Blood cells classification by image color and intensity features clustering

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dc.citation.conferenceLitteris et Artibus
dc.contributor.affiliationLviv Polytechnic National Universityuk_UA
dc.contributor.authorMelnyk, R. A.
dc.contributor.authorDubytskyi, A. O.
dc.coverage.countryUAuk_UA
dc.coverage.placenameLvivuk_UA
dc.date.accessioned2018-03-01T14:37:05Z
dc.date.available2018-03-01T14:37:05Z
dc.date.issued2015
dc.description.abstractA new approach for cells detection and classification on blood smear images is considered. Benefit of 4-connected over 8-connected component labeling for cell detection is shown. Color and intensity histogram clustering are proposed to extract common features for cells classification. A new approach for k-means initial centroids detection proposed. The algorithms effectiveness was tested and estimated for some blood smear images. The algorithm examples, figures and result table to illustrate the approach are presented.uk_UA
dc.format.pages46-49
dc.identifier.citationMelnyk R. A. Blood cells classification by image color and intensity features clustering / R. A. Melnyk, A. O. Dubytskyi // Litteris et Artibus : proceedings of the 5th International youth science forum, November 26–28, 2015, Lviv, Ukraine / Lviv Polytechnic National University. – Lviv : Lviv Polytechnic Publishing House, 2015. – P. 46–49. – Bibliography: 7 titles.uk_UA
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/39493
dc.language.isoenuk_UA
dc.publisherLviv Polytechnic Publishing Houseuk_UA
dc.relation.referencesen[1] C. Hc sliding windows: Object localization by efficient subwindow search”, CVPR, 2008. [2] Pham, Dzung L.; Xu, Chenyang; Prince, Jerry L., "Current Methods in Medical Image Segmentation". Annual Review of Biomedical Engineering 2: 315– 337, 2000. [3] Luigi Di Stefano, Andrea Bulgarelli, “A Simple and Efficient Connected Components Labeling Algorithm,” ICIAP, 10th International Conference on Image Analysis and Processing, pp.322, 1999. [4] N. Otsu, ‘‘A threshold selection method from gray level histograms,’’ IEEE Trans. Syst. Man Cybern. SMC-9, 62–66, 1979. [5] MacKay, David, "Chapter 20. An Example Inference Task: Clustering". Information Theory, Inference and Learning Algorithms. Cambridge University Press. pp. 284–292. ISBN 0-521-64298-1. MR 2012999, 2003 [6] Orchard M, Bouman C, “Color quantization of images”. IEEE Trans Signal Process 39(12):2677- 2690, 1991. [7] P. Maslak, “Normal peripheral blood smear - 1.” http://imagebank.hematology.org/AssetDetail.aspx?A ssetID=3666&AssetType=Asset, September 2008.uk_UA
dc.subjectcomputer visionuk_UA
dc.subjectvisual object detectionuk_UA
dc.subjectvisual object classificationuk_UA
dc.subjectbinarizationuk_UA
dc.subjectconnected component labelinguk_UA
dc.subjectintensity featureuk_UA
dc.subjectcolor featureuk_UA
dc.subjectcluster analysisuk_UA
dc.titleBlood cells classification by image color and intensity features clusteringuk_UA
dc.typeConference Abstractuk_UA

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Litteris et Artibus. – 2015 р.