A marker controlled watershed transform, which was used in the previous study is an efficient technique to segment the cells and split overlapping cells in the image. In order to analyze the cells in the images individually, the cells can be segmented using appropriate automated segmentation techniques, thereby avoiding the cumbersome and error-prone existing manual. The diagnosis of a patient’s pathological condition, through the study of peripheral blood smear images, is a highly complicated process, the results of which require high levels of precision. The results show the proposed method's capability in detection of erythrocytes in blood smear images. Accuracy and sensitivity of the proposed method are equal to 95.9% and 97.99%, respectively. This method creates 1300 sign in segmentation of 1274 erythrocytes available in 25 blood smear images. 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. Line segments have maximum and minimum gray level variations has a special pattern that is applicable for detections of the central regions of cells. For detecting this intensity variation structure, a line operator measuring gray level variations along several directional line segments is applied. Applying this transform, the gray intensity of cell images gradually reduces from the center of cells to their margins. This method uses gray scale structure of blood cell, which is obtained by exertion of Euclidian distance transform on binary images. 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. At the first step of this analysis, segmentation and detection of blood cells are inevitable. Most of the erythrocyte related diseases are detectable by hematology images analysis. Edge based and Thresholding techniquesĪre used usually with gray image of plant leaves and Region Based, Clustering and Watershed segmentation technique used With advantages and disadvantages of segmentation techniques in plant leaf analysis. Comparative analysis of different methods shown in table and concluded
Used in different application of image processing. This paper shows how different techniques of segmentation This paper discusses and reviews the various segmentation techniques like Edge Based, Threshold, Region Based,Ĭlustering and Watershed segmentation used in leaves analysis. Where every pixel in a region is comparative concerning some trademark or registered property, for example, color, intensity, The aftereffect of picture segmentation is an arrangement of areas that all things considered cover the whole picture, To rearrange and additionally change the representation of an image into something that is more significant and less demanding Segmentation is the process of dividing a digital image into number of parts of interest.
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