Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.

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With the growth of globalisation, security has been a major concern in digital exchange of medical information. Data hiding, also referred as watermarking, was introduced to authenticate medical data which is sent over the network. Watermarking of electronic patient reports (EPR) for telemedicine demands robust, imperceptible, high payload techniques. Blood vessel extraction is mainly used for diabetic retinopathy (DR) for automatic extraction and classification of severity of diseases. In our current work, blood vessel from fundus images is extracted using K-means segmentation. Afterwards, EPR is hidden using interpolation and trigonometric functions in fundus image. Finally, blood vessel is extracted from the watermarked image to measure the changes in accuracy of the proposed system. The percentage difference (< 0.25%) of accuracy in fundus images before watermarking and after watermarking claims the retention of devalorisation of vessel extraction accuracy measurement. High peak signal to noise ratio (PSNR) value (> 36) and high correlation (> 87%) between original and watermarked retinal image, establishes the robustness of the proposed non-blind watermarking method.Keywords: blood vessel extraction; non-blind watermarking; electronic patient report; EPR; K-means; peak signal to noise ratio; PSNR. Effect of trigonometric functions-based watermarking on blood vessel extraction 91Reference to this paper should be made as follows: Dey, N., Ahmed, S.S., Chakraborty, S., Maji, P., Das, A. and Chaudhuri, S.S. (2017) 'Effect of trigonometric functions-based watermarking on blood vessel extraction: an application in ophthalmology imaging', Int.

With the growth of globalisation, security has been a major concern in digital exchange of medical information. Data hiding, also referred as watermarking, was introduced to authenticate medical data which is sent over the network. Watermarking of electronic patient reports (EPR) for telemedicine demands robust, imperceptible, high payload techniques. Blood vessel extraction is mainly used for diabetic retinopathy (DR) for automatic extraction and classification of severity of diseases. In our current work, blood vessel from fundus images is extracted using K-means segmentation. Afterwards, EPR is hidden using interpolation and trigonometric functions in fundus image. Finally, blood vessel is extracted from the watermarked image to measure the changes in accuracy of the proposed system. The percentage difference (< 0.25%) of accuracy in fundus images before watermarking and after watermarking claims the retention of devalorisation of vessel extraction accuracy measurement. High peak signal to noise ratio (PSNR) value (> 36) and high correlation (> 87%) between original and watermarked retinal image, establishes the robustness of the proposed non-blind watermarking method.Keywords: blood vessel extraction; non-blind watermarking; electronic patient report; EPR; K-means; peak signal to noise ratio; PSNR. Effect of trigonometric functions-based watermarking on blood vessel extraction 91Reference to this paper should be made as follows: Dey, N., Ahmed, S.S., Chakraborty, S., Maji, P., Das, A. and Chaudhuri, S.S. (2017) 'Effect of trigonometric functions-based watermarking on blood vessel extraction: an application in ophthalmology imaging', Int.