Abstract
Brain imaging plays a vital role toward the identification of diseases such as seizures, lesions, sclerosis, and the other inhomogeneities. Methodologies for effective and efficient regulation of these diseases are to be planned so as to overcome the issues of mortality. This chapter highlights the contributions using beta mixture models in this direction. The experimentation is carried out in a MATLAB environment and the results are tabulated based on BRAINWEB images. The results are also compared with those of the existing models based on GMM using the performance evaluation parameters such as average difference, maximum distance, and image fidelity. The results showcase that the proposed methodology overcomes the GMM in all respects and it gives good recognization accuracy. The developed model can also be used for identifying the other diseases of the brain.
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Anuradha, S., Satyanarayana, C.H. (2017). Medical Image Segmentation Based on Beta Mixture Distribution for Effective Identification of Lesions. In: Patnaik, S., Popentiu-Vladicescu, F. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 555. Springer, Singapore. https://doi.org/10.1007/978-981-10-3779-5_17
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DOI: https://doi.org/10.1007/978-981-10-3779-5_17
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