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Content Provider | IET Digital Library |
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Author | Tavakoli, Mahsa Bank Orooji, Mahdi Teimouri, Mehdi Shahabifar, Ramita |
Abstract | Nowadays, the proficiency of Computer-Aided Diagnosis systems for early diagnosis of malignant nodules in baseline Computed Tomography (CT) scan of chest crucially depends on the authenticity of the segmented nodule. In this study, the authors introduce a new morphological feature called solidity radius (SR). They then employ this feature in the new segmentation framework for the automatic segmentation of nodule and the attached vessels around the seed point on the nodule, delineated by an expert. In the framework, they extract the SR and the curvature features and employ them to determine the candidate pixels of the nodule. They then use the convex-hull image of the candidate pixels to surround the nodule area. Afterward, using the region growing on the Hessian-based vesselness enhancement map, the attached vessels are labelled. Finally, they apply the traditional solidity feature of the segmented nodule and the pattern of the related skeleton to prune the false positive pieces. They validate the introduced approach on two datasets, including 56 and 481 CTs (containing 1205 nodules). They show the proficiency of their SR-based approach compared to the state-of-the-art methods with average Dice Similarity Coefficients of 77.98 and 77.47% for the two datasets, respectively. |
Starting Page | 1520 |
Ending Page | 1528 |
Page Count | 9 |
ISSN | 17519659 |
Volume Number | 14 |
e-ISSN | 17519667 |
Issue Number | Issue 8, Jun (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/14/8 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1054 |
Journal | IET Image Processing |
Publisher Date | 2020-03-09 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Automatic Segmentation Baseline Computed Tomography Scan Biology And Medical Computing Biomedical Imaging/measurement Chest Computer Vision And Image Processing Technique Computer-aided Diagnosis System Computerised Tomography CT Scan Curvature Features Feature Extraction Hessian-based Vesselness Enhancement Map Image Enhancement Image Recognition Image Segmentation Lung Malignant Nodules Medical Image Processing Morphological Feature Nodule Area Patient Diagnostic Method And Instrumentation Pulmonary Nodule Radiography And Computed Tomography Segmentation Framework Segmented Nodule 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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