Author/Authors :
Khajehpour، Hassan نويسنده Department of Physics and Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran , , Mehri Dehnavi، Alireza نويسنده Department of Medical Physics and Engineering, School of Medicine , , Taghizad، Hossein نويسنده Department of Physics and Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran , , Khajehpour، Esmat نويسنده Department of Medical Informatics, School of Health Information Management, Tehran University of Medical Sciences, Tehran, Iran , , Naeemabadi، Mohammadreza نويسنده Department of Physics and Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran ,
Abstract :
Most of the erythrocyte related diseases are detectable by hematology images analysis. At the first step of this analysis, segmentation
and detection of blood cells are inevitable. In this study, a novel method using a line operator and watershed algorithm is rendered
for erythrocyte detection and segmentation in blood smear images, as well as reducing over?segmentation in watershed algorithm
that is useful for segmentation of different types of blood cells having partial overlap. This method uses gray scale structure of blood
cell, which is obtained by exertion of Euclidian distance transform on binary images. Applying this transform, the gray intensity of
cell images gradually reduces from the center of cells to their margins. For detecting this intensity variation structure, a line operator
measuring gray level variations along several directional line segments is applied. Line segments have maximum and minimum gray
level variations has a special pattern that is applicable for detections of the central regions of cells. Intersection of these regions with
the signs which are obtained by calculating of local maxima in the watershed algorithm was applied for cells’ centers detection, as
well as a reduction in over?segmentation of watershed algorithm. This method creates 1300 sign in segmentation of 1274 erythrocytes
available in 25 blood smear images. Accuracy and sensitivity of the proposed method are equal to 95.9% and 97.99%, respectively.
The results show the proposed method’s capability in detection of erythrocytes in blood smear images.
