Khajehpour Hassan, Dehnavi Alireza Mehri, Taghizad Hossein, Khajehpour Esmat, Naeemabadi Mohammadreza
Department of Physics and Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Medical Informatics, School of Health Information Management, Tehran University of Medical Sciences, Tehran, Iran .
J Med Signals Sens. 2013 Jul;3(3):164-71.
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.
大多数红细胞相关疾病可通过血液学图像分析检测出来。在该分析的第一步，血细胞的分割和检测是必不可少的。在本研究中，提出了一种使用线算子和分水岭算法的新方法，用于在血液涂片图像中检测和分割红细胞，同时减少分水岭算法中的过度分割，这对于分割部分重叠的不同类型血细胞很有用。该方法利用血细胞的灰度结构，这是通过对二值图像施加欧几里得距离变换获得的。应用这种变换后，细胞图像的灰度强度从细胞中心向边缘逐渐降低。为了检测这种强度变化结构，应用了一种沿着几个方向线段测量灰度级变化的线算子。具有最大和最小灰度级变化的线段具有一种特殊模式，适用于检测细胞的中心区域。这些区域与通过分水岭算法计算局部最大值得到的标记的交集用于检测细胞中心，同时减少分水岭算法的过度分割。该方法在25张血液涂片图像中对1274个红细胞进行分割时创建了1300个标记。所提方法的准确率和灵敏度分别为95.9%和97.99%。结果表明了所提方法在血液涂片图像中检测红细胞的能力。
