Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era. Among various cancers identified so far, glioma, a type of brain tumor, is one of the deadliest cancers, and it remains challenging to the medicinal world. The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease. Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time. From the captured image, the affected part of the brain is analyzed using magnetic resonance imaging (MRI) technique. Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells. Number of image processing techniques are available for processing the images to identify the affected area. This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization (P-BTLBO) algorithm that automatically segments a brain tumor in an given MRI image. Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique. The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism. The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard. The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.