Methodology development algorithms for processing and analysis of optical coherence tomography images (O.C.T.)

• Κύριος συγγραφέας: Μανδελιάς, Κωνστασταντίνος
• Άλλοι συγγραφείς: Καγκάδης, Γεώργιος
• Μορφή: Thesis
• Γλώσσα: English
• Έκδοση: 2014
• Θέματα: Optical coherence tomography (Ο.C.Τ.); Wavelets; Οπτική συνεκτική τομογραφία (Ο.C.Τ.); Κυματίδια; Fuzzy c-means; 616.075 45

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.