NDLI: Lung segmentation based on random forest and multi-scale edge detection

Content Provider	IET Digital Library
Author	Liu, Caixia Zhao, Ruibin Pang, Mingyong
Abstract	To achieve an automatic and accurate segmentation of lungs and improve the clinical efficiency of computer-aided diagnosis, the authors present a lung segmentation algorithm based on the random forest method and a multi-scale edge detection technique. The algorithm carries a first step of lung region extraction and a second step of lung nodule segmentation. By combining texture information, the improved superpixel generation method can better deal with initial segmentation on lung computed tomography images with inhomogeneous intensity. Then, the lung region is further extracted by using the random forest classifier on the superpixel features, and the lung contours are corrected with a proposed circle tracing technique. Finally, the segmentation is further refined by employing a multi-scale edge detection technique, which enables their method to detect suspicious nodules with various intensities and sizes adaptively. The effectiveness of the proposed approach is demonstrated on a group of datasets by comparing with the corresponding ground truths as well as the classical algorithms. Experimental results show that the proposed method has a higher precision than the compared algorithms in a fully automatic fashion.
Starting Page	1745
Ending Page	1754
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 10, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0130
Journal	IET Image Processing
Publisher Date	2019-06-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate Segmentation Automatic Segmentation Biology And Medical Computing Biomedical Imaging/measurement Circle Tracing Technique Computer Vision And Image Processing Technique Computer-aided Diagnosis Computerised Tomography Edge Detection Feature Extraction Image Classification Image Recognition Image Segmentation Image Texture Improved Superpixel Generation Method Initial Segmentation Lung Lung Computed Tomography Image Lung Contours Lung Nodule Segmentation Lung Region Extraction Lung Segmentation Algorithm Lungs Medical Image Processing Multiscale Edge Detection Technique Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Radiography And Computed Tomography Random Forest Classifier Random Forest Method X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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