Share Email Print
cover

Proceedings Paper

New decision support tool for acute lymphoblastic leukemia classification
Author(s): Monica Madhukar; Sos Agaian; Anthony T. Chronopoulos
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. The developed system includes different methods to accurately measure furthermore cell properties in microscope blood film images. The blood images are exposed to series of pre-processing steps which include color correlation, and contrast enhancement. By performing K-means clustering on the resultant images, the nuclei of the cells under consideration are obtained. Shape features and texture features are then extracted for classification. The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images.

Paper Details

Date Published: 2 February 2012
PDF: 12 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829518 (2 February 2012); doi: 10.1117/12.905969
Show Author Affiliations
Monica Madhukar, The Univ. of Texas at San Antonio (United States)
Sos Agaian, The Univ. of Texas at San Antonio (United States)
Anthony T. Chronopoulos, The Univ. of Texas at San Antonio (United States)


Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

© SPIE. Terms of Use
Back to Top