Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

Yinhai Wang, Danny Crookes, Osama Sharaf Eldin, Shilan Wang, Peter Hamilton, Jim Diamond

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)
443 Downloads (Pure)


This paper introduces an automated computer- assisted system for the diagnosis of cervical intraepithelial neoplasia (CIN) using ultra-large cervical histological digital slides. The system contains two parts: the segmentation of squamous epithelium and the diagnosis of CIN. For the segmentation, to reduce processing time, a multiresolution method is developed. The squamous epithelium layer is first segmented at a low (2X) resolution. The boundaries are further fine tuned at a higher (20X) resolution. The block-based segmentation method uses robust texture feature vectors in combination with support vector machines (SVMs) to perform classification. Medical rules are finally applied. In testing, segmentation using 31 digital slides achieves 94.25% accuracy. For the diagnosis of CIN, changes in nuclei structure and morphology along lines perpendicular to the main axis of the squamous epithelium are quantified and classified. Using multi-category SVM, perpendicular lines are classified into Normal, CIN I, CIN II, and CIN III. The robustness of the system in term of regional diagnosis is measured against pathologists' diagnoses and inter-observer variability between two pathologists is considered. Initial results suggest that the system has potential as a tool both to assist in pathologists' diagnoses, and in training.
Original languageEnglish
Pages (from-to)112-121
Number of pages10
JournalIEEE Journal of Selected Topics in Signal Processing, Special Issue on Digital Image Processing Techniques for Oncology
Issue number1
Publication statusPublished - Feb 2009

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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