title: Tumor detection from enhanced magnetic resonance imaging using fuzzy curvelet.
abstract: Effective medical image analysis is possible by the use of technique known as segmentation. Segmentation is a very challenging task because there is not any standard segmentation method is available for any medical application. In this article, we have proposed an automatic brain MR image segmentation method. Fast discrete curvelet transform and spatial fuzzy C-mean algorithm is used for noise removal and segmentation of brain MR image. Fuzzy entropy has been used for calculating adaptive and optimal threshold to separate out the image segments. Our proposed system is exclusively based on the information contained by the image itself. No extra information and no human intervention are required in our proposed system. We have tested our proposed system on different T1, T2 and PD brain MR images.
authors.affiliation: Signal and Image Processing Laboratory, School of Information and Mechatronics, Gwangju Institute of science and Technology, South Korea.
authors.forename: M Arfan
authors.initials: MA
authors.lastname: Jaffar
authors.forename: Quratul
authors.initials: Q
authors.lastname: Ain
authors.forename: Tae Sun
authors.initials: TS
authors.lastname: Choi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*