A review on brain tumor segmentation of MRI images

• Published in: Magnetic resonance imaging Vol. 61; pp. 247 - 259
• Main Authors: Wadhwa, Anjali, Bhardwaj, Anuj, Singh Verma, Vivek
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.09.2019
• Subjects: Algorithms; Animals; Brain - diagnostic imaging; Brain - pathology; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumor; Classification; Cluster Analysis; Ensemble learning; Fuzzy Logic; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging; MRI; Neural Networks, Computer; Segmentation; Support Vector Machine; Brain tumor; Segmentation; Ensemble learning; MRI; Classification
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2019.05.043

The process of segmenting tumor from MRI image of a brain is one of the highly focused areas in the community of medical science as MRI is noninvasive imaging. This paper discusses a thorough literature review of recent methods of brain tumor segmentation from brain MRI images. It includes the performance and quantitative analysis of state-of-the-art methods. Different methods of image segmentation are briefly explained with the recent contribution of various researchers. Here, an effort is made to open new dimensions for readers to explore the concerned area of research. Through the entire review process, it has been observed that the combination of Conditional Random Field (CRF) with Fully Convolutional Neural Network (FCNN) and CRF with DeepMedic or Ensemble are more effective for the segmentation of tumor from the brain MRI images.