Development of Image Processing Algorithm for Skin OCT Imaging (II)

With the development of medical science, optical techniques are of particular importance in the medical field, and offer the therapeutic and diagnostic potentials. At present, the diagnosis of skin tumor often adopt skin biopsy technique in which a skin lesion is removed and sent to a pathologist to render a microscopic diagnosis. There are a lot of clinical experts are committed to the development of non-invasive methods such as ultrasound, dermoscopy, optical coherence tomography (OCT), confocal microscopy, photodynamic diagnosis technology, and so on. Especially OCT imaging technology can provide micrometer-resolution, high sensitivity and non-contact depth resolved cross-sectional images of biological samples, and is suitable for dermatology. However, hairs tended to create underlying shadow artifacts, and in sebaceous areas the many hair follicles and sebaceous glands tended to obscure normal layering. In skin cancer, cancer cell islands can mimic a hair follicle and make the ability using OCT to identify the location of skin cancer is greatly reduced. Therefore, how to combine the knowledge of image processing to analyze OCT images have become the research topics of optical image. In addition, collagen in damaged skin tissue will be re-generated during the period of postoperative recovery. Therefore, if we can focus on quantitative studies of regenerated collagen protein, it can be used as an important index of postoperative recovery process. In this project, we plan to develop a comprehensive, automatic OCT image processing computer-aid diagnosis (CAD) platform. Combined removable optical tomography system with multi-imaging technology developed by other sub-projects and verified by clinicians, to construct an appropriate overall quantitative tool that provides dermatologists to diagnose skin tumor and improve treatment at the earliest possible time. This tool can not only effectively track the recovery situation of patient after surgery but achieve the applied goal integrate clinical and engineering. At the present stage, we have already tried to develop initial image enhancement algorithms, automated skin layer segmentation procedure, and vessel segmentation algorithm for OCT images. Next, we will optimize the algorithms of OCT image enhancement and vessel segmentation to increase the adaptability and accuracy for different types of skin cancer. In addition, we will undertake the exploitation of three-dimensional OCT image reconstruction techniques to build 3D visualization module that can enhance the visibility and correlations between vessels and tumor. Finally, we will integrate fruitful results and focus on regions of angiogenesis and collagen regeneration by using the OCT image information to obtain quantitative information for establishing multiple quantitative indexes as a basis for clinical evaluation. This system we proposed can not only assist skin science, imaging science, and researchers in biomedical optics in acquiring more information but also establish the correlations between multiple types of information. The research results can facilitate the search for the unknown clinical questions.

Project ID:PB10501-3724
External Project ID:MOST104-2221-E182-023-MY2