Abstract
The focus of this study was to develop an image-based algorithm for the catheter detection. Epicardial full-volume 3D echocardiography datasets were used as an input data of the algorithm. In total, 9 datasets consisted of 15 three-dimensional timeframes were processed. To correctly detect the catheter, the feature-based approach was applied to recognition the catheter within the 3D echocardiography datasets. The algorithm based on gray-level co-occurrence matrix (GLCM) was applied as a feature extraction technique. Once the GLCM was computed, we obtained correlation, contrast, homogeneity and energy features. Then we applied feature thresholds to the catheter detection. These thresholds were obtained using Support Vector Machine (SVM) with the linear kernel function and standardization the predictor data. The average segmentation and recognition accuracies of the algorithm equal 94.16% and 87.2% respectively. The processing time for one 2D slice and one 3D dataset are equal to 9±0.2 milliseconds and 1.96±0.045 seconds, respectively. Though the algorithm is not time-consuming for 2D mode, it is still complicated to apply it for real-time surgery in 3D mode.
Original language | English |
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Pages | 252-257 |
Number of pages | 6 |
Publication status | Published - 2018 |
Event | 28th International Conference on Computer Graphics and Vision, GraphiCon 2018 - Tomsk, Russian Federation Duration: 24 Sep 2018 → 27 Sep 2018 |
Conference
Conference | 28th International Conference on Computer Graphics and Vision, GraphiCon 2018 |
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Country | Russian Federation |
City | Tomsk |
Period | 24.9.18 → 27.9.18 |
Keywords
- Catheter recognition
- Echocardiography
- GLCM
- Texture analysis
- Tracking
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition