Blood cells classification by image color and intensity features clustering
Електронний науковий архів Науково-технічної бібліотеки Національного університету "Львівська політехніка"
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Blood cells classification by image color and intensity features clustering
|
|
| Creator |
Melnyk, R. A.
Dubytskyi, A. O. |
|
| Contributor |
Lviv Polytechnic National University
|
|
| Subject |
computer vision
visual object detection visual object classification binarization connected component labeling intensity feature color feature cluster analysis |
|
| Description |
A 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. |
|
| Date |
2018-03-01T14:37:05Z
2018-03-01T14:37:05Z 2015 |
|
| Type |
Conference Abstract
|
|
| Identifier |
Melnyk 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.
http://ena.lp.edu.ua:8080/handle/ntb/39493 |
|
| Language |
en
|
|
| Relation |
[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.
|
|
| Format |
46-49
application/pdf |
|
| Coverage |
UA
Lviv |
|
| Publisher |
Lviv Polytechnic Publishing House
|
|