By Topic

Head and Neck Cancer Detection in Histopathological Slides

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Mete, M. ; Arkansas Univ., Little Rock, AR ; Xiaowei Xu ; Chun-Yang Fan ; Shafirstein, G.

Histopathology, one of the most important routines of all laboratory procedures used in pathology, is critical for the diagnosis of cancer. Experienced pathologists read the histological slides acquired from biopsy specimen in order to outline malignant areas. Recently, in terms of histological image analysis the improvements in imaging techniques led to the discovery of virtual histological slides. In this technique, a special microscope scans a glass slide and generates a virtual slide at a resolution of 0.25 mum/pixel. Output images are of sufficiently high quality to generate immense interest within the research community. Since the recognition of cancer areas are time consuming and error prone, in this paper we describe a new method for automatic squamous cell carcinoma, known as head-neck cancer, detection using very large digital histological slides. The density-based clustering algorithm (DBSCAN) plays a key role in the determination of the corrupted cell nuclei. Using the support vector machine (SVM) classifier, the experimental results on seven head-neck slides show that the proposed algorithm performed well, obtaining an average of 96% accuracy. The classifier performance is evaluated using the standard precision and recall measures, as well as predictive accuracy

Published in:

Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on

Date of Conference:

Dec. 2006