Leukocyte Segmentation in Blood Smear Images Using Region-Based Active Contours

Abstract

Abstract. In this paper, we propose a segmentation method for an automated differential counter using image analysis. The segmentation here is to extract leukocytes (white blood cells) and separate its constituents, nucleus and cytoplasm, in blood smear images. For this purpose, a regionbased active contour model is used where region information is estimated using a statistical analysis. The role of the regional statistics is mainly to attract evolving contours toward the boundaries of leukocytes, avoiding problems with initialization. And contour deformation near to the boundaries is constrained by an additional regularizer. The active contour model is implemented using a level set method and validated with a leukocyte image database.

Selection of the best features for leukocytes classification in blood smear microscopic images

“…Therefore, this step is the most important challenge in many works in the literatures and improvement of cell segmentation has been the most common effort in many researches. Many blood smear image segmentation methods have been proposed in the area of general segmentation of WBCs which are generally based on edge and border detection, region growing, filtering, mathematical morphology, and watershed clustering [5][6][7][8][9][10][11][12][13][14]. Despite many beneficial explorations have been carried out in WBCs segmentations, majority of them have some defects such as complexity of arithmetic, difficulty to ensure parameters and Ojai, e ' s ' so on.…”

Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing

“…[15] Thereafter, some active contour approaches for WBC segmentation have been explored. [161718] Segmentation of leukocyte using contour propagation with distance map guiding was introduced by Srijad et al . [19] Their results show that active contour guided by distance mapping from a neighboring area, is able to extract nucleus and cytoplasm regions.…”

Overlapping White Blood Cell Segmentation And Counting On Microscopic Blood Cell Images

“…Combined binary morphology and fuzzy c-means algorithms for WBC image segmentation are used in [7]. Active contour approaches for WBC image segmentation reported in [1,8]. Recently, there are researches on segmentation of microscopic cell image using color and texture feature approach [9] and combination of the fuzzy morphology and binary morphology for nucleus and cytoplasm segmentation is proposed in [2,10].…”

“…The fault diagnosis affects medical treatment to be performed. The counting of different classes of whiteblood-cell (WBC) is one of the most frequently performed blood tests and it plays an important role in the diagnosis of diseases such as anemia, leukemia, and HIV [1]. Manual differential counting by an expert is imprecise, difficult to reproduce, and subjective.…”