A Survey of Brain Tumor Segmentation and Classification Algorithms

Journal of Imaging

Taye Girma Debelee¹  Erena Siyoum Biratu¹  Yehualashet Megersa Ayano²  Friedhelm Schwenker³ 

[1] College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia;Ethiopian Artificial Intelligence Center, Addis Ababa 40782, Ethiopia;Institute of Neural Information Processing, Ulm University, 89081 Ulm, Germany;

关键词: brain tumor;    classification;    segmentation;    region growing;    shallow machine learning;    deep learning;   

DOI  :  10.3390/jimaging7090179

来源: DOAJ

【 摘 要 】

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

【 授权许可】

Unknown