NDLI: Lung vessel segmentation based on random forests

Content Provider	IET Digital Library
Author	Zhao, Bowen Cao, Zhulou Wang, Sicheng
Abstract	Lung vessel segmentation of computed tomography (CT) images is important in clinical practise and challenging due to difficulties associated with minor size and blurred edges of lung vessels. A vessel segmentation method is proposed for lung images based on a random forest classifier and sparse auto-encoder features. First, the multi-scale representations of lung images are obtained using the Gaussian pyramid. Second, a sparse auto-encoder of three layers is trained using randomly selected patches of these images. Next, the trained weight of the sparse auto-encoder is used as the convolution kernel to extract features of different scale images. Finally, a random forest classifier is exploited to segment the vessels. The proposed method was evaluated on the original and noise-added VESSEL12 dataset that is publicly available. Comparison with some classical methods and existing machine learning methods shows that the proposed method reaches the state-of-the-art accuracy. The results also show that a shallow neural network is a powerful feature extraction tool.
Starting Page	220
Ending Page	222
Page Count	3
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 4, Feb (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2016.4438
Journal	Electronics Letters
Publisher Date	2017-01-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Imaging/measurement Blurred Edges Computed Tomography Computer Vision And Image Processing Technique Computerised Tomography Convolution Kernel CT Image Edge Detection Feature Extraction Feature Extraction Tool Gaussian Pyramid Image Restoration Image Segmentation Knowledge Engineering Technique Learning in AI Lung Lung Image Lung Vessel Segmentation Machine Learning Method Medical Image Processing Multiscale Representations Neural Computing Technique Neural Nets Noise-added VESSEL12 Dataset Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Radiography And Computed Tomography Random Forest Classifier Shallow Neural Network Sparse Auto Encoder Sparse Autoencoder Feature X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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