title: Feature quantification and abnormal detection on cervical squamous epithelial cells.
abstract: Feature analysis and classification detection of abnormal cells from images for pathological analysis are an important issue for the realization of computer assisted disease diagnosis. This paper studies a method for cervical squamous epithelial cells. Based on cervical cytological classification standard and expert diagnostic experience, expressive descriptors are extracted according to morphology, color, and texture features of cervical scales epithelial cells. Further, quantificational descriptors related to cytopathology are derived as well, including morphological difference degree, cell hyperkeratosis, and deeply stained degree. The relationship between quantified value and pathological feature can be established by these descriptors. Finally, an effective method is proposed for detecting abnormal cells based on feature quantification. Integrated with clinical experience, the method can realize fast abnormal cell detection and preliminary cell classification.
authors: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
authors: Mingzhu
authors: M
authors: Zhao
authors: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
authors: Lei
authors: 0000-0001-5384-0757
authors: L
authors: Chen
authors: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
authors: Linjie
authors: L
authors: Bian
authors: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
authors: Jianhua
authors: 0000-0003-2567-5357
authors: J
authors: Zhang
authors: Department of Informatics, University of Hamburg, 22527 Hamburg, Germany.
authors: Chunyan
authors: C
authors: Yao
authors: Department of Informatics, University of Hamburg, 22527 Hamburg, Germany.
authors: Jianwei
authors: J
authors: Zhang
mesh_terms: D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis* / Q000473:pathology*; D002467:Cell Nucleus / Q000378:metabolism; D002584:Cervix Uteri / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003952:Diagnostic Imaging / Q000379:methods; D004847:Epithelial Cells / Q000166:cytology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012984:Software; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000473:pathology*