Shaukat, F., Raja, G., Gooya, A. et al. (1 more author) (2017) Fully automatic and accurate detection of lung nodules in CT images using a hybrid feature set. Medical Physics, 44 (7). pp. 3615-3629. ISSN 0094-2405
Abstract
PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantly. METHOD: The proposed method starts with pre-processing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multi scale dot enhancement filtering prior to nodule detection and feature extraction. Finally, classification of lung nodules is achieved using Support Vector Machine (SVM) classifier. The feature set consists of intensity, shape (2D and 3D) and texture features, which have been selected to optimize the sensitivity and reduce false positives. In addition to SVM, some other supervised classifiers like K-Nearest-Neighbour (KNN), Decision Tree and Linear Discriminant Analysis (LDA) have also been used for performance comparison. The extracted features have also been compared class-wise to determine the most relevant features for lung nodule detection. The proposed system has been evaluated using 850 scans from Lung Image Database Consortium (LIDC) dataset and k-fold cross validation scheme. RESULTS: The overall sensitivity has been improved compared to the previous methods and false positives per scan have been reduced significantly. The achieved sensitivities at detection and classification stages are 94.20% and 98.15% respectively with only 2.19 false positives per scan. CONCLUSIONS: It is very difficult to achieve high performance metrics using only a single feature class therefore hybrid approach in feature selection remains a better choice. Choosing right set of features can improve the overall accuracy of the system by improving the sensitivity and reducing false positives. This article is protected by copyright. All rights reserved.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 American Association of Physicists in Medicine. This is an author produced version of a paper subsequently published in Medical Physics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | CAD; Feature extraction; Lung nodule detection |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Apr 2017 15:44 |
Last Modified: | 20 Jul 2023 14:29 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/mp.12273 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:115106 |