Abstract
The present work aims to improve accuracy in computer aided diagnosis on brain tumor detection. To improve the segmentation process, a novel wavelet based fusion technique to combine two different segmentation techniques-absorbing Markov chain (AMC) and k-means clustering, is proposed here. The efficacy of the presented segmentation algorithm is demonstrated on the BRATS-2012 dataset containing ground truth images of MRI brain tumor images of T2 modality. A dice coefficient of ~78% on an average for the images is achieved against ~67% and ~72.9% dice values in the existing works. The present work also uses the support vector machine to classify the brain tumor as benign or malignant.
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Kothari, T., Pramanik, A., Maity, S.P. (2019). Brain Tumor Detection by Wavelet Based Fusion Technique. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_26
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DOI: https://doi.org/10.1007/978-981-13-8578-0_26
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