| Περίληψη: | Optical Coherence Tomography (OCT) is a catheter‐based imaging method
that employs near‐infrared light to produce high‐resolution cross sectional
intravascular images. Α new segmentation technique is implemented for automatic
lumen area extraction and stent strut detection in intravascular OCT images for the
purpose of quantitative analysis of neointimal hyperplasia (NIH). Also a graphical
user interface (GUI) is designed based on the employed algorithm.
Methods: Four clinical dataset of frequency‐domain OCT scans of the human
femoral artery were analysed. 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. Also the proposed method and all automatic
segmentation algorithms utilised in this thesis such as k‐means, FCM, MRF – ICM and
MRF – Metropolis were compared by means of mean distance difference in mm and
processing time in sec with the physician’s manual assessments.. The strut detection
procedure successfully identified 9.57 ± 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.
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