Segmentation and classification of brain tumors from MRI images based on adaptive mechanisms and ELDP feature descriptor. (July 2022) - Rasool Reddy, K.; Dhuli, Ravindra
Highlights: A new framework is designed for the segmentation and classification of brain MR images using adaptive strategies. A new feature extraction technique is proposed namely ELDP to capture the inherent textual details from MR images. The efficiency of the proposed approach is evaluated on the well-known performance measures and compared with the recent well-known approaches. Abstract: Generally, soft tissue information of the brain is analyzed using Magnetic Resonance Imaging (MRI). An unusual growth of cells (tumor) in or around the brain may affect its overall functionality. Among brain tumors, gliomas that appear frequently in adults pose various challenges to radiologists. If they are not recognized properly in the initial phases, patients may face severe health issues including death. So, early and precise diagnosis of gliomas is significant for increasing the survival rate of patients. Recently, several computer-aided diagnosis (CAD) approaches have been developed for the detection and classification of gliomas. However, it is a challenging issue due to the noise as well as low sensitive boundary pixels appearing in gliomas. Considering this idea, we present a new approach based on adaptive methodologies and local texture descriptors. Here, primarily, we apply a median filter to remove noise from brain MRI images. Then, we employ adaptive decomposition strategies, namely bi-dimensional empirical mode decomposition (BEMD), and modified quasi-bivariate variational mode decomposition (MQBVMD) to attain meaningful sub-images. Later, we extract hidden texture details from the sub-images using an ELDP feature descriptor and then use a support vector machine (SVM) to distinguish brain MRI images as low-grade (LG) and high-grade (HG). Finally, we identify the infected region of gliomas with the help of adaptive K-means clustering and morphological operations. Using these sequences of operations, the proposed framework obtained 94.44% classification accuracy and 83.14% of dice similarity coefficient (DSC) value which is relatively high when compared to existing approaches.