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首页> 外文会议>Conference on Optical Coherence tomography and coherence domain optical methods in biomedicine XXI >Textural Analysis of Optical Coherence Tomography Skin Images: quantitative differentiation between healthy and cancerous tissues
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Textural Analysis of Optical Coherence Tomography Skin Images: quantitative differentiation between healthy and cancerous tissues

机译：光学相干断层摄影皮肤图像的质地分析：健康癌组织之间的定量分化

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Optical Coherence Tomography (OCT) offers real-time high-resolution three-dimensional images of tissue microstructures. In this study, we used OCT skin images acquired from ten volunteers, neither of whom had any skin conditions addressing the features of their anatomic location. OCT segmented images are analyzed based on their optical properties (attenuation coefficient) and textural image features e.g., contrast, correlation, homogeneity, energy, entropy, etc. Utilizing the information and referring to their clinical insight, we aim to make a comprehensive computational model for the healthy skin. The derived parameters represent the OCT microstructural morphology and might provide biological information for generating an atlas of normal skin from different anatomic sites of human skin and may allow for identification of cell microstructural changes in cancer patients. We then compared the parameters of healthy samples with those of abnormal skin and classified them using a linear Support Vector Machines (SVM) with 82% accuracy.
机译：光学相干断层扫描（OCT）提供组织微观结构的实时高分辨率三维图像。在这项研究中，我们使用了来自十个志愿者的OCT皮肤图像，既不是谁都有任何皮肤状况，用于解决其解剖位置的特征。通过例如光学性质（衰减系数）和纹理图像特征，例如，利用信息和参考其临床洞察力的对比度，相关性，均匀性，能量，熵等来分析OCT分段图像。我们的目标是制作综合计算模型对于健康的皮肤。衍生的参数表示OCT微结构形态，可以提供来自人体皮肤不同解剖位点的生成正常皮肤的地图的生物学信息，并且可以识别癌症患者的细胞微观结构变化。然后，将健康样品的参数与异常皮肤的参数进行比较，并使用线性支持向量机（SVM）分类为82％的精度。

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