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A Hybrid Maximum Likelihood Estimation Approach for non-local medical image denoising and tumour detection using Cuckoo-based Neuro Fuzzy Classifiers
| Content Provider | Semantic Scholar |
|---|---|
| Author | Velayudham, A. Kumar, K. Madhan |
| Copyright Year | 2014 |
| Abstract | Medical images are usually corrupted by several noises starting right from the acquiring process complicating the automatic feature extraction and analysis of clinical data. To obtain the best possible diagnosis it is vital that medical images be clear, sharp, and free of noise and artifacts. In this research paper, we propose a hybrid method to denoise, detect and classify the tumour part from CT medical images. Our proposed approach consists of four phases, such as denoising, region segmentation, feature extraction and classification. In the denoising phase Maximum Likelihood estimation method is being used for removing noise. We have taken into consideration two noises, Gaussian and salt & pepper for the proposed technique. The performance of the proposed technique is assessed on the five CT images for the parameters, PSNR and SDME. In the segmentation process K-means clustering technique is employed. For the feature extraction, the parameters contrast, energy and gain are extracted. In classification, a modified technique called Cuckoo-Neuro Fuzzy (CNF) algorithm is developed and applied for detection of the tumour region. Then, classification is done based on the fuzzy rules generated. The proposed denoising technique have shown better values for the SDME of 69.8798 and PSNR of 25.1008 for salt & pepper noise which is very superior compared to existing methods. Moreover it has shown an accuracy of 96.9%. The obtained results have been found to be better than the existing methods. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ajbasweb.com/old/ajbas/2014/December/335-349.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
