Detection of Cervical Cancer at an Early Stage Using Hybrid Segmentation Techniques from PAP Smear Images

Ray, Asmita; Maitra, Indra Kanta; Bhattacharyya, Debnath

doi:10.14257/ijast.2018.112.03

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…In addition, a Neutrosophic Adaptive Mean Shift Cut-based Cervical Cancer Detection Technique (NAMSC-CCDT) was contributed in [17] to facilitate an effective segmentation of Pap smear cells. In this NAMSC-CCDT, the image is first transformed into a neutrosophic set represented through true, false and indeterminacy membership values.…”

Section: Previous Related Workmentioning

confidence: 99%

“…Proposed HLC-BC-GCST 99.51 99.11 Marinakis, 2008 [13] 98.56 98. 13 Marinakis, 2009 [14] 98.73 98.24 Chankong, 2014 [15] 98.92 97.86 Zhang, 2014 [16] 98.43 97.83 Guo, 2018 [21] 98.32 97.84 Ray, 2018 [21] 98.55 97.21 [13] 98.12 3.42 Marinakis, 2009 [14] 97.86 4.32 Chankong, 2014 [15] 97.43 3.78 Zhang,2014 [16] 97.23 3.65 Guo, 2018 [21] 98.03 2.96 Ray, 2018 [17] 98.09 2.92 Table 3. Comparative Performance: Precision and Recall Value.…”

Section: First Author and Yearmentioning

confidence: 99%

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A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

et al. 2022

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Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach, the Linear Iterative Clustering process is employed to cluster the potential features of the preprocessed image, which is then combined with GrabCut to prevent the issues that arise when the number of superpixels is minimized. In addition, the proposed HLC-BC-GCST scheme benefits of the advantages of the Gaussian mixture model (GMM) on the extracted features from the iterative clustering method, based on which the mapping is performed to describe the energy function. Then, Bayes classification is used for reconstructing the graph cut model from the extracted energy function derived from the GMM model-based Linear Iterative Clustering features for better computation and implementation. Finally, the boundary optimization method is utilized to considerably minimize the roughness of cervical cells, which contains the cytoplasm and nuclei regions, using the GrabCut algorithm to facilitate improved segmentation accuracy. The results of the proposed HLC-BC-GCST scheme are 6% better than the results obtained by other standard detection approaches of cervical cancer using graph cuts.

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Section: Previous Related Workmentioning

confidence: 99%

Section: First Author and Yearmentioning

confidence: 99%

A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

et al. 2022

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“…In the overlapping clumps of cervical cell images, different cells overlap each other, making their boundaries more blurred. e structural analysis of cervical cells shows that most of the nuclei are located in the cells [34]. Although the positions vary greatly, the cytoplasmic boundary surrounds the nucleus.…”

Section: Filtering Based On the Radial Region Of The Nucleusmentioning

confidence: 99%

Overlapping Cell Segmentation of Cervical Cytology Images Based on Nuclear Radial Boundary Enhancement

Wang

Lan

Niu

et al. 2022

Mobile Information Systems

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The accurate segmentation of cervical cell images is one of the key steps of the cervical cancer computer-aided diagnosis system. For the problem of overlapping cell and boundary blurring in cervical cell clusters, the researchers propose a segmentation algorithm based on the nuclear radial boundary enhancement for overlapping cell of cervical cytology images. This method not only suppresses the noise of cervical cytology images but also preserves the contrast of overlapping cell boundary. The researchers generate the weight graph by the candidate contour points and contour line segment attributes and utilize the dynamic programming algorithm to find the shortest path in the weight graph. The shortest path corresponds to the coarse segmentation contour in the cell image. The level set model is used to finely segment the obtained coarse cell segmentation boundary, so as to obtain the final cervical cell boundary. Through the quantitative and qualitative evaluation results, such as dice similarity coefficient, true positive rate, and false positive rate, it can be seen that the overlapping cell segmentation algorithm in this paper has achieved better segmentation results. Compared with other current overlap cell segmentation algorithms, the segmentation results obtained in this paper have greater advantages.

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“…The superpixel area is clustered, and the final image segmentation result is obtained, but the segmentation accuracy is not high. Literature [19] focuses on simple linear iterative clustering superpixels and fast and automatically adjustable Gaussian path The image segmentation technology combined with the basis function kernel fuzzy C-means combines superpixels and image segmentation as a preprocessing step for image classification and then provides better results for image classification, but the accuracy of the algorithm is not high [20].…”

Section: Related Workmentioning

confidence: 99%

An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour

Wei

Wang

2021

Wireless Communications and Mobile Computing

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The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford-Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.

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Detection of Cervical Cancer at an Early Stage Using Hybrid Segmentation Techniques from PAP Smear Images

Abstract: Abstract

Cited by 5 publications

References 7 publications

A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

Overlapping Cell Segmentation of Cervical Cytology Images Based on Nuclear Radial Boundary Enhancement

An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour

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