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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006
eBook Chapter
50 An Improved Kernel-Based Adaptive Image Segmentation Process for Lung Cancer Detection from Biopsy Images
By
Walker H. Land, Jr
,
Walker H. Land, Jr
Dept. of Bioengineering,
Binghamton University
Binghamton, New York
USA
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Richard M. Lee, Jr
,
Richard M. Lee, Jr
Dept. of Computer Science,
Binghamton University
Binghamton, New York
USA
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Daniel W. Mckee, Jr
,
Daniel W. Mckee, Jr
Dept. of Mathematics and Computer Science,
Mansfield University
Binghamton, New York
USA
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Dansheng Song, Jr
,
Dansheng Song, Jr
Division of Cancer Control and Prevention, Moffitt Cancer and Research Institute
University of South Florida
Tampa, Florida
USA
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Wei Qian, Jr
,
Wei Qian, Jr
Dept. OF Interdisciplinary Oncology, Moffitt Cancer and Research Institute
University of South Florida
Tampa, Florida
USA
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Tatyana Zhokov, Jr
Tatyana Zhokov, Jr
Division of Cancer control and Prevention, Moffitt Cancer and Research Institute
University of South Florida
Tampa, Florida
USA
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Page Count:
6
-
Published:2006
Citation
Land, WH, Jr, Lee, RM, Jr, Mckee, DW, Jr, Song, D, Jr, Qian, W, Jr, & Zhokov, T, Jr. "An Improved Kernel-Based Adaptive Image Segmentation Process for Lung Cancer Detection from Biopsy Images." Intelligent Engineering Systems through Artificial Neural Networks, Volume 16. Ed. Dagli, CH, Buczak, AL, Enke, DL, Embrechts, M, & Ersoy, O. ASME Press, 2006.
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The purpose of this study was to develop a computer-based second opinion diagnostic tool that could read microscope images of lung tissue from resection and lung cells from needle biopsies, and then classify these tissue samples as normal or cancerous. This problem can be partitioned into three areas: segmentation, feature extraction and measurement, and finally classification. One component of this research is to introduce the Mean Shift segmentation algorithm as a prior stage to a kernel-based extension of the Fuzzy C-Means clustering algorithm that provides a coarse initial segmentation. This process is followed by heuristic-based mechanisms to improve the accuracy...
Abstract
Introduction
Segmentation — Phase 1
Feature Identification and Measurement — Phase 2
Classification — Phase 3
Results
Discussion
Conclusions
Acknowledgements
References
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