Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. (July 2015)

• Record Type: Journal Article
• Authors: Nabizadeh, Nooshin; Kubat, Miroslav
• Is Part Of: Computers & electrical engineering. Volume 45(2015)
• Page Start: 286
• Page End: 301
• Publication Date: 2015-07
• Subjects: Lesion detection/segmentation -- Gabor wavelet features -- Statistical features -- MR imaging -- T1-weighted -- Fluid-attenuated inversion recovery; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.02.007
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: A fully automatic system for detection of slices that contain tumor in MR images is presented. A fully automatic system for tumor segmentation using single-spectral MR images is presented. A study for evaluating the efficacy of statistical features over Gabor wavelet features is included. Abstract: Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure owing to the variability and complexity of the location, size, shape, and texture of these lesions. Because of intensity similarities between brain lesions and normal tissues, some approaches make use of multi-spectral anatomical MRI scans. However, the time and cost restrictions for collecting multi-spectral MRI scans and some other difficulties necessitate developing an approach that can detect tumor tissues using a single-spectral anatomical MRI images. In this paper, we present a fully automatic system, which is able to detect slices that include tumor and, to delineate the tumor area. The experimental results on single contrast mechanism demonstrate the efficacy of our proposed technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity. Moreover, we include a study evaluating the efficacy of statistical features over Gabor wavelet features using several classifiers. This contribution fills the gap in the literature, as is the first to compare these sets of features for tumor segmentation applications.