Optimal Segmentation Framework for Detection of Brain Anomalies
Автор: Nageswara Reddy P, C.P.V.N.J.Mohan Rao, Ch.Satyanarayana
Журнал: International Journal of Engineering and Manufacturing(IJEM) @ijem
Статья в выпуске: 6 vol.6, 2016 года.
Бесплатный доступ
This work presents an enhancement in accuracy for brain disorder detection using optimal unification. The strategy for detection of segments and brain regions causing medical conditions are described. This work demonstrates the application of multilateral filter and applied watershed method with EM-GM method. The most popular existing techniques of brain tumor detection are not optimal compared to this combination of Watershed and EM-GM technique with the proposed optimal unification technique. The result is optimally unified and achieved high accuracy. The multilateral filter enhances the image edges for better segmentation using signal amplitude moderation of the pixel. In the unification process, the optimal sets of segments are divided and finest merged results are considered with the brain regions detected with anomalies. Henceforth the number of possible medical investigations will be reduced.
Brain MR Images, T1 Images, HMA, Watershed Method, EM-GM Method, Multilateral Filter, Optimal Unification
Короткий адрес: https://sciup.org/15014419
IDR: 15014419
Список литературы Optimal Segmentation Framework for Detection of Brain Anomalies
- N. Porz, "Multi-modalodal glioblastoma segmentation: Man versus machine", PLOS ONE, vol. 9, pp. e96873, 2014.
- S. Bauer, R. Wiest, L.-P. Nolte and M. Reyes, "A survey of MRI-based medical image analysis for brain tumor studies", Phys. Med. Biol., vol. 58, no. 13, pp. R97-R129, 2013.
- D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr and A. W. Toga, "Online resource for validation of brain segmentation methods", Neuroimage, vol. 45, no. 2, pp. 431-439, 2009.
- M. Stille, M. Kleine ; J. Hagele ; J. Barkhausen ; T. M. Buzug, "Augmented Likelihood Image Reconstruction", IEEE Transactions on Medical Imaging, Volume:35, Issue:1.
- A. Gooya, G. Biros and C. Davatzikos, "Deformable registration of glioma images using EM algorithm and diffusion reaction modeling", IEEE Trans. Med. Imag., vol. 30, no. 2, pp. 375-390, 2011.
- L. Weizman, "Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI", Med. Image Anal., vol. 16, no. 1, pp. 177-188, 2012.
- K. Hameeteman, "Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading", Med. Image Anal., vol. 15, no. 4, pp. 477-488, 2011.
- S. Ahmed, K. M. Iftekharuddin and A. Vossough, "Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI", IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 2, pp. 206-213, 2015.
- R. Achanta et al., "SLIC superpixels compared to state-of-the-art su- perpixel methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274–2282, Nov. 2012.
- B. B. Avants et al., "A reproducible evaluation of ANTs similarity metric performance in brain image registration," Neuroimage, vol. 54, no. 3, pp. 2033–44, Feb. 2011.
- N. Subbanna, D. Precup, L. Collins, and T. Arbel, "Hierarchical prob- abilistic Gabor and MRF segmentation of brain tumours in MRI vol- umes," Proc. MICCAI, vol. 8149, pp. 751–758, 2013.
- H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, "Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1930–1943, Aug. 2013.
- A. Islam, S. M. S. Reza, and K. M. Iftekharuddin, "Multi-fractal texture estimation for detection and segmentation of brain tumors," IEEE Trans. Biomed. Eng., vol. 60, no. 11, pp. 3204–3215, Nov. 2013.
