S
Sos S. Agaian
Researcher at City University of New York
Publications - 582
Citations - 10193
Sos S. Agaian is an academic researcher from City University of New York. The author has contributed to research in topics: Image processing & Computer science. The author has an hindex of 38, co-authored 532 publications receiving 8216 citations. Previous affiliations of Sos S. Agaian include College of Staten Island & University of Texas System.
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Patent
Systems and methods for automated screening and prognosis of cancer from whole-slide biopsy images
TL;DR: In this paper, a complete scheme for automated quantitative analysis and assessment of human and animal tissue images of several types of cancers is presented, which includes a variety of sub-systems, which could be used separately or in conjunction to automatically grade cancerous regions.
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(n, k, p)-Gray code for image systems
TL;DR: Computer simulations demonstrate that the new parametric Gray code shows better performance than other traditional Gray codes for these applications in image systems, namely, image bit-plane decomposition, image denoising, and encryption.
Patent
Systems and methods for quantitative analysis of histopathology images using multiclassifier ensemble schemes
TL;DR: In this paper, a multi-stage detection and classification of cancer regions from digitized images of biopsy slides is described, including the use of quaternions, logarithmic mappings of color channels, and application of wavelets to log-rithmic color channel mappings.
Journal ArticleDOI
Non-Linear Direct Multi-Scale Image Enhancement Based on the Luminance and Contrast Masking Characteristics of the Human Visual System
TL;DR: A multi-scale image enhancement algorithm based on a new parametric contrast measure that incorporates not only the luminance masksing characteristic, but also the contrast masking characteristic of the human visual system is presented.
Journal ArticleDOI
An improved image processing technique for asphalt concrete X-ray CT images
TL;DR: In this article, a comparative evaluation of two image segmentation techniques for processing asphalt concrete microstructure images obtained with X-ray computed tomography (CT) is presented, which are the adaptive enhancement-based thresholding algorithm (AETA) and the watershed segmentation embedded into the volumetric-based VTA.