Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake

Abstract-The automatic interpretation of Pap Smear image is one of challenging issues in some aspects. Accurate segmentation for each cell is an important procedure that must be done so that no information is lost during the evaluation process. However, the presence of overlapping cells in Pap Smear image make the automated analysis of these cytology images become more difficult. In most of the studies, cytoplasm segmentation is the difficult stage because the boundaries between cells are very thin. In this study, we propose an algorithm that can segment the overlapping cytoplasm. First, the morphology operation and global thresholding to segment cytoplasm is done. Second, the overlapping area on cytoplasm region is separated using morphological operation and distance criteria on each pixel. The proposed method has been evaluated against the results of manual tracing by experts. The experiment results show that the proposed method can segment the overlapping cytoplasm as similar as experts do, i.e., 2.897 ± 3.632 (mean ± std) using Hausdorff distance.

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“…After we get the marker image (M), Morphological Opening is applied to the marker image (M) using Equation 8.…”

Section: (7)mentioning

confidence: 99%

“…Cytoplasm is the cell membrane outside the nucleus. The studied conducted by Yang-Mao et al [7] and Li et al [8] had successfully performed segmentation of nuclei and cytoplasm area using Gradient Vector Flow (GVF) snake. However, the study only uses a single cell as an input to be analyzed.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation

Kurniawan

Muhimmah

Kurniawardhani

et al. 2017

CommIT (Communication and Information Technology) Journal

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“…Genctav et al [1] Li et al [2] proposed both nucleus and Cytoplasm segmentation in cervical smear images using Radiating GVF Snake. After preprocessing, the regions in the image are roughly clustered into nucleus, background and the cytoplasm by a spatial K-means clustering algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Various Techniques for Classification and Segmentation of Cervical Cell Images - A Review

Sharma¹,

Mangat²

2016

IJCA

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Pap smear test plays an important role for the early diagnosis of cervical cancer in which human cells taken from the cervix of patient are analysed for pre-cancerous changes. The manual analysis of these cells by expert cytologist is labor intensive and time consuming job. The automatic and accurate detection of cervical cells are two critical preprocessing steps for automatic Pap smear image analysis and also for diagnosis of pre-cancerous changes in the uterine cervix. Similarly, the reliable segmentation of abnormal nuclei in cervical cytology is of utmost importance in automation-assisted screening techniques. This paper presents, the existing automated methods for the detection, segmentation and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The majority of cytoplasm segmentation uses K-means algorithm, edge detection method, thresholding approach, graph cut and active contours technique. Most of existing work is done on images of isolated cells, especially for those which are in the Herlev data set. For segmentation of images which contains multiple cells, level set and thresholding techniques have been used. The nucleus segmentation varies as: single-nucleus segmentation, touching-nuclei splitting and multiple-nuclei segmentation. However, many segmentation methods incorporates shape priors, usually enforcing elliptical shapes in order to overcome cell occlusion and noise. The main focus of this paper is comprehensive literature survey of various existing classification and segmentation techniques. The shortcomings and failures of the existing work are also provided for further enhancement and improvement of overall performance and accuracy.

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“…This method is based on the fuzzy C-means clustering and mathematical morphology. Segmentation method based on deformable model, [11][12][13] and other methods.…”

Section: Introductionmentioning

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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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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Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake

Cited by 139 publications

References 17 publications

Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation

Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation

Various Techniques for Classification and Segmentation of Cervical Cell Images - A Review

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

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