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Automatic stent segmentation in IOCT images using combined feature extraction techniques and mathematical morphology
| Content Provider | Semantic Scholar |
|---|---|
| Author | Moraes, Matheus Cardoso Cárdenas, Diego Armando Cardona Furuie, Sergio Shiguemi |
| Copyright Year | 2013 |
| Abstract | Atherosclerosis causes millions of deaths and billions in expenses worldwide. Intravascular Optical Coherence Tomography (IOCT) is an intravascular imaging modality, used in coronary visualization and neo-intima post stent re-stenosis investigation. Segmentation is important for the re-obstruction quantification, improving the overall procedures. As IOCT is relatively new, few fully automatic stent segmentation works can be found in the literature. Since IOCT provides hundreds of images, non-automatic segmentation procedures may be an arduous task. Consequently, we present a fully automatic stent segmentation methodology, based on a combination of contrast stretching; wavelet decompositions as Feature Extraction; and morphological reconstruction used as post-processing so as to select and improve the previous obtained information. The evaluation was performed by segmenting 160 images from pig coronaries, containing a variety of stent disposition; hence, the outcomes were compared with their corresponding gold standards. The final results led to: True Positive (%) = 93.35±6.49, and False Positive (%) = 8.05±11.6.. The outcome provided accurate values; in addition, it is a complete automatic approach. |
| Starting Page | 1215 |
| Ending Page | 1218 |
| Page Count | 4 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cinc.org/archives/2013/pdf/1215.pdf |
| Alternate Webpage(s) | http://cinc.mit.edu/archives/2013/pdf/1215.pdf |
| Journal | Computing in Cardiology 2013 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
