Please use this identifier to cite or link to this item:
|Title:||Texture analysis of bone marrow in knee MRI for classification of subjects with bone marrow lesion : data from the osteoarthritis initiative||Authors:||Chuah, Tong Kuan
Reeth, Eric Van
Poh, Chueh Loo
|Keywords:||DRNTU::Engineering::Chemical engineering::Biochemical engineering||Issue Date:||2013||Source:||Chuah, T. K., Reeth, E. V., Sheah, K., & Poh, C. L. (2013). Texture analysis of bone marrow in knee MRI for classification of subjects with bone marrow lesion : data from the osteoarthritis initiative. Magnetic resonance imaging, 31(6), 930-938.||Series/Report no.:||Magnetic resonance imaging||Abstract:||Visualization of bone marrow lesion (BML) can improve the diagnosis of many bone disorders that are associated with it. A quantitative approach in detecting BML could increase the accuracy and efficiency of diagnosing those bone disorders. In this paper, we investigated the feasibility of using magnetic resonance imaging (MRI)-based texture to (a) identify slices and (b) classify subjects with and without BML. A total of 58 subjects were studied; 29 of them were affected by BML. The ages of subjects ranged from 45 to 74years with a mean age of 59. Texture parameters were calculated for the weight-bearing region of distal femur. The parameters were then analyzed using Mann–Whitney U test and individual feature selection methods to identify potentially discriminantive parameters. Forward feature selection was applied to select features subset for classification. Classification results from eight classifiers were studied. Results show that 98 of the 147 parameters studied are statistically significantly different between the normal and affected marrows: parameters based on co-occurrence matrix are ranked highest in their separability. The classification of subjects achieved an area under the receiver operating characteristic curve (AUC) of 0.914, and the classification of slices achieved an AUC of 0.780. The results show that MRI-texture-based classification can effectively classify subjects/slices with and without BML.||URI:||https://hdl.handle.net/10356/99716
|ISSN:||0730-725X||DOI:||http://dx.doi.org/10.1016/j.mri.2013.01.014||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCBE Journal Articles|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.