Segmentation and classification of cervix lesions by pattern and texture analysis

Yeshwanth Srinivasan, Fei Gao, Bhakti Tulpule, Shuyu Yang, Sunanda Mitra, Brian Nutter

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


This work aims at automated segmentation of major lesions observed in early stages of uterine cervical cancer. Automated segmentation reduces subjective variability and cost in current manual evaluation methods used to determine the biopsy locations for diagnosis. Two different methods, a non-convex optimisation approach and mathematical morphological approach, are used to segment the aceto-white region. Within this region other abnormalities, such as mosaic patterns, are classified by fuzzy c-means using a textural feature obtained from skeletonised vascular structures. These vascular structures are extracted by a series of morphological operations. Minimisation of uncertainties for degraded images is also discussed.

Original languageEnglish
Pages (from-to)234-246
Number of pages13
JournalInternational Journal of Intelligent Systems Technologies and Applications
Issue number3-4
StatePublished - 2006


  • automated segmentation
  • cervix lesions
  • classification
  • fuzzy c-means
  • morphological operators
  • texture analysis
  • uncertainties


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