Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential Filter
Solitary pulmonary nodules (SPN) in digital radiography (DR) images often have unclear contours and infiltration, which make it a challenging task for traditional segmentation models to get satisfactory segmentation results. To overcome this challenge, this paper has proposed an adaptive SPN segment...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7855681/ |
| _version_ | 1819163171204628480 |
|---|---|
| author | Dan Wang Junfeng Wang Yihua Du Peng Tang |
| author_facet | Dan Wang Junfeng Wang Yihua Du Peng Tang |
| author_sort | Dan Wang |
| collection | DOAJ |
| description | Solitary pulmonary nodules (SPN) in digital radiography (DR) images often have unclear contours and infiltration, which make it a challenging task for traditional segmentation models to get satisfactory segmentation results. To overcome this challenge, this paper has proposed an adaptive SPN segmentation model for DR images based on random walks segmentation and sequential filter. First, the SPN image is decomposed to get the cartoon component, which is used to acquire a set of seeds. Second, the seeds selection tactic is employed to optimize the scope of walking pixels and reduce the number of seeds, which could reduce the computational cost. Finally, we incorporate the sequential filter and construct the new representation of the weight and the probability matrices. In this paper, by using a data set of 724 SPN cases, the proposed method was tested and compared with four different models, and five kinds of evaluation indicators were given to evaluate the effect of segmentation. Experimental results indicate that the proposed method performs well on the blurred edge, as it could get relatively accurate results. |
| first_indexed | 2024-12-22T17:39:53Z |
| format | Article |
| id | doaj.art-208779ee24134d869a0a36bc99d1aa11 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T17:39:53Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-208779ee24134d869a0a36bc99d1aa112022-12-21T18:18:26ZengIEEEIEEE Access2169-35362017-01-0151460146810.1109/ACCESS.2017.26685237855681Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential FilterDan Wang0Junfeng Wang1https://orcid.org/0000-0003-1699-2270Yihua Du2Peng Tang3College of Computer Science, Sichuan University, Chengdu, ChinaCollege of Computer Science, Sichuan University, Chengdu, ChinaAffiliated Hospital of Southwest Medical University, Luzhou, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu, ChinaSolitary pulmonary nodules (SPN) in digital radiography (DR) images often have unclear contours and infiltration, which make it a challenging task for traditional segmentation models to get satisfactory segmentation results. To overcome this challenge, this paper has proposed an adaptive SPN segmentation model for DR images based on random walks segmentation and sequential filter. First, the SPN image is decomposed to get the cartoon component, which is used to acquire a set of seeds. Second, the seeds selection tactic is employed to optimize the scope of walking pixels and reduce the number of seeds, which could reduce the computational cost. Finally, we incorporate the sequential filter and construct the new representation of the weight and the probability matrices. In this paper, by using a data set of 724 SPN cases, the proposed method was tested and compared with four different models, and five kinds of evaluation indicators were given to evaluate the effect of segmentation. Experimental results indicate that the proposed method performs well on the blurred edge, as it could get relatively accurate results.https://ieeexplore.ieee.org/document/7855681/Image segmentationrandom walkssequential filterdigital radiographysolitary pulmonary nodule |
| spellingShingle | Dan Wang Junfeng Wang Yihua Du Peng Tang Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential Filter IEEE Access Image segmentation random walks sequential filter digital radiography solitary pulmonary nodule |
| title | Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential Filter |
| title_full | Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential Filter |
| title_fullStr | Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential Filter |
| title_full_unstemmed | Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential Filter |
| title_short | Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential Filter |
| title_sort | adaptive solitary pulmonary nodule segmentation for digital radiography images based on random walks and sequential filter |
| topic | Image segmentation random walks sequential filter digital radiography solitary pulmonary nodule |
| url | https://ieeexplore.ieee.org/document/7855681/ |
| work_keys_str_mv | AT danwang adaptivesolitarypulmonarynodulesegmentationfordigitalradiographyimagesbasedonrandomwalksandsequentialfilter AT junfengwang adaptivesolitarypulmonarynodulesegmentationfordigitalradiographyimagesbasedonrandomwalksandsequentialfilter AT yihuadu adaptivesolitarypulmonarynodulesegmentationfordigitalradiographyimagesbasedonrandomwalksandsequentialfilter AT pengtang adaptivesolitarypulmonarynodulesegmentationfordigitalradiographyimagesbasedonrandomwalksandsequentialfilter |