Abstract
The medical imaging technology plays a crucial role in visualization and analysis of the human body with unprecedented accuracy and resolution. Analyzing the multimodal for disease-specific information across patients can reveal important similarities between patients, hence their underlying diseases and potential treatments. Classification of MR brain images as normal or abnormal with information about the level at which it lies is a very important task for further processing, which is helpful for the diagnosis of diseases. This paper focuses on the abnormality detection of brain MR images using search and retrieval technique performed on similar anatomical structure images. Similar anatomical structure images are retrieved using Modified Local Binary Pattern (MOD-LBP) features of the query and target images and the level of the image is identified. The query image is compare with images in the same level and classification is done using the SVM classifier. The result reveals that the classification accuracy is improved significantly when the query image is compared with similar anatomical structure images.
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References
Ramana Reddy, B.V., Radhika Mani, M., Subbaiah, K.V.: Texture Classification Method using Wavelet Transforms Based on Gaussian Markov Random Field. International Journal of Signal and Image Processing 1(1) (2010)
Prakash, I.S.K.N., Satya Prasad, K.: M-Band Dual Tree Complex Wavelet Transform For Texture Image Indexing and Retrieval. IJAET (July 2012), ISSN: 2231–1963
Ruchika, M.S., Singh, A.R.: Compression of Medical Images Using Wavelet Transforms. (IJSCE) 2(2), May 2012, ISSN: 2231–2307
Varghese, A., Balakrishnan, K., Varghese, R.R., Paul, J.S.: Content Based Image Retrieval of Brain MR Images across different classes. World Academy of Science, Engineering and Technology 80 (2013)
Müller, H., Müller, W.: Automated Benchmarking in Content-Based Image Retrieval, work supported by Swiss National Foundation for Scientific Research (grant no. 2000–052426.97)
Unay, D., Ekin, A., Jasinsch, R.: Local Structure-Based Region-Of- Interest Retrieval in brain MR Images. IEEE Transactions on Information technology in Biomedicine 14(4), July 2010
Bashier, H.K., Abdu Abusham, E., Khalid, F.: Face detection based on graph structure and neural networks. Trends in Applied Sciences Research 7(8), 683–691 (2012), ISSN 1819 3579/DOI:10.3923/tasr.2012.683.691
Othman, M.F.B., Abdullah, N.B., Kamal, N.F.B.: MRI brain classification using support vector machine. In: 4th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2011 (2011)
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Varghese, A., Manesh, T., Balakrishnan, K., George, J.S. (2016). Level Based Anomaly Detection of Brain MR Images Using Modified Local Binary Pattern. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_42
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DOI: https://doi.org/10.1007/978-3-319-23036-8_42
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