title: Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.
abstract: Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Cheng
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Huang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Shuangliang
authors.initials: S
authors.lastname: Cao
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Ru
authors.initials: R
authors.lastname: Yang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Zhaoqiang
authors.initials: Z
authors.lastname: Yun
authors.affiliation: Department of Obstetrics and Gynecology, Nanfang Hospital of Southern Medical University, Guangzhou, China.
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Wang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
mesh_terms: D001932:Brain Neoplasms / Q000145:classification / Q000175:diagnosis*; D005910:Glioma / Q000145:classification / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008579:Meningioma / Q000145:classification / Q000175:diagnosis*; D012680:Sensitivity and Specificity