SU-E-I-90: Fast and Robust Algorithm Towards Vessel Lumen and Stent Strut Detection in Optical Coherence Tomography.

PURPOSE

Optical Coherence Tomography (OCT) is a catheter-based imaging method that employs near-infrared light to produce high-resolution cross-sectional intravascular images. We propose a new segmentation technique for automatic lumen area extraction and stent strut detection in intravascular OCT images for the purpose of quantitative analysis of neointimal hyperplasia (NIH).

METHODS

Two clinical dataset of frequency-domainOCT scans of the human femoral artery were analyzed. First, a segmentation method based on Fuzzy C-Means (FCM) clustering and Wavelet Transform (WT) was applied towards inner luminal contour extraction. Subsequently, stent strut positions were detected by utilizing metrics derived from the local maxima of the wavelet transform into the FCM membership function.

RESULTS

The inner lumen contour and the position of stent strut were extracted with very high accuracy. Compared with manual segmentation by an expert physician, the automatic segmentation had an average overlap value of 0.917 ± 0.065 for all OCT images included in the study. The strut detection procedure successfully identified 6.7 ± 0.5 struts for each OCT image.

CONCLUSIONS

A new fast and robust automatic segmentation technique combining FCM and WT for lumen border extraction and strut detection in intravascular OCT images was designed and implemented. The proposed algorithm may be employed for automated quantitative morphological analysis of in-stent neointimal hyperplasia.