A fluid dynamics-based deformable model for segmentation of cervical cell images

Cengizler, Caglar; Güven, Mustafa; Avcı, Mutlu

doi:10.1007/s11760-014-0719-3

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Previous studies show that, features extracted from both cytoplasmic region and nuclei are essential for detection of abnormality in an automated Pap smear screening system [2, 5, 7, 22]. We proposed methods for discrimination of overlapped nuclei which should be suggested as an elimination mechanism before feature extraction for abnormality detection [3].…”

Section: Discussionmentioning

confidence: 99%

Data cluster analysis-based classification of overlapping nuclei in Pap smear samples

Güven

Cengizler

2014

BioMedical Engineering OnLine

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BackgroundThe extraction of overlapping cell nuclei is a critical issue in automated diagnosis systems. Due to the similarities between overlapping and malignant nuclei, misclassification of the overlapped regions can affect the automated systems’ final decision. In this paper, we present a method for detecting overlapping cell nuclei in Pap smear samples.MethodJudgement about the presence of overlapping nuclei is performed in three steps using an unsupervised clustering approach: candidate nuclei regions are located and refined with morphological operations; key features are extracted; and candidate nuclei regions are clustered into two groups, overlapping or non-overlapping, A new combination of features containing two local minima-based and three shape-dependent features are extracted for determination of the presence or absence of overlapping. F1 score, precision, and recall values are used to evaluate the method’s classification performance.ResultsIn order to make evaluation, we compared the segmentation results of the proposed system with empirical contours. Experimental results indicate that applied morphological operations can locate most of the nuclei and produces accurate boundaries. Independent features significance test indicates that our feature combination is significant for overlapping nuclei. Comparisons of the classification results of a fuzzy clustering algorithm and a non-fuzzy clustering algorithm show that the fuzzy approach would be a more convenient mechanism for classification of overlapping.ConclusionThe main contribution of this study is the development of a decision mechanism for identifying overlapping nuclei to further improve the extraction process with respect to the segmentation of interregional borders, nuclei area, and radius. Experimental results showed that our unsupervised approach with proposed feature combination yields acceptable performance for detection of overlapping nuclei.

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Section: Discussionmentioning

confidence: 99%

Data cluster analysis-based classification of overlapping nuclei in Pap smear samples

Güven

Cengizler

2014

BioMedical Engineering OnLine

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“…Cengizler et al [3] proposed fluid dynamics based deformable model for the segmentation of cervical cell image.In this approach, simulation starts inside the cytoplasmic region and the simulate fluid is attracted toward the cell contours. The gradient magnitude of the image is considered as the source for extracting particles.…”

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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“…Yan et al proposed an effective segmentation method using capillary active contours, which extended geodesic active contours to capillaries modeled on the physical phenomenon of capillary actions (Yan and Kassim, 2006). However, deformable models can easily have leakage around the edge (Angelini et al, 2005;Cengizler et al, 2014). The leakage gets into the area outside of blood vessels during iterative optimization, especially at the end and the stenotic parts of blood vessels.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model

Fan

Bian

Chen

et al. 2020

Front. Neuroinform.

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Automated cerebrovascular segmentation of time-of-flight magnetic resonance angiography (TOF-MRA) images is an important technique, which can be used to diagnose abnormalities in the cerebrovascular system, such as vascular stenosis and malformation. Automated cerebrovascular segmentation can direct show the shape, direction and distribution of blood vessels. Although deep neural network (DNN)-based cerebrovascular segmentation methods have shown to yield outstanding performance, they are limited by their dependence on huge training dataset. In this paper, we propose an unsupervised cerebrovascular segmentation method of TOF-MRA images based on DNN and hidden Markov random field (HMRF) model. Our DNN-based cerebrovascular segmentation model is trained by the labeling of HMRF rather than manual annotations. The proposed method was trained and tested using 100 TOF-MRA images. The results were evaluated using the dice similarity coefficient (DSC), which reached a value of 0.79. The trained model achieved better performance than that of the traditional HMRFbased cerebrovascular segmentation method in binary pixel-classification. This paper combines the advantages of both DNN and HMRF to train the model with a not so large amount of the annotations in deep learning, which leads to a more effective cerebrovascular segmentation method.

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A fluid dynamics-based deformable model for segmentation of cervical cell images

Cited by 5 publications

References 15 publications

Data cluster analysis-based classification of overlapping nuclei in Pap smear samples

Data cluster analysis-based classification of overlapping nuclei in Pap smear samples

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

Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model

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