Please use this identifier to cite or link to this item:
|Title:||Computeraided Grading of Gliomas Based on Local and Global MRI Features|
Hsieh, Kevin Li-Chun
|Keywords:||Brain tumor;Diffuse glioma;Glioblastoma;Computer-aided diagnosis;Image moment;Magnetic resonance imaging|
|Issue Date:||2019-09-19 09:54:04 (UTC+8)|
|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.
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.
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.
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.
|Relation:||Computer Methods and Programs in Biomedicine, Vol.139, pp.31-38|
|Appears in Collections:||[圖書資訊與檔案學研究所] 期刊論文|
Files in This Item:
All items in 學術集成 are protected by copyright, with all rights reserved.