Enhanced Performance of XXXX Classification via Tumor Region Augmentation and Partition.

@entity1313 @entity5 @entity11 @entity1653 @entity977 @entity2414
UNASSIGNED: 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 @entity1313 (i.e., @entity1653 , @entity11 , and @entity977 ) in T1-weighted contrast-enhanced MRI (CE-MRI) images. @entity2414 ( @entity2414 ), 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 @entity1313 , because of the great variations in @entity5 shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented @entity5 region via image dilation is used as the ROI instead of the original @entity5 region because @entity5 surrounding tissues can also offer important clues for @entity5 types. Second, the augmented @entity5 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 @entity5 region as ROI, using augmented @entity5 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 @entity1313 in T1-weighted CE-MRI.
