Segmentation of sputum cell image for early lung cancer detection

N. Werghi; C. Donner; F. Taher; H. Alahmad

doi:10.1049/cp.2012.0433

Segmentation of sputum cell image for early lung cancer detection

N. Werghi
, C. Donner
, F. Taher
, H. Alahmad

Electrical Engineering

University of Osnabrueck

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its early detection significantly increases the chances of an effective treatment. To that end, computer-aided diagnosis system using images of sputum stained smears has been an attractive approach due to its practicality, low cost, and invasiveness. In this context, we present a framework for the detection and segmentation of sputum cells in sputum images using respectively, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with an other competitive technique via a series of experimentation conducted with a data set of 88 images.

Original language	British English
Title of host publication	IET Conference on Image Processing, IPR 2012
Edition	600 CP
DOIs	https://doi.org/10.1049/cp.2012.0433
State	Published - 2012
Event	IET Conference on Image Processing, IPR 2012 - London, United Kingdom Duration: 3 Jul 2012 → 4 Jul 2012

Publication series

Name	IET Conference Publications
Number	600 CP
Volume	2012

Conference

Conference	IET Conference on Image Processing, IPR 2012
Country/Territory	United Kingdom
City	London
Period	3/07/12 → 4/07/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bayesian classification
cell detection
early lung cancer detection
mean shift
Medical image

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10.1049/cp.2012.0433

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title = "Segmentation of sputum cell image for early lung cancer detection",

abstract = "Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15\%. Its early detection significantly increases the chances of an effective treatment. To that end, computer-aided diagnosis system using images of sputum stained smears has been an attractive approach due to its practicality, low cost, and invasiveness. In this context, we present a framework for the detection and segmentation of sputum cells in sputum images using respectively, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with an other competitive technique via a series of experimentation conducted with a data set of 88 images.",

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series = "IET Conference Publications",

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AU - Werghi, N.

AU - Donner, C.

AU - Taher, F.

AU - Alahmad, H.

PY - 2012

Y1 - 2012

N2 - Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its early detection significantly increases the chances of an effective treatment. To that end, computer-aided diagnosis system using images of sputum stained smears has been an attractive approach due to its practicality, low cost, and invasiveness. In this context, we present a framework for the detection and segmentation of sputum cells in sputum images using respectively, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with an other competitive technique via a series of experimentation conducted with a data set of 88 images.

AB - Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its early detection significantly increases the chances of an effective treatment. To that end, computer-aided diagnosis system using images of sputum stained smears has been an attractive approach due to its practicality, low cost, and invasiveness. In this context, we present a framework for the detection and segmentation of sputum cells in sputum images using respectively, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with an other competitive technique via a series of experimentation conducted with a data set of 88 images.

KW - Bayesian classification

KW - cell detection

KW - early lung cancer detection

KW - mean shift

KW - Medical image

UR - https://www.scopus.com/pages/publications/84865517754

U2 - 10.1049/cp.2012.0433

DO - 10.1049/cp.2012.0433

M3 - Conference contribution

AN - SCOPUS:84865517754

SN - 9781849196321

T3 - IET Conference Publications

BT - IET Conference on Image Processing, IPR 2012

T2 - IET Conference on Image Processing, IPR 2012

Y2 - 3 July 2012 through 4 July 2012

ER -

Segmentation of sputum cell image for early lung cancer detection

Abstract

Publication series

Conference

UN SDGs

Keywords

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