Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering
In this paper, we propose a detection method of pulmonary nodules in X-ray computed tomography (CT) scans by use of three image filters and appearance-based k-means clustering. First, voxel values are suppressed in radial directions so as to eliminate extra regions in the volumes of interest (VOIs)....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-05-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1999-4893/8/2/209 |
| _version_ | 1818272338911690752 |
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| author | Takanobu Yanagihara Hotaka Takizawa |
| author_facet | Takanobu Yanagihara Hotaka Takizawa |
| author_sort | Takanobu Yanagihara |
| collection | DOAJ |
| description | In this paper, we propose a detection method of pulmonary nodules in X-ray computed tomography (CT) scans by use of three image filters and appearance-based k-means clustering. First, voxel values are suppressed in radial directions so as to eliminate extra regions in the volumes of interest (VOIs). Globular regions are enhanced by moment-of-inertia tensors where the voxel values in the VOIs are regarded as mass. Excessively enhanced voxels are reduced based on displacement between the VOI centers and the gravity points of the voxel values in the VOIs. Initial nodule candidates are determined by these filtering processings. False positives are reduced by, first, normalizing the directions of intensity distributions in the VOIs by rotating the VOIs based on the eigenvectors of the moment-of-inertia tensors, and then applying an appearance-based two-step k-means clustering technique to the rotated VOIs. The proposed method is applied to actual CT scans and experimental results are shown. |
| first_indexed | 2024-12-12T21:40:29Z |
| format | Article |
| id | doaj.art-b35dd57816d442429216ad4e480072d4 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-12T21:40:29Z |
| publishDate | 2015-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-b35dd57816d442429216ad4e480072d42022-12-22T00:11:04ZengMDPI AGAlgorithms1999-48932015-05-018220922310.3390/a8020209a8020209Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based ClusteringTakanobu Yanagihara0Hotaka Takizawa1Graduate School of Systems and Information Engineering, University of Tsukuba, 305-8573, JapanFaculty of Engineering, Information and Systems, University of Tsukuba, 305-8573, JapanIn this paper, we propose a detection method of pulmonary nodules in X-ray computed tomography (CT) scans by use of three image filters and appearance-based k-means clustering. First, voxel values are suppressed in radial directions so as to eliminate extra regions in the volumes of interest (VOIs). Globular regions are enhanced by moment-of-inertia tensors where the voxel values in the VOIs are regarded as mass. Excessively enhanced voxels are reduced based on displacement between the VOI centers and the gravity points of the voxel values in the VOIs. Initial nodule candidates are determined by these filtering processings. False positives are reduced by, first, normalizing the directions of intensity distributions in the VOIs by rotating the VOIs based on the eigenvectors of the moment-of-inertia tensors, and then applying an appearance-based two-step k-means clustering technique to the rotated VOIs. The proposed method is applied to actual CT scans and experimental results are shown.http://www.mdpi.com/1999-4893/8/2/209chest X-ray computed tomography (CT) scansdetection of pulmonary nodulesmoment-of-inertiaappearance-based k-means clustering |
| spellingShingle | Takanobu Yanagihara Hotaka Takizawa Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering Algorithms chest X-ray computed tomography (CT) scans detection of pulmonary nodules moment-of-inertia appearance-based k-means clustering |
| title | Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering |
| title_full | Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering |
| title_fullStr | Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering |
| title_full_unstemmed | Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering |
| title_short | Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering |
| title_sort | pulmonary nodule detection from x ray ct images based on region shape analysis and appearance based clustering |
| topic | chest X-ray computed tomography (CT) scans detection of pulmonary nodules moment-of-inertia appearance-based k-means clustering |
| url | http://www.mdpi.com/1999-4893/8/2/209 |
| work_keys_str_mv | AT takanobuyanagihara pulmonarynoduledetectionfromxrayctimagesbasedonregionshapeanalysisandappearancebasedclustering AT hotakatakizawa pulmonarynoduledetectionfromxrayctimagesbasedonregionshapeanalysisandappearancebasedclustering |