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dc.contributor.authorTareef, Afaf
dc.contributor.authorSong, Yang
dc.contributor.authorCai, Weidong
dc.contributor.authorHuang, Heng
dc.contributor.authorChang, Hang
dc.contributor.authorWang, Yue
dc.contributor.authorFulham, Michael
dc.contributor.authorFeng, Dagan
dc.contributor.authorChen, Mei
dc.date.accessioned2019-06-07
dc.date.available2019-06-07
dc.date.issued2016-10-25
dc.identifier.citationA. Tareef, Y. Song, W. Cai, H. Huang, H. Chang, Y. Wang, M. Fulham, D. Feng, M. Chen, “Automatic Segmentation of Overlapping Cervical Smear Cells based on Local Distinctive Features and Guided Shape Deformation”, Neurocomputing, 221:94-107, January 2017. https://doi.org/10.1016/j.neucom.2016.09.070en_AU
dc.identifier.urihttp://hdl.handle.net/2123/20507
dc.description.abstractAutomated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the rst stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classi ed by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is re ned by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. Keywords: overlapping cervical smear cells, feature extraction, sparse coding, shape deformation, distance regularized level set.en_AU
dc.language.isoen_AUen_AU
dc.publisherElsevieren_AU
dc.relationARC DP140100211
dc.rightsThe final authenticated version is available online at: https://doi.org/10.1016/j.neucom.2016.09.070 with CC-BY-NC-ND licenseen_AU
dc.titleAutomatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformationen_AU
dc.typeArticleen_AU
dc.identifier.doi10.1016/j.neucom.2016.09.070


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