文章基本信息

标题：A Novel technique for the Detection of Mixed Noise in Medical Images using Datamining
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作者：Er. Renuka Gujral ; Er. Nishi
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2015
卷号：8
期号：11
页码：231-242
DOI：10.14257/ijsip.2015.8.11.21
出版社：SERSC
摘要：In Digital image processing; many researches have been done on image denoising so far. Nowadays, the noise detection from an image is the most challenging task. Though, the various algorithms introduced for the detection of noise type from a noisy image, but these algorithms work only for detection of single type of noise. To overcome the limitation of the previous built algorithms, we investigate the data mining technique called Support Vector Machine. The SVM is a powerful supervised learning method which is to be used for the detection of mixed noise models. Broadly, this technique detects the different types of noise from a mixed noise image; noise can be either single or mixed type of noise. The different parameters have combined to describe the properties of these different noise models so as to perform the detection. The detecting algorithm has been achieved by applying the SVM on the training dataset of different medical images and further extensive tests are performed on the test dataset for detection of each noise type model. This detection technique clearly outperforms various techniques with the high accuracy of results for different proposed noise models
关键词：Detection of noise type; Mixed Noise Models; Datamining; Support Vector ; Machine; Training dataset; Test dataset; Multiclass SVM