7,617 research outputs found
A Parallel Implementation of the Thresholding Problem by Using Tissue-Like P Systems
In this paper we present a parallel algorithm to solve the thresholding problem by using Membrane Computing techniques. This bio-inspired algorithm has been implemented in a novel device architecture called CUDATM, (Compute Unified Device Architecture). We present some examples, compare the obtained time and present some research lines for the future.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN-2009-13192Junta de Andalucía P08-TIC-04200Ministerio de Educación y Ciencia MTM2009-12716Junta de Andalucía PO6-TIC-02268Universidad del País Vasco EHU09/0
Designing Tissue-like P Systems for Image Segmentation on Parallel Architectures
Problems associated with the treatment of digital images have several interesting features from a bio-inspired point of view. One of them is that they can be
suitable for parallel processing, since the same sequential algorithm is usually applied in
different regions of the image. In this paper we report a work-in-progress of a hardware
implementation in Field Programmable Gate Arrays (FPGAs) of a family of tissue-like
P systems which solves the segmentation problem in digital images.Ministerio de Ciencia e Innovación TIN-2009-13192Junta de Andalucía P08-TIC-04200Junta de Andalucía PO6-TIC-02268Ministerio de Educación y Ciencia MTM2009-1271
Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images
Cell segmentation in microscopy is a challenging problem, since cells are
often asymmetric and densely packed. This becomes particularly challenging for
extremely large images, since manual intervention and processing time can make
segmentation intractable. In this paper, we present an efficient and highly
parallel formulation for symmetric three-dimensional (3D) contour evolution
that extends previous work on fast two-dimensional active contours. We provide
a formulation for optimization on 3D images, as well as a strategy for
accelerating computation on consumer graphics hardware. The proposed software
takes advantage of Monte-Carlo sampling schemes in order to speed up
convergence and reduce thread divergence. Experimental results show that this
method provides superior performance for large 2D and 3D cell segmentation
tasks when compared to existing methods on large 3D brain images
Image Segmentation Inspired by Cellular Models using hardware programming
Several features of image segmentation make it suitable for bio–inspired techniques. It can be parallelized, locally solved and the input data can be easily encoded using representations inspired by nature. In this paper, we present a new hardware system that follows the Membrane Computing approach, and performs edge–based segmentation, noise removal and thresholding of digital images
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.
In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists
Segmenting images with gradient-based edge detection using Membrane Computing
In this paper, we present a parallel implementation of a new algorithm for segmenting images with
gradient-based edge detection by using techniques from Natural Computing. This bio-inspired parallel
algorithm has been implemented in a novel device architecture called CUDA™(Compute Unified Device
Architecture). The implementation has been designed via tissue P systems on the framework of
Membrane Computing. Some examples and experimental results are also presented.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08–TIC-04200Junta de Andalucía P06-TIC-02268Ministerio de Educación y Ciencia MTM2009-12716Universidad del Pais Vasco EHU09/0
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