Muhammad Aksam Iftikhar

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Visual object tracking (VOT) is an important subfield of computer vision. It has widespread application domains, and has been considered as an important part of surveillance and security system. VOA facilitates finding the position of target in image coordinates of video frames.While doing this, VOA also faces many challenges such as noise, clutter,(More)
The histopathological examination of tissue specimens is necessary for the diagnosis and grading of colon cancer. However, the process is subjective and leads to significant inter/intra observer variation in diagnosis as it mainly relies on the visual assessment of histopathologists. Therefore, a reliable computer-aided technique, which can automatically(More)
In recent years, classification of colon biopsy images has become an active research area. Traditionally, colon cancer is diagnosed using microscopic analysis. However, the process is subjective and leads to considerable inter/intra observer variation. Therefore, reliable computer-aided colon cancer detection techniques are in high demand. In this paper, we(More)
Texture is a combination of repeated patterns with regular/irregular frequency. It can only be visualized but hard to describe in words. Liver structure exhibit similar behavior, it has maximum disparity in intensity texture inside and along boundary which serves as a major problem in its segmentation and classification. Problem gets more complicated when(More)
Colon cancer diagnosis based on microscopic analysis of biopsy sample is a common medical practice. However, the process is subjective, biased and leads to interobserver variability. Further, histopathologists have to analyze many biopsy samples per day. Therefore, factors such as tiredness, experience and workload of histopathologists also affect the(More)
Denoizing of magnetic resonance (MR) brain images has been focus of numerous studies in the past. The performance of subsequent stages of image processing, in automated image analysis, is substantially improved by explicit consideration of noise. Nonlocal means (NLM) is a popular denoizing method which exploits usual redundancy present in an image to(More)
Image de-noising is an essential intermediate step in several medical applications related to brain MRI. The noise present in brain MRI degrades the performance of computer-aided analysis of these images. Therefore, the noise should be removed prior to subsequent processing. Non-local means (NLM) is a classical de-noising algorithm, which has been(More)
Image denoising is an integral component of many practical medical systems. Non-local means (NLM) is an effective method for image denoising which exploits the inherent structural redundancy present in images. Improved adaptive non-local means (IANLM) is an improved variant of classical NLM based on a robust threshold criterion. In this paper, we have(More)
In this paper, a robust method is proposed for segmentation of medical images by exploiting the concept of information gain. Medical images contain inherent noise due to imaging equipment, operating environment and patient movement during image acquisition. A robust medical image segmentation technique is thus inevitable for accurate results in subsequent(More)
Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed(More)