Shaukat, F, Raja, G, Gooya, A et al. (1 more author) (2017) Fully automatic 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 preprocessing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multiscale 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‐Neighbor (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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2017, American Association of Physicists in Medicine. This is the peer reviewed version of the following article: 'Shaukat, F, Raja, G, Gooya, A, and Frangi, A (2017). Fully automatic detection of lung nodules in CT images using a hybrid feature set. Medical Physics, 44 (7). pp. 3615-3629,' which has been published in final form at [https://doi.org/10.1002/mp.12273]. This article may be used for non¬commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. |
Keywords: | CAD, feature extraction, lung nodule detection |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Sep 2018 14:59 |
Last Modified: | 04 Sep 2018 14:59 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/mp.12273 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135025 |