European Journal of Molecular & Clinical Medicine

As digital cameras become more enhanced and smaller, CCD sensors can relate to only one color of a pixel. This color mosaic pattern is called as Bayer Pattern (BP) which requires processing to obtain a color image with a higher resolution. Each image pixel that undergoes interpolation has a full color spectrum based on surrounding pixel colors. Here we introduce Adaptive CFA(ACFA) interpolation model. For normal image regions hue technique is used while edge regions adapt the new technique. It is proposed to apply fuzzy logic and fuzzy rule which is based on Genetic Algorithm that uses random local search to enhance the PSNR. Medical image reconstruction by this proposed fuzzy based method outperforms the other medical image reconstruction methods.

Multi-dimensional image processing techniques are more helpful for medical image diagnosis process. In this research work brain MRI or CT scan images are collecting for input and attain the preprocessed image. Depending on scalability and integration on medical images we get multi model diagnosed image. The 2 scale multi dimensional sparse representation is applied on selected image for acquiring the original and de-noise image. This method improves the performance metrics such as accuracy 98.45%, recall 98.98% and throughput 97.58% have been improved