Detection and segmentation of sputum cell for early lung cancer detection

Naoufel Werghi; Christian Donner; Fatma Taher; Hussain Al-Ahmad

doi:10.1109/ICIP.2012.6467484

Detection and segmentation of sputum cell for early lung cancer detection

Naoufel Werghi
, Christian Donner
, Fatma Taher
, Hussain Al-Ahmad

Electrical Engineering

University of Osnabrueck

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

11 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. For this purpose, a computer-aided design system using images of sputum stained smears is a practical, low-cost, and totally non invasive solution. In this paper, 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 other competitive approaches via a series of experiments conducted with a data set of 88 images.

Original language	British English
Title of host publication	2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages	2813-2816
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2012.6467484
State	Published - 2012
Event	2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States Duration: 30 Sep 2012 → 3 Oct 2012

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2012 19th IEEE International Conference on Image Processing, ICIP 2012
Country/Territory	United States
City	Lake Buena Vista, FL
Period	30/09/12 → 3/10/12

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

Access to Document

10.1109/ICIP.2012.6467484

OpenUrl availability

Full text

Cite this

@inproceedings{2b50fc5109cb4d709dd946b7ae6728b9,

title = "Detection and segmentation of sputum cell 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. For this purpose, a computer-aided design system using images of sputum stained smears is a practical, low-cost, and totally non invasive solution. In this paper, 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 other competitive approaches via a series of experiments conducted with a data set of 88 images.",

keywords = "Bayesian classification, cell detection, early lung cancer detection, mean shift, medical image",

author = "Naoufel Werghi and Christian Donner and Fatma Taher and Hussain Al-Ahmad",

year = "2012",

doi = "10.1109/ICIP.2012.6467484",

language = "British English",

isbn = "9781467325332",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "2813--2816",

booktitle = "2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings",

note = "2012 19th IEEE International Conference on Image Processing, ICIP 2012 ; Conference date: 30-09-2012 Through 03-10-2012",

}

Werghi, N, Donner, C, Taher, F & Al-Ahmad, H 2012, Detection and segmentation of sputum cell for early lung cancer detection. in 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings., 6467484, Proceedings - International Conference on Image Processing, ICIP, pp. 2813-2816, 2012 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, FL, United States, 30/09/12. https://doi.org/10.1109/ICIP.2012.6467484

Detection and segmentation of sputum cell for early lung cancer detection. / Werghi, Naoufel; Donner, Christian; Taher, Fatma et al.
2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. p. 2813-2816 6467484 (Proceedings - International Conference on Image Processing, ICIP).

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

TY - GEN

T1 - Detection and segmentation of sputum cell for early lung cancer detection

AU - Werghi, Naoufel

AU - Donner, Christian

AU - Taher, Fatma

AU - Al-Ahmad, Hussain

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. For this purpose, a computer-aided design system using images of sputum stained smears is a practical, low-cost, and totally non invasive solution. In this paper, 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 other competitive approaches via a series of experiments 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. For this purpose, a computer-aided design system using images of sputum stained smears is a practical, low-cost, and totally non invasive solution. In this paper, 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 other competitive approaches via a series of experiments 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/84875846169

U2 - 10.1109/ICIP.2012.6467484

DO - 10.1109/ICIP.2012.6467484

M3 - Conference contribution

AN - SCOPUS:84875846169

SN - 9781467325332

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 2813

EP - 2816

BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings

T2 - 2012 19th IEEE International Conference on Image Processing, ICIP 2012

Y2 - 30 September 2012 through 3 October 2012

ER -

Detection and segmentation of sputum cell for early lung cancer detection

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

OpenUrl availability

Other files and links

Fingerprint

Cite this