Nuclei Segmentation using a Level Set Active Contour Method and Spatial Fuzzy C-Means Clustering


Digitized histology images are analyzed by expert pathologists in one of several approaches to assess precervical cancer conditions such as cervical intraepithelial neoplasia (CIN). Many image analysis studies focus on detection of nuclei features to classify the epithelium into the CIN grades. The current study focuses on nuclei segmentation based on level set active contour segmentation and fuzzy c-means clustering methods. Logical operations applied to morphological post-processing operations are used to smooth the image and to remove non-nuclei objects. On a 71-image dataset of digitized histology images (where the ground truth is the epithelial mask which helps in eliminating the non epithelial regions), the algorithm achieved an overall nuclei segmentation accuracy of 96.47%. We propose a simplified fuzzy spatial cost function that may be generally applicable for any n-class clustering problem of spatially distributed objects.

Meeting Name

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017 (2017: Feb. 27-Mar. 1, Porto, Portugal)


Electrical and Computer Engineering

Second Department


Keywords and Phrases

Computer graphics; Computer vision; Cost functions; Diseases; Fuzzy systems; Histology; Image analysis; Image processing; Numerical methods; Active contours; Cervical cancers; Epithelium; Fuzzy C means clustering; Level Set method; Nuclei segmentation; Image segmentation; Fuzzy C-means Clustering; Image Processing

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Document Type

Article - Conference proceedings

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Publication Date

01 Feb 2017