T1 - W, T2 - W, FLAIR and MPR type magnetic resonance brain images

Over decades, medical image diagnosis has gained prominence, as it has saved millions of lives from dreadful diseases. Improvising image processing techniques in medical images has provoked substantial increment in the efficiency levels of patient diagnosis. Segregation and visualization of pathologies have been made available by several image processing algorithms, under which tumor recognition and tissue segmentation in Magnetic Resonance (MR) brain images are accomplished in this paper using a novel approach. The novel methodology suggested through this paper ensemble the functioning of two different techniques well known to the research community. The techniques are Bacteria Foraging Optimization (BFO) and Modified Fuzzy C - Means (MFCM) algorithms, where, BFO is familiar for its optimization abilities and MFCM, an advancement of Fuzzy C - Means algorithm initiates the clustering operation. Both these techniques are well employed into a single framework to perform MR brain image segmentation, so that effective tumor segregation and tissue segmentation can be achieved, concurrently. Frequent parameter tuning is not required in the case of the proposed combinational algorithm, which is entirely an automated approach, and development of such algorithm would facilitate the radiologists in patient diagnosing procedures, as they extricate both manual intervention and large time consumption. With the support rendered by an automated algorithm, large volumes of clinical datasets could be assessed with ease. The proposed algorithm is validated by the radiologists and also using the comparison parameters such as sensitivity, Specificity, Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and computational time. By analysing the above said parameters it has been proved that the proposed algorithm is prodigious than the state-of-art segmentation algorithms. The sensitivity and the specificity values offered by the proposed methodology are 0.9048 and 0.9825, respectively. In addition to the clinical datasets, MR brain images from Harvard Brainweb database, Brainweb simulated database and BRATS-2013 challenge database are used to demonstrate the segmentation efficiency of the proposed algorithm. These factors are good enough to prove that the proposed combinational framework can be preferably opted for medical image analysis.