A novel color image segmentation method and its application to white blood cell image analysis

Wu, Jianhua; Zeng, Pingping; Zhou, Yuan; Olivier, Christian

doi:10.1109/icosp.2006.345700

Cited by 83 publications

(46 citation statements)

References 5 publications

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“…This feature shows that \IMAGE AND" operation can be applied on the two binary images to segment the cytoplasm region of leukocyte, and leukocyte nucleus region can be obtained by the method proposed in Ref. 18. The \IMAGE XOR" operation is conducted between the cytoplasm region binary image and the nucleus region binary image to get a binary image.…”

Section: 15mentioning

confidence: 99%

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A Method of Leukocyte Segmentation Based on S Component and B Component Images

Yang

Cao

Shi

2014

J. Innov. Opt. Health Sci.

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A leukocyte segmentation method based on S component and B component images is proposed. Threshold segmentation operation is applied to get two binary images in S component and B component images. The samples used in this study are peripheral blood smears. It is easy tō nd from the two binary images that gray values are the same at every corresponding pixels in the leukocyte cytoplasm region, but opposite in the other regions. The feature shows that \IMAGE AND" operation can be employed on the two binary images to segment the cytoplasm region of leukocyte. By doing \IMAGE XOR" operation between cytoplasm region and nucleus region, the leukocyte segmentation can be retrieved e®ectively. The segmentation accuracy is evaluated by comparing the segmentation result of the proposed method with the manual segmentation by a hematologist. Experiment results show that the proposed method is of a higher segmentation accuracy and it also performs well when leukocytes overlap with erythrocytes. The average segmentation accuracy of the proposed method reaches 97.7% for segmenting¯ve types of leukocyte. Good segmentation results provide an important foundation for leukocytes automatic recognition.

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Section: 15mentioning

confidence: 99%

“…Leukocyte nucleus region can be obtained by the method proposed in Ref. 18, as is shown in Fig. 2(e), the centroids belonging to the leukocyte nucleus region should be calculated.…”

Section: Segmentation Of Leukocytes By Fusing the S Component And B Cmentioning

confidence: 99%

A Method of Leukocyte Segmentation Based on S Component and B Component Images

Yang

Cao

Shi

2014

J. Innov. Opt. Health Sci.

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“…Those methods can be broadly classified as edge-based [15], region-based [16], threshold-based [17], and watershed-based [18,19] segmentation schemes. Wu et al [15] stated that cell boundaries are not sharp enough to perform edge-based segmentation in leukocyte images. An improved seeded region growing algorithm for cell segmentation was presented by Mehnert and Jackway [20].…”

Section: Literature Surveymentioning

confidence: 99%

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Mohapatra

Patra

Kumar

2012

ISRN Artificial Intelligence

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The segmentation of leukocytes and their components acts as the foundation for all automated image-based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented images is plentiful, suitable segmentation routines need to be developed for better disease recognition. Clustering is an essential image segmentation procedure which segments an image into desired regions. A judicious integration of rough sets and fuzzy sets is suitably employed towards leukocyte segmentation in a clustering framework. In this study, the goodness of fuzzy sets and rough sets is suitably integrated to achieve improved segmentation performance. The membership concept of fuzzy sets endow is efficient handling of overlapping partitions, and the rough sets provide a reasonable solution to deal with uncertainty, vagueness, and incompleteness in data. Such synergistic combination gives the proposed scheme an edge over standard cluster-based segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means algorithm is robust in segmenting stained blood microscopic images. The accomplished segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which leads to automated leukemia detection.

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“…In [11], an imaging system based upon acousto-optic tunable filter has been proposed to detect the WBCs. However, the existing methods [2][3][4][5][6][7][8], [11] have limited accuracy in the detection of overlapping cells. In [12], a circle detection algorithm has been used to count red blood cells (RBCs) and WBCs.…”

Section: Introductionmentioning

confidence: 99%

“…Iterative Otsu thresholding detects staining spots along-with WBC [3]. Contrast enhancement (using intensity stretching techniques) and segmentation (based on hue, saturation, intensity model) improve the visibility and decompose the leukemia images into blast and nucleus parts [4].…”

Section: Introductionmentioning

confidence: 99%