Abstract
This article introduces an approximate rehabilitation of unidentifiable edge in medical images by settling demarcation line based on experimental evidence, by using the essence of classical discrepancy theoretic approach. The main intriguing aspect of this method is distributing the quality points over the ambiguous area by the virtue of jittered sampling method for validating the points in a deterministic way, after finding the decisive point. Before this textual enhancement on unidentifiable edge, a known image classification model is introduced by using wavelet transformation to classify the brain MRI in three categories as normal, benign, and malignant. Here, we are taking timestamp one snapshot of an image, not to consider the aliasing and anti-aliasing effect. We are concentrated on the edges those are not detected even after applying edge detection method.
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Hudait, A., Pandey, N., Vashistha, L., Das, M.N., Sen, A. (2018). A New Approach Using Discrepancy Theory for MR Image Segmentation. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-10-6872-0_14
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DOI: https://doi.org/10.1007/978-981-10-6872-0_14
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