The Experts below are selected from a list of 28140 Experts worldwide ranked by ideXlab platform

Rastislav Lukac - One of the best experts on this subject based on the ideXlab platform.

  • Vector edge operators for cDNA Microarray spot localization.
    Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2007
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis
    Abstract:

    This paper introduces a vector-based framework for edge detection and spot localization in cDNA Microarray data. Since cDNA Microarray images can be viewed as vector fields, both their spectral and spatial characteristics should be used to determine edges, discontinuities and structural elements. Building upon the powerful nature of nonlinear operators, the proposed vector edge operators can effectively localize Microarray spots outperforming the commonly used scalar edge detectors. Moreover, due to the utilization of the principle of robust statistics, vector edge detectors are relatively immune to the noise present in Microarray images. Simulation studies reported in this paper indicate that the proposed framework yields excellent performance and it can be readily incorporated in the cDNA Microarray processing pipeline.

  • cDNA Microarray imaging using single sensor technology
    Canadian Conference on Electrical and Computer Engineering, 2006
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis
    Abstract:

    A new solution for cDNA Microarray image acquisition and processing is introduced. The proposed solution uses a two-color filter array placed on top of a single image sensor to capture the two-channel cDNA Microarray data as a single monochromatic image, thus reducing twofold the memory and storage space. The vectorial nature of the cDNA Microarray data is restored using the proposed image processing framework which also denoises and enhances the acquired image.

  • CCECE - cDNA Microarray Imaging using Single-Sensor Technology
    2006 Canadian Conference on Electrical and Computer Engineering, 2006
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis
    Abstract:

    A new solution for cDNA Microarray image acquisition and processing is introduced. The proposed solution uses a two-color filter array placed on top of a single image sensor to capture the two-channel cDNA Microarray data as a single monochromatic image, thus reducing twofold the memory and storage space. The vectorial nature of the cDNA Microarray data is restored using the proposed image processing framework which also denoises and enhances the acquired image.

  • cDNA Microarray image segmentation using root signals
    International Journal of Imaging Systems and Technology, 2006
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis
    Abstract:

    A vector processing based framework suitable for cDNA Microarray image segmentation is introduced and analyzed in this paper. By using nonlinear, generalized selection vector filters the framework proposed here classifies the cDNA image data as either Microarray spots or image background. The solution converges to a root signal that represents the segmented cDNA Microarray image with the regular spots ideally separated from the background and with their coloration uniquely described by dominant color vectors. It will be demonstrated that the framework readily unifies image denoising, enhancement, data normalization, irregular spot rejection, and spot segmentation in one processing step delivering excellent performance at reasonable computational cost. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 51–64, 2006

  • cDNA Microarray image processing using fuzzy vector filtering framework
    Fuzzy Sets and Systems, 2005
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis, Bogdan Smolka, A N Venetsanopoulos
    Abstract:

    This paper presents a novel filtering framework capable of processing cDNA Microarray images. The proposed two-component adaptive vector filters integrate well-known concepts from the areas of fuzzy set theory, nonlinear filtering, multidimensional scaling and robust order-statistics. By appropriately setting the weighting coefficients in a generalized framework, the method is capable of removing noise impairments while preserving structural information in cDNA Microarray images. Noise removal is performed by tuning a membership function which utilizes distance criteria applied to cDNA vectorial inputs at each image location. The classical vector representation, adopted here for a two-channel processing task, as well as a new color-ratio model representation are used. Simulation studies reported in this paper indicate that the proposed adaptive fuzzy vector filters are computationally attractive, yield excellent performance and are able to preserve structural information while efficiently suppressing noise in cDNA Microarray data.

Erwin P. Bottinger - One of the best experts on this subject based on the ideXlab platform.

Konstantinos N Plataniotis - One of the best experts on this subject based on the ideXlab platform.

  • Vector edge operators for cDNA Microarray spot localization.
    Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2007
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis
    Abstract:

    This paper introduces a vector-based framework for edge detection and spot localization in cDNA Microarray data. Since cDNA Microarray images can be viewed as vector fields, both their spectral and spatial characteristics should be used to determine edges, discontinuities and structural elements. Building upon the powerful nature of nonlinear operators, the proposed vector edge operators can effectively localize Microarray spots outperforming the commonly used scalar edge detectors. Moreover, due to the utilization of the principle of robust statistics, vector edge detectors are relatively immune to the noise present in Microarray images. Simulation studies reported in this paper indicate that the proposed framework yields excellent performance and it can be readily incorporated in the cDNA Microarray processing pipeline.

  • cDNA Microarray imaging using single sensor technology
    Canadian Conference on Electrical and Computer Engineering, 2006
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis
    Abstract:

    A new solution for cDNA Microarray image acquisition and processing is introduced. The proposed solution uses a two-color filter array placed on top of a single image sensor to capture the two-channel cDNA Microarray data as a single monochromatic image, thus reducing twofold the memory and storage space. The vectorial nature of the cDNA Microarray data is restored using the proposed image processing framework which also denoises and enhances the acquired image.

  • CCECE - cDNA Microarray Imaging using Single-Sensor Technology
    2006 Canadian Conference on Electrical and Computer Engineering, 2006
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis
    Abstract:

    A new solution for cDNA Microarray image acquisition and processing is introduced. The proposed solution uses a two-color filter array placed on top of a single image sensor to capture the two-channel cDNA Microarray data as a single monochromatic image, thus reducing twofold the memory and storage space. The vectorial nature of the cDNA Microarray data is restored using the proposed image processing framework which also denoises and enhances the acquired image.

  • cDNA Microarray image segmentation using root signals
    International Journal of Imaging Systems and Technology, 2006
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis
    Abstract:

    A vector processing based framework suitable for cDNA Microarray image segmentation is introduced and analyzed in this paper. By using nonlinear, generalized selection vector filters the framework proposed here classifies the cDNA image data as either Microarray spots or image background. The solution converges to a root signal that represents the segmented cDNA Microarray image with the regular spots ideally separated from the background and with their coloration uniquely described by dominant color vectors. It will be demonstrated that the framework readily unifies image denoising, enhancement, data normalization, irregular spot rejection, and spot segmentation in one processing step delivering excellent performance at reasonable computational cost. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 51–64, 2006

  • cDNA Microarray image processing using fuzzy vector filtering framework
    Fuzzy Sets and Systems, 2005
    Co-Authors: Rastislav Lukac, Konstantinos N Plataniotis, Bogdan Smolka, A N Venetsanopoulos
    Abstract:

    This paper presents a novel filtering framework capable of processing cDNA Microarray images. The proposed two-component adaptive vector filters integrate well-known concepts from the areas of fuzzy set theory, nonlinear filtering, multidimensional scaling and robust order-statistics. By appropriately setting the weighting coefficients in a generalized framework, the method is capable of removing noise impairments while preserving structural information in cDNA Microarray images. Noise removal is performed by tuning a membership function which utilizes distance criteria applied to cDNA vectorial inputs at each image location. The classical vector representation, adopted here for a two-channel processing task, as well as a new color-ratio model representation are used. Simulation studies reported in this paper indicate that the proposed adaptive fuzzy vector filters are computationally attractive, yield excellent performance and are able to preserve structural information while efficiently suppressing noise in cDNA Microarray data.

Yaw-ching Yang - One of the best experts on this subject based on the ideXlab platform.

Aldo Massimi - One of the best experts on this subject based on the ideXlab platform.