Detection and segmentation of cervical cell cytoplast and nucleus

Lin, Chuen-Horng; Chan, Yung-Kuan; Chen, Chun‐Chieh

doi:10.1002/ima.20198

Cited by 47 publications

(30 citation statements)

References 17 publications

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“…Some previous researchers mostly also used grayscale images [8] - [14]. However, single-canal signal produces less information than color canal information.…”

Section: Resultsmentioning

confidence: 99%

Color canals modification with canny edge detection and morphological reconstruction for cell nucleus segmentation and area measurement in normal Pap smear images

Riana

Dewi

Widyantoro

et al. 2014

AIP Conference Proceedings

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This paper presents a cell nucleus segmentation and area measurement of Pap smear images by means of modification of color canals with Canny edge detection and morphological reconstruction methods. Cell nucleus characterization plays an important role for classifying the degree of abnormality in cervical cancer. The aim of this work is to find the matched measurement method with the manual nucleus area measurement. In this work, we utilized pap smear single cell images from Herlev data bank in RGB mode. The cell images were selected from 90 normal class subjects that include: Normal Superficial, Normal Intermediate, and Normal Columnar classes. The nucleus of each cell image was cropped manually to localize from the cytoplasm. The color canals modification was performed on each cropped nucleus image by, first, separating each R, G, B, and grayscale canals, then implementing addition operation based on color canals (R+G+B, R+G, R+B, G+B, and grayscale). The Canny edge detection was applied on those modifications resulting in binary edge images. The nucleus segmentation was implemented on the edge images by performing region filling based on morphological reconstruction. The area property was calculated based on the segmented nucleus area. The nucleus area from the proposed method was verified to the existing manual measurement (ground truth) of the Herlev data bank. Based on thorough observation upon the selected color canals and Canny edge detection. It can be concluded that Canny edge detection with R+G+B canal is the most significant for all Normal classes (r 0,305, p-value 0.05). While for Normal Superficial and Normal Intermediate, Canny edge detection is significant for all RGB modifications with (r 0.414 -0.817 range, , p-value 0.05), and for Normal Columnar, Canny edge detection is significant for R+B canal (r 0.505, p-value 0.05).

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“…Some previous researchers mostly also used grayscale images [8] - [14]. However, single-canal signal produces less information than color canal information.…”

Section: Resultsmentioning

confidence: 99%

Color canals modification with canny edge detection and morphological reconstruction for cell nucleus segmentation and area measurement in normal Pap smear images

Riana

Dewi

Widyantoro

et al. 2014

AIP Conference Proceedings

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“…The proposed detector enhances the gradients of the edges of the cytoplast and nucleus while suppressing the noise gradients, and then specifies the pixels with higher gradients as possible edge pixels. The proposed algorithm is then compared with seed region growing feature extraction and level set method using 10 cervix smear images, to end quality of segmentation result is also compared [17]. Plissiti, M. E., et al submits an automated method for cell nuclei detection.…”

Section: ) Detailed Review Of Considered Publicationsmentioning

confidence: 99%

Segmentation of Cervical Cells for Automated Screening of Cervical Cancer: A Review

2017

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Abstract--In automatic screening of cervical cytology, the morphological features of cell play a determining role. To avoid false diagnosis,exigency to precisely extraction these features led to emergence of new segmentation models. In this paper author aspire to present literature review of research done in the field of segmentation stage in automatic screening of cervical smear images. Total of 35 publications are considered for the time period of 10 years. A detailed study of segmentation technique proposed in each publication is considered, which presents a chronological development and upgradation of segmentation models. This review assist researcher to have thorough knowledge of various state-of-art segmentation models and the problems and complexities required to be tackled, for unambiguous determination of malignancies in cervical cytology.

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“…doi:10.1016/j.patrec.2011.01.008 nucleus. Several image processing methods are proposed in this scope, such as active contours (Bamford and Lovell, 1998), template fitting (Wu et al, 1998) and edge detectors (Lin et al, 2009;Tsai et al, 2008;Yang-Mao et al, 2008). These methods exhibit remarkable performance in the segmentation of the structural parts of the cell.…”

Section: Introductionmentioning

confidence: 99%

Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images

Plissiti

Nikou

Charchanti

2011

Pattern Recognition Letters

129

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a b s t r a c tIn this work, we present an automated method for the detection and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The detection of the candidate nuclei areas is based on a morphological image reconstruction process and the segmentation of the nuclei boundaries is accomplished with the application of the watershed transform in the morphological color gradient image, using the nuclei markers extracted in the detection step. For the elimination of false positive findings, salient features characterizing the shape, the texture and the image intensity are extracted from the candidate nuclei regions and a classification step is performed to determine the true nuclei. We have examined the performance of two unsupervised (K-means, spectral clustering) and a supervised (Support Vector Machines, SVM) classification technique, employing discriminative features which were selected with a feature selection scheme based on the minimal-Redundancy-Maximal-Relevance criterion. The proposed method was evaluated on a data set of 90 Pap smear images containing 10,248 recognized cell nuclei. Comparisons with the segmentation results of a gradient vector flow deformable (GVF) model and a region based active contour model (ACM) are performed, which indicate that the proposed method produces more accurate nuclei boundaries that are closer to the ground truth.

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Detection and segmentation of cervical cell cytoplast and nucleus

Cited by 47 publications

References 17 publications

Color canals modification with canny edge detection and morphological reconstruction for cell nucleus segmentation and area measurement in normal Pap smear images

Color canals modification with canny edge detection and morphological reconstruction for cell nucleus segmentation and area measurement in normal Pap smear images

Segmentation of Cervical Cells for Automated Screening of Cervical Cancer: A Review

Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images

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