Abstract
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 propose two intelligent segmentation methods, fuzzy c-mean and Geodesic Active Contours of Caselles level set method to do the MR image segmentation jobs so as to find the effect they yield. The results show those intelligent methods both do a pretty job than other common image segmentation algorithm.
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Chen, YS., Chen, SH., Chang, CC. (2010). Comparison of Multispectral Image Processing Techniques to Brain MR Image Classification between Fuzzy C-Mean Method and Geodesic Active Contours of Caselles Level Set Method. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16693-8_50
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DOI: https://doi.org/10.1007/978-3-642-16693-8_50
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