Abstract
In Medical Diagnosis, Magnetic Resonance Images play a significant role. This work presents a hybrid technique to detect the abnormal parts in the magnetic resonance images of a human. The proposed technique consists of five stages namely, Noise reduction, Smoothing, Feature extraction, Dimensionality reduction or Feature reduction, and Classification. First stage obtains the noise reduced MR image using KSL (Kernel-Sobel-Low pass) Filter. In the second stage, smoothing is done by histogram equalization. Third stage obtains the features related with MRI images using discrete wavelet transformation. In the fourth stage, the features of magnetic resonance images have been reduced using principles component analysis to the more essential features. At last stage, the classifier based on K-means has been used to classify subjects as normal or abnormal MRI human images. Classification accuracy of 98.80% has been obtained by the proposed algorithm. The result shows that the proposed technique is robust and effective compared with other recent works.
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Lakshmi Devasena, C., Hemalatha, M. (2011). Hybrid Image Classification Technique to Detect Abnormal Parts in MRI Images. In: Das, V.V., Thankachan, N. (eds) Computational Intelligence and Information Technology. CIIT 2011. Communications in Computer and Information Science, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25734-6_31
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DOI: https://doi.org/10.1007/978-3-642-25734-6_31
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