Computer-aided grading of gliomas based on local and global MRI features. (February 2017)

• Record Type: Journal Article
• Authors: Hsieh, Kevin Li-Chun; Lo, Chung-Ming; Hsiao, Chih-Jou
• Is Part Of: Computer methods and programs in biomedicine. Volume 139(2017)
• Page Start: 31
• Page End: 38
• Publication Date: 2017-02
• Subjects: Brain tumor -- Diffuse glioma -- Glioblastoma -- Computer-aided diagnosis -- Image moment -- Magnetic resonance imaging; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.10.021
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Numerous quantitative image features were developed from brain MR images to characterize gliomas of different gradings. Image features including local texture and global histogram moment features were combined for malignancy evaluation. The likelihoods of malignancy of tumors were predicted to provide diagnostic decisions to radiologists. Abstract: Background and objectives: A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors. Methods: The acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model. Results: Performances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy ( p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement κ = 0.698, p < 0.001. Conclusions: Numerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.