Comparison of Multispectral Image Processing Techniques to Brain MR Image Classification
Brain Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water,proteinaceous fluid,soft tissue and other tissues with a variety of contrast.The abundance fractions of tissue signatures provided by multispectral images can be very useful for medical diagnosis compared to other modalities.Multiple Sclerosis (MS) is thought to be a disease in which the patient immune system damages the isolating layer of myelin around the nerve fibers.This,nerve damage is visible in Magnetic Resonance (MR) scans of the brain.Manual segmentation is extremely time-consuming and tedious.Therefore,fully automated MS detection methods are being developed which can classify large amounts of MR data,and do not suffer from inter observer variability.In this paper we use standard fuzzy c-means algorithm (FCM) for multi-spectral images to segment patient MRI data.Geodesic Active Contours of Caselles level set is another method we implement to do the brain image segmentation jobs.And then we implement anther modified Fuzzy C-Means algorithm,where we call Bias-Corrected FCM as BCFCM,for bias field estimation for the same thing.Experimental results show the success of all these intelligent techniques for brain medical image segmentation.
Magnetic Resonance Imaging Fuzzy C-means Algorithm Caselles Level Set Method Bias-Corrected FCM Optimization
Yen-Sheng Chen Shao-Hsien Chen Jeih-Jang Liou
Department of Creative Product and Technological Application,Lan Yang Institute of Technology,No.79, Department of Mechanical Engineering,National Chin-Yi University of Technology,No.35,Lane 215,Sec.1, Department of Product and Media Design,Fo Guang University No.160,Linwei Rd.,Jiaosi,Yilan County 262
国际会议
沈阳
英文
1998-2002
2012-09-07（万方平台首次上网日期，不代表论文的发表时间）