A survey of MRI-based brain tumor segmentation methods

Abstract

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). MRIbased brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

“…The original SVM algorithm was contributed by Vladimir N. Vapnik and its modern version was developed by Cortes and Vapnik in 1993 [33]. The SVM algorithm is based on the study of a supervised learning technique and is applied to one-class classification problem to n-class classification problems [1, 34–36].…”

Magnetic Resonance Imaging Segmentation Techniques of Brain Tumors: A Review

“…If the object can be segmented by a single threshold, it is noted as global thresholding. However, if there are more than two objects, then the segmentation should be implemented using local thresholding (20,23,25,26). The main problem of this type of segmentation is that only the intensity information is considered and the relationships between the pixels are neglected; therefore, some pixels do not attend the desired or the background regions.…”

“…The deformable model is used for a wide range of applications, especially in medical fields, due to its capability to accommodate the variability of biological structures of different patients (10,34). Jin et al (25), and Gordillo et al (20), concluded that the good results of brain tumor segmentation using conventional methods (e.g., region based method, pixel based method, and edge based method) are hard to achieve. Additionally, due to the emersion of volumetric three-dimensional medical imaging data, the segmentation of this data is a challenging problem to extract the boundary features that belong to the same structure.…”

“…The main disadvantage of region growing methods is the PV effect, which limits the accuracy of MR brain image segmentation. Therefore, PV blurs the borders between different tissue, since the voxel may contain more than one kind of tissue types (20,25,30). Region growing methods are more sensitive to noise, thus producing holes in the extracted regions (18).…”

A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images

“…Because of this, many fully automatic and semi-automatic algorithms have been created to expedite the segmentation process. Reviews of these methods can be found in Bauer et al (2013b) and Wang and Liu (2014). Benchmarks of fully automatic algorithms have demonstrated encouraging accuracies (Menze et al, 2015), but acceptance of these methods in the clinic is limited due to concerns about errors and transparency (Gordillo et al, 2013).…”