Research on Modified Fuzzy C-means Algorithm in Lung Nodules Computer-aided Diagnosis (CAD) System

DOI

10.2991/icmmct-17.2017.141How to use a DOI?

Keywords

CAD; Fuzzy C-means Algorithm; Punishment Factor; Neighborhood Space Window; Gray Scale

Abstract

It is important for the early diagnosis and treatment of lung cancer in the Computer-aided Diagnosis/Detection (CAD) system, and accurate segmentation of pulmonary nodules from tomographic images is the basic and active research problem for the benign or malign diagnosis. For this reason, this work seeks to develop automatic detection and classification method of lung nodules. First, the algorithm separates lung parenchyma from the anatomical structures based on maximum between-cluster variance, image dilation and erosion. Secondly, a modified robust fuzzy c-means clustering(rFCM) segmentation algorithm is proposed, this method improves the objective function by adding a punishment factor, for eliminating the influence from noise and non-uniform gray problem. Experimental results have shown that the proposed method can achieve more accurate segmentation and perform better than other traditional algorithms in classification and recognition, Furthermore, the segmentation results on brain images also get a satisfied performance.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qing Li
AU  - Hui Liu
PY  - 2017/04
DA  - 2017/04
TI  - Research on Modified Fuzzy C-means Algorithm in Lung Nodules Computer-aided Diagnosis (CAD) System
BT  - Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
PB  - Atlantis Press
SP  - 684
EP  - 689
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-17.2017.141
DO  - 10.2991/icmmct-17.2017.141
ID  - Li2017/04
ER  -