Segmentation of sputum color image for lung cancer diagnosis based on mean shift algorithm

Fatma Taher, Naoufel Werghi, Hussain Al-Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


This paper presents the mean shift segmentation algorithm for segmenting the extracted sputum cells into nuclei and cytoplasm regions. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer. The mean shift is a mode seeking process on a surface design with a kernel. Also it will be used as a strategy to perform multistart global optimization. The histogram analysis is used to find the best distribution of the nuclei and cytoplasm sputum cell pixels and to find the best color space that can be used to perform the mean shift segmentation. The Mena shift method offers better performance compared to other segmentation algorithm including Hopefield Neural Network (HNN). The new method is validated on a set of manually defined ground truths sputum images.

Original languageBritish English
Title of host publicationIEEE AFRICON 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781467359405
StatePublished - 2013
EventIEEE AFRICON 2013 - Pointe-Aux-Piments, Mauritius
Duration: 9 Sep 201312 Sep 2013

Publication series

NameIEEE AFRICON Conference
ISSN (Print)2153-0025
ISSN (Electronic)2153-0033


ConferenceIEEE AFRICON 2013


  • histogram analysis
  • lung cancer
  • Mean shift segmentation
  • sputum image


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