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    1. Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography

      Automatic vessel lumen segmentation and stent strut detection in intravascular 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. The authors propose a 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: A clinical dataset of frequency-domain OCT scans of the human femoral artery was analyzed. First, a segmentation method based on the Markov random field (MRF) model was employed for lumen area identification. Second, textural and edge information derived from local intensity distribution and continuous wavelet transform (CWT) analysis were integrated ...
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    2. Diagnostic efficacy of computer extracted image features in optical coherence tomography of the precancerous cervix

      Diagnostic efficacy of computer extracted image features in optical coherence tomography of the precancerous cervix
      Purpose: To determine the diagnostic efficacy of optical coherence tomography (OCT) to identify cervical intraepithelial neoplasia (CIN) grade 2 or higher by computer-aided diagnosis (CADx). Methods: OCT has been investigated as a screening/diagnostic tool in the management of preinvasive and early invasive cancers of the uterine cervix. In this study, an automated algorithm was developed to extract OCT image features and identify CIN 2 or higher. First, the cervical epithelium was detected by a combined watershed and active contour method. Second, four features were calculated: The thickness of the epithelium and its standard deviation and the contrast between the ...
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