Title :
Genetic algorithm for parameter optimization of image segmentation algorithm
Author_Institution :
John von Neumann Fac. of Inf., Obuda Univ., Budapest, Hungary
Abstract :
In the current practice of medicine, histopathological examinations are some of the most important tools for clinical diagnoses of a large group of diseases. To help pathologists and to reduce the subjectivity level, it has been proposed that computer-aided procedures be used to provide objective results. The first step of these procedures is the segmentation of the tissue image. In our research, we try to detect nuclei, glands and surface epithelium in Haematoxylin and Eosin (HE) stained colon tissue samples. This paper focuses on the identification of epithelial cell nuclei.
Keywords :
biological tissues; cellular biophysics; genetic algorithms; image segmentation; medical image processing; object detection; clinical diagnoses; computer-aided procedures; diseases; epithelial cell nuclei identification; genetic algorithm; glands detection; haematoxylin and Eosin stained colon tissue; histopathological examinations; image segmentation algorithm; medicine; nuclei detection; parameter optimization; pathologists; subjectivity level; surface epithelium detection; tissue image segmentation; Accuracy; Colon; Genetic algorithms; Glands; Image segmentation; Informatics; Optimization; GPGPU; colon tissue; epithelium detection; genetic algorithm; medical image segmentation;
Conference_Titel :
Computational Intelligence and Informatics (CINTI), 2013 IEEE 14th International Symposium on
Conference_Location :
Budapest
Print_ISBN :
978-1-4799-0194-4
DOI :
10.1109/CINTI.2013.6705220