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

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. Introduction American National Cancer Institute statistics that almost 22.7% cancer deaths is caused by lung cancer, and pulmonary nodules are potential manifestation of lung cancer [8]. So, early diagnosis is one of the key issues that can reduce the mortality of lung cancer [5]. Therefore, research on the lung cancer computer-aided diagnosis techniques has become more and more important. For promoting the development of CAD technology in lung cancer [2][3], the National Cancer Institute (NCI) of The United States established a lung CT image database---Lung Imaging Database Consortium (LIDC), which contains 1012 chest cases and 1356 nodules at the time of this study [4][13]. Each nodule case contains the painting boundary coordinates and vision features from four radiologists. And the malignancy ratings were set by the visual decision of the four radiologists from one to five. The bigger value represents more malignant suspicious. The main working process of the CAD system is as follows: (1) the segmentation of lung nodules; (2) features extraction; (3) classification and recognition. The key technique includes image processing and machine learning. In this paper, the main method of lung nodules segmentation is based on FCM algorithm, which has been widely used in machine learning area. The FCM algorithm is accord with human cognitive characteristics, which are simple to describe and easy to implement. But when it is applied to the field of image segmentation, the traditional FCM algorithm[7] is sensitive to noise, low in neighborhood information correlation, and easy to fall into local convergence. To solve these problems, many scholars have studied on it. Ahmed [1] etc. proposed a FCM algorithm of offset correction (FCM_S), which introduces spatial information by modifying the objective function of traditional FCM algorithm. The algorithm achieves 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Engineering, volume 126