Segmentation of lung from CT using various active contour models

Nithila Ezhil E.; Kumar S. S.

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Segmentation of lung from CT using various active contour models

机译：使用各种活动轮廓模型从CT分割肺

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The aim of the paper is to develop a region based active contour model using variational level set function for segmentation of lung. Ultimately, segmentation of parenchyma is essential for accurate diagnosis of various lung diseases. Among many imaging modalities, Computed Tomography (CT) is a pioneer to most image analysis applications. This work proposes a powerful technique named Selective Binary and Gaussian filtering-new Signed Pressure Force (SBGF-new SPF) function for segmentation of CT lung images. This process detects the external boundary of the lung and effectively stops the contour even at blurry boundaries. The proposed algorithm was compared with four different active contour models. Comparative experiments demonstrate the advantage of proposed method in terms of computation time and accurate segmented lung. (C)2018 Elsevier Ltd. All rights reserved.

机译：本文的目的是使用变异水平集函数开发基于区域的活动轮廓模型，以进行肺分割。最终，实质分割对于准确诊断各种肺部疾病至关重要。在许多成像方式中，计算机断层扫描（CT）是大多数图像分析应用程序的先驱。这项工作提出了一种强大的技术，称为CT肺图像分割的选择性二值和高斯滤波-新的有符号压力（SBGF-新的SPF）功能。此过程可以检测到肺的外部边界，即使边界模糊也可以有效地停止轮廓。将该算法与四种不同的主动轮廓模型进行了比较。比较实验证明了该方法在计算时间和准确分割肺部方面的优势。（C）2018 Elsevier Ltd.保留所有权利。

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来源
《Biomedical signal processing and control》 |2019年第1期|57-62|共6页
作者
Nithila Ezhil E.; Kumar S. S.;
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作者单位

Noorul Islam Ctr Higher Educ, Kumaracoil 629180, India;

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原文格式 PDF
正文语种 eng
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关键词
Segmentation; Active contour; Level set; Gaussian filtering; Signed pressure force;

机译：分割;有效轮廓;水平集;高斯滤波;有符号压力;

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1. Performance Evaluation of Geometric Active Contour (GAC) and Enhanced Geometric Active Contour Segmentation Model (ENGAC) for Medical Image Segmentation [J] . Ajala Funmilola Alaba, Emuoyibofarhe Justice O Computer Engineering and Intelligent Systems . 2012,第3期

机译：几何主动轮廓（GAC）和增强型几何主动轮廓分割模型（ENGAC）用于医学图像分割的性能评估
2. Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system [J] . KeshaniM., AzimifarZ., TajeripourF., Computers in Biology and Medicine . 2013,第4期

机译：使用SVM分类器和主动轮廓模型对肺结节进行分割和识别：完整的智能系统
3. Lung Segmentation by New Curve Stopping Function Using Geodesic Active Contour Model [J] . Chul Ho WON, Dong Hun KIM, Jung Hyun LEE, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences . 2006,第6期

机译：测地线活动轮廓模型通过新的曲线停止函数进行肺分割
4. Fetal lung segmentation using texture-based boundary enhancement and active contour models [C] . Li Xiaomin, Wang Yuanyuan, Yu Jinhua, 2010 3rd International Conference on Biomedical Engineering and Informatics . 2010

机译：使用基于纹理的边界增强和活动轮廓模型进行胎儿肺分割
5. Semantic Segmentation and Object Detection Based On Active Contour Model and Fuzzy Clustering. [D] . Memar Kouchehbagh, Sara. 2016

机译：基于主动轮廓模型和模糊聚类的语义分割与目标检测。
6. Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach [O] . Heewon Chung, Hoon Ko, Se Jeong Jeon, 2018

机译：利用主动轮廓模型和贝叶斯方法对具有胸膜结节识别的肺自动分割
7. Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model [O] . K. Gopi, J. Selvakumar 2018

机译：使用级别凝固方法的肺肿瘤检测与分割方法分析

Segmentation of lung from CT using various active contour models

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