Multilevel Thresholding Based Segmentation and Feature Extraction for Pulmonary Nodule Detection

被引：36

作者：

John, Jibi ^{[1
]}

Mini, M. G. ^{[2
]}

机构：

[1] Model Engn Coll, Kochi 682021, Kerala, India

[2] Coll Engn, Cherthala 688541, Kerala, India

来源：

INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING, SCIENCE AND TECHNOLOGY (ICETEST - 2015) | 2016年 / 24卷

关键词：

Computed Tomography(CT) scans; Lung Cancer; Medical images; Multilevel thresholding; Segmentation; Pulmonary Nodule Detection; LUNG SEGMENTATION; CT;

D O I：

10.1016/j.protcy.2016.05.209

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The identification of pulmonary nodules in humans has always been a vexing problem in the field of medical electronics. In this paper, an approach is proposed for pulmonary nodule segmentation and feature extraction using multilevel thresholding. The suitably extracted features can go a long way in the efficient detection of pulmonary nodules, which in turn can improve the chances for successful classification of nodules. The proposed segmentation with three level thresholding along with the features extracted can be incorporated to any suitable classification architecture to detect pulmonary nodules with better accuracy. (C) 2016 The Authors. Published by Elsevier Ltd.

引用

收藏

页码：957 / 963

页数：7

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