title: Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field.
abstract: White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice.
authors.affiliation: Department of Computing and Information Systems, The University of Melbourne, Australia. Electronic address: royp@student.unimelb.edu.au.
authors.forename: Pallab Kanti
authors.initials: PK
authors.lastname: Roy
authors.affiliation: Department of Computing and Information Systems, The University of Melbourne, Australia.
authors.forename: Alauddin
authors.initials: A
authors.lastname: Bhuiyan
authors.affiliation: NCRIS Facility Fellow Image Informt, Centre for Advanced Imaging, The University of Queensland, Australia.
authors.forename: Andrew
authors.initials: A
authors.lastname: Janke
authors.affiliation: Department of Radiology, The University of Melbourne, Australia.
authors.forename: Patricia M
authors.initials: PM
authors.lastname: Desmond
authors.affiliation: Department of Ophthalmology, National University of Singapore, Singapore.
authors.forename: Tien Yin
authors.initials: TY
authors.lastname: Wong
authors.affiliation: College of Medicine, Biology and Environment, Australian National University, Canberra, Australia.
authors.forename: Walter P
authors.initials: WP
authors.lastname: Abhayaratna
authors.affiliation: Central Clinical School (Alfred Hospital Campus), Monash University, Melbourne, Australia.
authors.forename: Elsdon
authors.initials: E
authors.lastname: Storey
authors.affiliation: Department of Computing and Information Systems, The University of Melbourne, Australia. Electronic address: kotagiri@unimelb.edu.au.
authors.forename: Kotagiri
authors.initials: K
authors.lastname: Ramamohanarao
mesh_terms: D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D003287:Contrast Media; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D056784:Leukoencephalopathies / Q000473:pathology*; D000069550:Machine Learning; D008390:Markov Chains; D015233:Models, Statistical; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D066127:White Matter / Q000473:pathology*
chemical_list: D003287:Contrast Media
keywords: Cerebrovascular diseases; Magnetic resonance imaging (MRI); Markov Random Field (MRF); Random forest (RF); White matter lesions (WMLs)