International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 8, No. 11, 8 Nov. 2016
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An Automatic Segmentation of Brain Tumor from MRI Scans through Wavelet Transformations
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Author(s)
Kalaiselvi T
1,*
P. Nagaraja
1
Index Terms
Clustering, K-means, Segmentation, Tumor, Wavelet
Abstract
Fully automatic brain tumor detection is one of the critical tasks in medical image processing. The proposed study discusses the tumor segmentation process by means of wavelet transformation and clustering technique. Initially, MRI brain images are preprocessed by various wavelet transformations to sharpen the images and enhance the tumor region. This helps to quicken the clustering technique since tumor region appears good in sharpened CSF region. Finally, a wavelet decomposition method is applied in CSF region and extracts the tumor portion. This proposed method analyzes the performance of various wavelet types such as Haar, Daubechies (db1, db2, db3, db4 and db5), Coiflet, Morlet and Symlet in MRI scans. Experiments with the proposed method were done on 5 volume datasets collected from the popular brain tumor pools are BRATS2012 and whole brain atlas. The quantitative measures of results were compared using the metrics false alarm (FA) and missed alarm (MA). The results demonstrate that the proposed method obtaining better performance in the terms of both quantity and visual appearance.
Cite This Paper
Kalaiselvi T, Nagaraja P,"An Automatic Segmentation of Brain Tumor from MRI Scans through Wavelet Transformations", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.11, pp.59-65, 2016. DOI: 10.5815/ijigsp.2016.11.08
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