Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

By Jun Cheng, Wei Huang, Shuangliang Cao, Ru Yang, Wei Yang, Zhaoqiang Yun, Zhijian Wang, Qianjin Feng. 2015

815 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Neurology

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “The brain cancer images were taken from [160] and the human brain cancer characteristics were extracted from [161].”
Brain tumor classification using deep CNN features via transfer learning (2019, positive): “Based on sensitivity and specificity measures, the proposed method is superior to the works [17,19] that used these measures.”
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network (2021, methodology): “methodologies [5,32] start with segmentations performed by experienced radiologists, and thus, they are semi-automatic methods.”
Towards a guideline for evaluation metrics in medical image segmentation (2022, neutral): “[30,31] and cell nuclei detection in pathology microscopy from Caicedo et al.”
INet: Convolutional Networks for Biomedical Image Segmentation (2021, background): “The BTD [43] dataset contains 3,064 T1C images from 233 patients with three kinds of brain tumor: meningioma (708 slices), glioma (1,426 slices), and pituitary tumor (930 slices).”
Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “This framework was evaluated using the dataset of [63] which contains MRI images captured from 233 patients with different types of brain tumours: meningioma (708 samples), glioma (1426 samples), and pituitary (930 samples).”
Multi-Classification of Brain Tumor Images Using Deep Neural Network (2019, methodology): “First, data augmentation is used to avoid overfitting by making a geometric and color distortion on the original images [23], [31], [38].”
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, background): “In the literature, there are also studies that used the same database for classification with pre-trained networks [23,35–40] or, as input, they use only tumor region or some features that are extracted from the tumor region [7,21,23,41,42].”
Brain Tumor Type Classification via Capsule Networks (2018, methodology): “To test our proposed approach, we have used the data set presented in References [8,21].”
Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries (2018, background): “For example Meningiomas, typically, originate in the tissue be-tween the skull and the brain, and Gliomas are normally located in the substance of the brain.”