Cataloged from PDF version of article.Computer-based imaging systems are becoming important tools for quantitative assessment
of peripheral blood and bone marrow samples to help experts diagnose blood disorders
such as acute leukemia. These systems generally initiate a segmentation stage
where white blood cells are separated from the background and other nonsalient objects.
As the success of such imaging systems mainly depends on the accuracy of this stage,
studies attach great importance for developing accurate segmentation algorithms.
Although previous studies give promising results for segmentation of sparsely distributed
normal white blood cells, only a few of them focus on segmenting touching and overlapping
cell clusters, which is usually the case when leukemic cells are present. In this article,
we present a new algorithm for segmentation of both normal and leukemic cells in
peripheral blood and bone marrow images. In this algorithm, we propose to model color
and shape characteristics of white blood cells by defining two transformations and introduce
an efficient use of these transformations in a marker-controlled watershed algorithm.
Particularly, these domain specific characteristics are used to identify markers and
define the marking function of the watershed algorithm as well as to eliminate false white
blood cells in a postprocessing step. Working on 650 white blood cells in peripheral
blood and bone marrow images, our experiments reveal that the proposed algorithm
improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. (C) 2014 International Society for Advancement of Cytometr
