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In this work, ex-vivo tissue samples of 32 human aortic rings including aneurysm, atherosclerosis, aortic stenosis, aortic insufficiency, and bicuspid aortic valve diseases were obtained from surgical interventions. Healthy aortic specimens were considered as controls when excised from transplant donors undergoing non-aortic related pathologies.
The aim is to co-register measurements from HSI (HyperSpectral Imaging) and OCT (Optical Coherence Tomography) imaging modalities, obtaining maps of chemical composition and morphological structure, being able track changes at each point of the tissue sample in approximately 100 cm2 of the inner aortic wall. These samples have been imaged ex-vivo using wide-field HSI, in the SWIR (1000-1700 nm) ranges, and OCT. OCT was used to generate attenuation coefficient maps of tissue specimens. Additionally, HSI was used to estimate elastin, collagen, lipid and water content of the samples.
An inversely proportional relationship has been observed between the aorta’s diameter and their attenuation coefficient. Furthermore, an increase in the mean squared error of the spectral fitting has been noted in pathological samples. This study underscores the potential of integrating HSI and OCT for the advanced characterization and early diagnosis of complex aortic diseases, highlighting their critical role in improving patient outcomes.
Automatic pigmented lesion segmentation through a dermoscopy-guided OCT approach for early diagnosis
With actual methods, the delineation of lesion margins directly on OCT images during early stages of the disease is still really challenging and, at the same time, relevant from a prognosis perspective. This work proposes combining DD and OCT images to take advantage of their complementary information. The goal is to guide lesions delineation on OCT images considering the clinical features on DD images. The developed method applies image processing techniques to DD image to automatically segment the lesion; later, and after a calibration procedure, DD and OCT images become coregistered. In a final step the DD segmentation is transferred into the OCT image. Applying advanced image processing techniques and the proposed strategy of lesion delimitation, histopathological characteristics of the segmented lesion can be studied on OCT images afterwards. This proposal can lead to early, real-time and non-invasive diagnosis of pigmented lesions.
Arc-welding defect detection by means of principal component analysis and artificial neural networks
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