NDLI: Lung tumour detection by fusing extended local binary patterns and weighted orientation of difference from computed tomography

Content Provider	IET Digital Library
Author	Shakoor, Mohammad Hossein
Abstract	Lung cancer is one of the leading causes of death in the world. Although early detection of lung tumours (nodules) can remarkably diminish the mortal rate, precise detection of them is not always possible by visual inspection of the computerised tomography images. Since nodules with different sizes have non-uniform shape and brightness, texture attributes and also the gradient of orientation can be good candidate features, which have been used for this purpose. They determined the co-occurrence matrix of the extended local binary pattern (ELBP) along with weighted orientation difference (WOD) for each sub-region of the lung area. Local binary pattern is a texture descriptor that can extract the discriminative features efficiently. The proposed ELBP is rotation invariant and suitable to describe non-uniform patterns. Moreover, WOD as a structural feature uses the magnitude of each edge as the weight of its orientation difference. After constructing the co-occurrence matrix, discriminative features were extracted from this matrix and fed into a support vector machine in order to classify each sub-region as a cancerous (nodule) or normal tissue. The proposed method was compared to some of state-of-the-art nodule detection methods and was assessed over several real datasets in terms of specificity, sensitivity and accuracy.
Starting Page	877
Ending Page	884
Page Count	8
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 6, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5070
Journal	IET Image Processing
Publisher Date	2019-01-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Biology And Medical Computing Biomedical Imaging/measurement Brightness Cancer Cancerous Tissue Computed Tomography Computer Vision And Image Processing Technique Computerised Tomography Computerised Tomography Image Cooccurrence Matrix Discriminative Feature Extraction Early Detection ELBP Extended Local Binary Pattern Feature Extraction Graph Theory Image Classification Image Recognition Image Texture Knowledge Engineering Technique Lung Lung Area Lung Cancer Lung Tumour Detection Lung Tumours Matrix Algebra Medical Image Processing Mortal Rate Nonuniform Pattern Normal Tissue Patient Diagnostic Method And Instrumentation Precise Detection Radiography And Computed Tomography Set Theory Structural Feature Support Vector Machine Texture Attributes Texture Descriptor Tumours Weighted Orientation Difference WOD 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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