Kother Mohideen

Learn More
Breast cancer continues to be a significant public health problem in the world. The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms. Dense region in(More)
Breast cancer is the most leading cause of death in women nowadays. Screening mammography is currently the best available radiological technique for early detection of breast cancer. The detection of breast cancer is disturbed due to the existence of artifacts which reduce the rate of accuracy. For this reason, the pre-processing of mammogram images is very(More)
We propose an algorithm for mixed noise reduction in Hyperspectral Imagery (HSI). The hyperspectral data cube is considered as a three order tensor. These tensors give a clear view about both spatial and spectral modes. The HSI provides ample spectral information to identify and distinguish spectrally unique materials, thus they are spectrally over(More)
—In this paper we deal with mixed noise reduction algorithm for Hyperspectral imagery (HSI). The hyperspectral data cube is considered as a three order tensor that is able to jointly treat both spatial and spectral modes. This entire denoising process is based on the K-SVD denoising algorithm. Our work involved in minimizing model to remove mixed noise such(More)
This paper proposes an approach for enhancement and de-noising of the images having fine edges and homogeneous smooth regions by using singular value decomposition filtering technique on the diffused image subspaces. The existing singular value decomposition based image de-noising technique faces the problem of selecting the optimum threshold parameter for
  • 1