GPGPU-based data parallel region growing algorithm for cell nuclei detection

Nowadays microscopic analysis of tissue samples is done more and more by using digital imagery and special immunodiagnostic software. These are typically specific applications developed for one distinct field, but some subroutines are commonly repeated, for example several applications contain steps...

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Bibliographic Details
Published in:2011 IEEE 12th International Symposium on Computational Intelligence and Informatics pp. 493 - 499
Main Authors: Szenasi, S., Vamossy, Z., Kozlovszky, M.
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2011
Subjects:
Algorithm design and analysis
biomedical image processing
Computer architecture
CUDA
data parallel algorithm
GPGPU
Graphics processing unit
Instruction sets
Kernel
Microprocessors
nuclei detection
Synchronization
ISBN:1457700441, 9781457700446
Online Access:Get full text
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Summary:Nowadays microscopic analysis of tissue samples is done more and more by using digital imagery and special immunodiagnostic software. These are typically specific applications developed for one distinct field, but some subroutines are commonly repeated, for example several applications contain steps that can detect cell nuclei in a sample image. The aim of our research is developing a new data parallel algorithm that can be implemented even in a GPGPU environment and that is capable of counting hematoxylin eosin (HE) stained cell nuclei and of identifying their exact locations and sizes (using a variation of the region growing method). Our presentation contains the detailed description of the algorithm, the peculiarity of the CUDA implementation, and the evaluation of the created application (regarding its accuracy and the decrease in the execution time).
ISBN:1457700441
9781457700446
DOI:10.1109/CINTI.2011.6108556