White Blood Cell Segmentation by Color-Space-Based  K-Means Clustering

Zhang, Congcong; Xiao, Xiaoyan; Li, Xiaomei; Chen, Ying‐Jie; Wu, Zhen Sen; Chang, Jun; Zheng, Chengyun; Li, Zhi

doi:10.3390/s140916128

Cited by 114 publications

(74 citation statements)

References 16 publications

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“…In the experiments, the nuclei were determined by segmenting the images using K‐means segmentation algorithm. K‐means is chosen due to its common usage . The number of clusters for the K‐means to segment nuclei was chosen to be 5, since fewer clusters were resulted in undersegmentation and more clusters also were caused oversegmentation.…”

Section: Resultsmentioning

confidence: 99%

A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

Seri̇n

Erturkler

2019

Int J Imaging Syst Tech

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The cell is the smallest unit of living beings, which has structural and functional properties. Almost all cell behaviors are regulated by various intracellular reactions initiated by the signaling. The signaling and the distance between cells influence each other. Thus, cell-location-based modeling and analyzing of histopathological tissues provide important information to the expert. In literature, methods such as distance-based threshold, K-Nearest Neighbor, Voronoi graphs, Delaunay triangulation, and colored graph have been used. However, circular neighborhood relationships of cells have not been considered by any CAD system so far despite of its crucial impact. Thus, we developed the circular neighborhood cell-graph. Histopathological images of liver were classified by using features extracted from T-Distance, K-Nearest Neighbor, Voronoi, Delaunay, and the proposed cell-graph. Then, the classification performances of the methods were compared. Experimental results show that liver tissue images can be classified with accuracy of 95.7% by using the features provided by the proposed cellgraph model. K E Y W O R D Scell graph, histopathological image analyzing, nuclei, proximity graph

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Section: Resultsmentioning

confidence: 99%

A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

Seri̇n

Erturkler

2019

Int J Imaging Syst Tech

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“…A shadowed C-means [13] based segmentation algorithm is proposed in [14]. A colorspace-based k-means clustering method to segment leukocyte from blood smear image have been proposed in [15].…”

Section: Introductionmentioning

confidence: 99%

Robust leukocyte segmentation in blood microscopic images based on intuitionistic fuzzy divergence

Danyali

Helfroush

Moshavash

2015

2015 22nd Iranian Conference on Biomedical Engineering (ICBME)

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Image processing-based analysis of microscopic leukocyte helps in early detection of many diseases. It is a challenging issue to segment leukocytes under uneven imaging conditions since features of microscopic leukocyte images change in different labratories. This paper introduces an automatic robust method to segment leukocyte from blood microscopic images using intuitionistic fuzzy divergence based thresholding. The method is constructed based on three simple assumptions which is common between blood microscopic images. To evaluate the robustness of proposed method, it has been tested on three dataset. Experimental results demonstrate that proposed approach effectively segment leukocytes from various type of blood microscopic images.

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“…Analyzing blood smeared images is challenging but it is a very crucial task. WBC count is amazingly useful to diagnose diseases, to know the effectiveness of therapy for cancer patient and most of all, it plays an important role in body immune system to fight any infections [1] [2]. Most blood disease can be detected by calculating the number of WBC in blood smeared image.…”

Section: Introductionmentioning

confidence: 99%

“…Segmentation is responsible to prune out the object of interest in the image which in this case, is WBC region and it contributes to the accuracy of WBC detection stage. Segmentation method can be divided into several categories which are threshold based, learning based, active contour based, metaheuristic based and saliency based [2]. However, threshold based method is reported to be the best and reliable to segment the uniform image and fast.…”

Section: Introductionmentioning

confidence: 99%

White blood cell counting analysis of blood smear images using various segmentation strategies

Safuan

Tomari

Zakaria

et al. 2017

AIP Conference Proceedings

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Abstract. In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several color analysis subtraction which are RGB, CMYK and HSV, and Otsu thresholding. Noises and unwanted regions that present after the segmentation process is eliminated by applying a combination of morphological and Connected Component Labelling (CCL) filter. Eventually, Circle Hough Transform (CHT) method is applied to the segmented image to estimate the number of WBC including the one under the clump region. From the experiment, it is found that G-S yields the best performance.

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White Blood Cell Segmentation by Color-Space-Based K-Means Clustering

Cited by 114 publications

References 16 publications

A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

Robust leukocyte segmentation in blood microscopic images based on intuitionistic fuzzy divergence

White blood cell counting analysis of blood smear images using various segmentation strategies

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