Segmentation of White Blood Cell Nucleus Using Active Contour

Marzuki, Nurhanis Izzati Che; Mahmood, Nasrul Humaimi; Razak, Mohd Azhar Abdul

doi:10.11113/jt.v74.4675

Cited by 14 publications

(11 citation statements)

References 13 publications

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“…In 2015, Marzukia et al [9] proposed a system to segment the nuclei of WBCs based on the active contour technique. The RGB image is converted to a gray image and then the active contour is applied to the resulted image to get the segmented nucleus.…”

Section: Literature Reviewmentioning

confidence: 99%

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Mohammed

Abdulla²

2020

UHD J SCI TECH

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Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Mohammed

Abdulla²

2020

UHD J SCI TECH

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“…Liu et al 13 proposed a segmentation method combined mean shift clustering, color space conversion, and nucleus mark watershed operation to segment leukocyte. Marzuki et al 14 proposed a leukocyte nucleus segmentation method by using active contour; the segmentation results are based on the parameter values obtained from segmentation process. Yang et al 15 proposed a method of leukocyte segmentation based on S component and B component image; the Otsu's histogram thresholding method 16 is used in the S component to segment the leukocyte nucleus (OS); however, when the image is dyed too lightly, it will result in over-segmentation or undersegmentation.…”

Section: Introductionmentioning

confidence: 99%

Leukocyte nucleus segmentation method based on enhancing the saliency of saturation component

Wang

Cao

2019

Journal of Algorithms & Computational Technology

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The accuracy of the leukocyte nucleus segmentation is an important preprocessing step in the leukocyte automatic analysis. However, different dyeing conditions or different illumination conditions may cause capturing different color leukocyte images in microscopic imaging system, which will result in the over-segmentation or under-segmentation of the leukocyte nucleus. A leukocyte nucleus segmentation method based on enhancing the saliency of the saturation component is proposed. While applying the set of calibration offset values DR, DG, and DB of the red (R), green (G), and blue (B) chrominance value on the blood smear microscopic images, it can enhance the saliency of the saturation component and the saliency of the leukocyte nucleus region increases the most obviously. The leukocyte nuclei are then segmented using Otsu's histogram thresholding method. The experimental results show that the proposed algorithm outperforms the related algorithms in segmentation accuracy, over-segmentation rate, error rate, and relative distance error. It improves the accuracy, robustness, and universality further.

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“…The active contour is used to segment the nucleus in the work presented in [11]. Watershed algorithm is used in [8,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Feature extraction means to transfer the input data into different set of features. In [11], the roundness value of 0.8 is chosen to describe as a single leukocyte. While, the convex hull is used in [15].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of White Blood cells Segmentation using Otsu Threshold and Watershed Transformation

Salem

Sobhy

Dosoky

2016

JBEMi

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The aim of white blood cells (WBC) segmentation is to separate leukocytes from other different components in the blood peripheral image. In this paper, a method to segment white blood cells from microscopic images is proposed. The proposed method consists of three stages; Pre-processing, segmentation, and finally post-processing. In the pre-processing step; the color correction is used to enhance the image. In the segmentation step; two techniques have been used which are Otsu threshold and watershed marker-controlled followed by feature extraction. Shape features are used to differentiate between single and grouped cells. Artifacts are removed in the post-processing step. Experimental results show that the accuracy is 99.3% and 93.3% for the watershed based and the Otsu threshold based methods respectively. Experiments demonstrate that watershed marker-controlled outperforms Otsu threshold in the segmentation of WBC.

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Segmentation of White Blood Cell Nucleus Using Active Contour

Cited by 14 publications

References 13 publications

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Leukocyte nucleus segmentation method based on enhancing the saliency of saturation component

A Comparative Study of White Blood cells Segmentation using Otsu Threshold and Watershed Transformation

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