Lungs Nodule Detection by Using Fuzzy Morphology from CT Scan Images

M. Jaffar, Ayyaz Hussain, A. M. Mirza
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引用次数: 5

Abstract

In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) scanned images by using Fuzzy C-Mean (FCM) and morphological techniques. First of all, fuzzy have been used for automated segmentation of lungs. Region of interests (ROIs) have been extracted by using 8 directional searches slice by slice and then 3D ROI image have been constructed. A 3D template has been constructed and convolves with the 3D ROI image. Finally FCM have been used to extract ROI that contain nodule. The proposed system is capable to perform fully automatic segmentation and nodule detection from CT Scan Lungs images, based solely on information contained by the image itself. The technique was tested against the 50 datasets of different patients received from Aga Khan Medical University, Pakistan and Lung Image Database Consortium (LIDC) dataset.
基于CT扫描图像模糊形态学的肺结节检测
本文提出了一种利用模糊c均值(FCM)和形态学技术从计算机断层扫描(CT)图像中检测肺结节的方法。首先,模糊图像被用于肺的自动分割。通过逐层8次定向搜索提取感兴趣区域,构建三维感兴趣区域图像。构造了三维模板,并与三维ROI图像进行卷积。最后利用FCM提取了含有结节的ROI。该系统能够完全基于图像本身包含的信息,从CT扫描肺部图像中执行全自动分割和结节检测。该技术针对来自巴基斯坦阿加汗医科大学和肺图像数据库联盟(LIDC)数据集的50个不同患者数据集进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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