This thesis contributes to knowledge by developing algorithms that automatically detect and quantify structures of clinical interest in reflectance confocal microscopy (RCM) images, captured in-vivo and from excised skin tissue. The first part of the thesis presents an algorithm that detects the dermalepidermal junction (DEJ), characterised by papillae, in cubes of RCM images of in-vivo skin. A cube of images is a number of mosaic images captured at different depths parallel to the skin surface. A classifier, which makes use of texture and anatomical-based features was designed. The anatomical-based features are parameters that quantify the absence and presence of papillae across different images of the cube. The second part of the thesis analyses RCM images of excised tissue collected during Mohs surgery. These tissue samples include basal cell carcinoma (BCC) and non-diseased samples. An algorithm was developed to differentiate between (i) cancerous regions, (ii) regions of inflammation, and (iii) non-diseased regions. A classifier based on texture and nuclei-concentration features was designed. The nuclei concentration in cancerous sites is different from that in nondiseased sites and thus can be used to distinguish the two. The third part of the thesis analyses RCM video sequences of in-vivo skin imaged at the level of the DEJ. The boundaries of superficial skin capillaries can be delineated by visually observing the highly reflective red blood cells (RBCs) passing through the capillaries. An algorithm that automatically detects skin capillaries in RCM video sequences was developed. Additionally, an algorithm that quantifies the velocity of RBCs in cross-sectionally imaged capillaries is devised. The change in total capillary area (per unit frame area), individual capillary parameters and RBC velocity due to incremental ultra-violet radiation (UVR) doses are analysed in both fair and dark skinned volunteers. The work presented in this thesis has the potential to increase the acceptance of RCM in the dermatology clinic, both for diagnosis and for assessing treatment response of skin conditions located at (or above) the DEJ. Additionally, the thesis enhances the potential of using RCM images of excised samples instead of preparing the tissue for histological examinations during surgery.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:718559 |
| Date | January 2017 |
| Creators | Damato, Elaine |
| Contributors | Penney, Graeme Patrick ; Coleman, Andy |
| Publisher | King's College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://kclpure.kcl.ac.uk/portal/en/theses/automated-analysis-in-reflectance-confocal-microscopy-images-of-skin-anatomy-and-pathologies(6bff8c67-0b66-4f5e-9f3e-7e885df92977).html |
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