title: Cytoplasm segmentation on cervical cell images using graph cut-based approach.
abstract: This paper proposes a method to segment the cytoplasm in cervical cell images using graph cut-based algorithm. First, the A* channel in CIE LAB color space is extracted for contrast enhancement. Then, in order to effectively extract cytoplasm boundaries when image histograms present non-bimodal distribution, Otsu multiple thresholding is performed on the contrast enhanced image to generate initial segments, based on which the segments are refined by the multi-way graph cut method. We use 21 cervical cell images with non-ideal imaging condition to evaluate cytoplasm segmentation performance. The proposed method achieved a 93% accuracy which outperformed state-of-the-art works.
authors.affiliation: Department of Biomedical Engineering, Shenzhen University, Shenzhen 518060, China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen 518060, China Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound Imaging, Shenzhen 518060, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Zhang
authors.forename: Hui
authors.initials: H
authors.lastname: Kong
authors.forename: Chien Ting
authors.initials: CT
authors.lastname: Chin
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
authors.forename: Siping
authors.initials: S
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001331:Automation; D002467:Cell Nucleus / Q000378:metabolism; D002584:Cervix Uteri / Q000166:cytology / Q000473:pathology*; D003287:Contrast Media / Q000737:chemistry; D003593:Cytoplasm / Q000378:metabolism* / Q000473:pathology; D005260:Female; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D011381:Programming Languages; D012984:Software; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000473:pathology
chemical_list: D003287:Contrast Media
keywords: A* channel; Cervical cell; cytoplasm segmentation; graph cut