Cell Nucleus Segmentation of Skin Tumor Using Image Processing

Abstract

Automation and quantification of diagnosis of tumor cell images have been studied for these three decades in the field of medical imaging technology. Many techniques of image processing were proposed to solve problems such as nucleus segmentation and classification. But these studies have mainly focused on epithelial tumors. Nonepithelial skin tumors such as dermatofibroma (DF) and dermatofibrosarcoma protuberans (DFSP) have not been enough studied. DF is benign tumorous disease and DFSP is mid%grade malignant tumor. Recently, it is necessary that criterion of classification between DF and DFSP is quantitatively specified. in this paper a system for segmenting cell nuclei of DF and DFSP is proposed. Nuclei regions are objectively segmented and surrounded using edges of strength by the system. Segmentation of arbitrary% shaped nuclear regions and weakly stained nuclear region is made. A dynamic thresholding method with combining Laplacian histograin with Ohtsu's method is used for segmentation. Segmentation test was done using real tissue cell images of DF and DFSP to evaluate validity of this system. Shape characteristics such as grade of similarity, to circle were also computed from the segmented regions to assure that some differences between DF and DFSP is expressed iii its distribution. Keyn%ords - Segmentation, skin tuinor, cell nucleus, dynainic threshold, Laplacian histograin

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410134

Entities

People

  • Teruaki Oka
  • Tomoo Joke
  • Toshiyuki Tanaka

Organizations

  • Keio University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Cancer
  • Cell Nucleus
  • Cells
  • Cervical Cancers
  • Classification
  • Clinical Laboratories
  • Computer Vision
  • Digital Images
  • Equations
  • Extraction
  • Gray Scale
  • Health Services
  • Histograms
  • Image Processing
  • Medical Personnel
  • Neoplasms
  • Sarcoma

Readers

  • Computer Vision.
  • Data Mining and Knowledge Discovery.
  • Oncology (Cancer Research).