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Articles by Mohammad A.U. Khan
Total Records ( 3 ) for Mohammad A.U. Khan
  Md. Hasanul Kabir , M. Abdullah-Al-Wadud , Mohammad A.U. Khan , Abdur Rashid and Saghir Ahmad
  In this study, we present an image contrast enhancement method based on block-wise intensity pair distribution. The proposed algorithm takes the local intensity-pair distribution, instead of using the intensity-pair distribution of the whole image. To deal with the issues of contrast stretch and over-enhancement, a linear magnitude mapping function is used as a substitute to a non-linear one. This linear mapping preserves the relative contrast enhancement ratio between the gray levels. The local information from blocks facilitates contrast enhancement, enhances subtle edge information and eliminates noises from the image. The proposed algorithm is suitable for detail analysis of image features.
  Mohammad A.U. Khan , Phan Tran Ho Truc , Rabya Bahadur and Sara Javed
  In this study, we propose a novel framework for vessel enhancement in angiography images. The proposed approach utilize the image directional information to estimate the Hessian eigenvalues with less noise sensitivity and thus can correctly reveal more small, thin vessels. Also, the directional image decomposition helps to avoid junction suppression due to which, yields vessel tree are more continuous. Qualitative and quantitative evaluations show that the proposed filter generates better performance in comparison with conventional Hessian-based approaches.
  Mohammad A.U. Khan , M. Khalid Khan , M. Aurangzeb Khan and M. Talal Ibrahim
  A corneal endothelial cell image provides vast amount of information about a human eye. The cell density and cell shape parameters of a given endothelial cell image help ophthalmologists in making many vital clinical decisions. The acquired endothelial image is poor in contrast where cell boundaries are masked in the background. Previously, most of the work was based on morphological operations in spatial domain. However, if we think of cell structure as texture hidden in noisy background, we can get help from wavelet-texture segmentation, a well studied area. In this study, we propose a non-subsampled Wavelet pyramid decomposition of lowpass region. At certain level of the pyramid, we start observing cleaner cell boundary structure which greatly facilitates its segmentation. Once segmented automatic cell counting can be used and simulation results have shown improvement in cell density count.
 
 
 
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