A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain

TL;DR

This paper reviews various automated methods for detecting and segmenting brain tumors from MRI images, highlighting their advantages and limitations to guide future research.

Contribution

It provides a comprehensive overview of existing MRI-based brain tumor detection and segmentation techniques, emphasizing their strengths and weaknesses.

Findings

Different segmentation methods have varying accuracy and computational efficiency.

MRI-based detection approaches are crucial for early diagnosis and treatment planning.

The review identifies gaps and challenges in current techniques.

Abstract

Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy. There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images. These detection and segmentation approaches are reviewed with an importance placed on enlightening the advantages and drawbacks of these methods for brain tumor detection and segmentation. The use of MRI image detection and segmentation in different procedures are also described. Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.