title: 3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach.
abstract: This study investigates a fast distribution-matching, data-driven algorithm for 3D multimodal MRI brain glioma tumor and edema segmentation in different modalities. We learn non-parametric model distributions which characterize the normal regions in the current data. Then, we state our segmentation problems as the optimization of several cost functions of the same form, each containing two terms: (i) a distribution matching prior, which evaluates a global similarity between distributions, and (ii) a smoothness prior to avoid the occurrence of small, isolated regions in the solution. Obtained following recent bound-relaxation results, the optima of the cost functions yield the complement of the tumor region or edema region in nearly real-time. Based on global rather than pixel wise information, the proposed algorithm does not require an external learning from a large, manually-segmented training set, as is the case of the existing methods. Therefore, the ensuing results are independent of the choice of a training set. Quantitative evaluations over the publicly available training and testing data set from the MICCAI multimodal brain tumor segmentation challenge (BraTS 2012) demonstrated that our algorithm yields a highly competitive performance for complete edema and tumor segmentation, among nine existing competing methods, with an interesting computing execution time (less than 0.5s per image).
authors.affiliation: Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Tunisia. Electronic address: inesnjeh28@yahoo.fr.
authors.forename: Ines
authors.initials: I
authors.lastname: Njeh
authors.affiliation: Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Tunisia.
authors.forename: Lamia
authors.initials: L
authors.lastname: Sallemi
authors.affiliation: GE Healthcare, London, ON, Canada.
authors.forename: Ismail Ben
authors.initials: IB
authors.lastname: Ayed
authors.affiliation: Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Tunisia; Department of Nuclear Medicine, CHU Habib Bourguiba, Sfax, Tunisia.
authors.forename: Khalil
authors.initials: K
authors.lastname: Chtourou
authors.affiliation: Neuroimaging Research Center: CENIRICM, Pitie Salpetriere Hospital, Paris, France; Department of Neuroradiology, Pitie Salpetriere, Paris, France.
authors.forename: Stephane
authors.initials: S
authors.lastname: Lehericy
authors.affiliation: Neuroimaging Research Center: CENIRICM, Pitie Salpetriere Hospital, Paris, France; Department of Neuroradiology, Pitie Salpetriere, Paris, France.
authors.forename: Damien
authors.initials: D
authors.lastname: Galanaud
authors.affiliation: Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Tunisia.
authors.forename: Ahmed Ben
authors.initials: AB
authors.lastname: Hamida
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000150:complications / Q000473:pathology*; D004487:Edema / Q000209:etiology / Q000473:pathology*; D005910:Glioma / Q000150:complications / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D012680:Sensitivity and Specificity; D016008:Statistical Distributions; D013382:Subtraction Technique
keywords: BraTS2012; Brain tumor; Edema; Graph cut distribution matching; MRI; Segmentation