A Sugeno And Tsukamoto Fuzzy Inference System For Denoising Medical Images

Research Article
Sindhana Devi M and Soranamagesweri M
Fuzzy inference system, Sugeno type, Medical image denoising, Tsukamoto type fuzzy inference system, Neuro fuzzy system, Neuro fuzzy filters.

All digital images contain some degree of noise due to the corruption in its acquisition and transmission by various effects. Particularly, medical image are likely disturbed by a complex type of addition noise depending on the devices which are used to capture or store them. The presence of noise in the medical image reduces the visual quality that complicates diagnosis and treatment. In this paper Tsukamoto type fuzzy inference system for noise filtering in medical images is proposed. The proposed system consist of two fuzzy filters and a post processor The proposed method is suitable for various types of noisy images and it is highly preferred by the medical expert: The proposed method performance is evaluated in terms of MSE (Mean Squared Error) and PSNR (Peak Signal to Noise Ratio). The results demonstrate its usefulness for noise reduction in medical imaging. Thus the proposed method clearly outperforms the previous approaches of medical image denoising in terms of quantitative performance measures as well as in terms of visual quality of the images.