Brain tumor classification from multi-modality MRI using wavelets and machine learning

Khalid Usman,Kashif Rajpoot

doi:10.1007/s10044-017-0597-8

Abstract

In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. The data from multi-modal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skull-stripped, and the histogram matching is performed with a reference volume of high contrast. From the preprocessed images, the following features are then extracted: intensity, intensity differences, local neighborhood and wavelet texture. The integrated features are subsequently provided to the random forest classifier to predict five classes: background, necrosis, edema, enhancing tumor and non-enhancing tumor, and then these class labels are used to hierarchically compute three different regions (complete tumor, active tumor and enhancing tumor). We performed a leave-one-out cross-validation and achieved 88% Dice overlap for the complete tumor region, 75% for the core tumor region and 95% for enhancing tumor region, which is higher than the Dice overlap reported from MICCAI BraTS challenge.

Highlights

The detection and diagnosis of brain tumor from MRI is crucial to decrease the rate of casualties
In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans
The data from multimodal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skullstripped, and the histogram matching is performed with a reference volume of high contrast

Summary

Introduction

The detection and diagnosis of brain tumor from MRI is crucial to decrease the rate of casualties. Brain tumor is difficult to cure, because the brain has a very complex structure and the tissues are interconnected with each other in a complicated manner. Robust and efficient segmentation of brain tumor is still an important and challenging task. Tumor segmentation and classification is a challenging task, because tumors vary in shape, appearance and location. It is hard to fully segment and classify brain tumor from mono-modality scans, because of its complicated structure. MRI provides the ability to capture multiple images known as multimodality images, which can provide the detailed structure of brain to efficiently classify the brain tumor [1].

Results

Discussion

Conclusion