Segmentation of nucleus and cytoplasm of white blood cells using Gram-Schmidt orthogonalization and deformable models

S. H. Rezatofighi, R. A. Zoroofi, R. Sharifian, H. Soltanian-Zadeh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

Automatic recognition of white blood cells in hematological can be divided into four major parts: preprocessing, image segmentation, feature extraction and classification. Due to the multifarious nature of these cells and uncertainty in the hematological images, segmentation of white blood cells is one of the most important stages in this process. A scrupulous segmentation obviously reduces errors of next stages. In this paper, we introduce a novel method based on Gram-Schmidt process and parametric deformable models for segmenting the nucleus and cytoplasm. Also, we propose a new preprocessing method for improving the results of cytoplasm segmentation. Moreover, for finding the initial contour for parametric deformable model, an automatic scheme is defined. Experimental results show that our proposed method is capable of segmenting the white blood cells in the hematological images. To evaluate the proposed algorithm quantitatively, we compare its results with the manual segmentations by a hematologist. This study shows robustness of the proposed method. Another feature of the proposed method is that it is simple to implement.

Original languageEnglish
Title of host publication2008 9th International Conference on Signal Processing, ICSP 2008
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages801-805
Number of pages5
ISBN (Print)9781424421794
DOIs
Publication statusPublished - 8 Dec 2008
Externally publishedYes
EventInternational Conference on Signal Processing 2008 - Beijing, China
Duration: 26 Oct 200829 Oct 2008
Conference number: 9th

Conference

ConferenceInternational Conference on Signal Processing 2008
Abbreviated titleICSP 2008
CountryChina
CityBeijing
Period26/10/0829/10/08

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