An optical approach for pigmented lesions detection in human skin is presented. As differences between normal skin tissue and pigmentation tissue (and even a change in pigmentation development) can be detected by their optical properties, this paper presents a new, potentially noninvasive approach for skin cancer detection. Since each wavelength has different penetration depth into the tissue and different absorption, the goal was to check whether a combination of information obtained from five different wavelengths, can increase the detection probability and reduce the false positive probability, compared to using only one wavelength. Temporal tracking of back-reflected secondary speckle patterns generated while illuminating the tested area with several lasers and applying periodic vibrations to the surface via a controlled vibration source at several stimulation frequencies. As a sequel to the previous work conducted in our laboratory which investigated pigmented lesions interaction with one light source, this work deals with increasing the number of parameters that are being looked at and considered at the same time. Using five wavelengths, 9 vibration frequencies and 19 signal analysis parameters, ex-vivo experiments were performed on porcine skin tissues and were analyzed using artificial intelligence tools which could detect the strong features for each wavelength individually. Combining the wavelengths produced impressive results compared to the results by each wavelength separately: both types of errors, false positive and false negative, decreased to less than 2%. Such a significant change in its impact on patients shows the value of this method. This paper shows the possibility of optically separating normal skin from pigmentation tissue, by using the advantages of multi-spectral optimization. This is a necessary proof of concept as a preliminary step toward our future experiments, which may differentiate between different types of pigmentation, and even malignancy and benign tissues.

中文翻译：

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使用机器学习进行组织探测的多光谱优化

提出了一种用于检测人体皮肤色素沉着病变的光学方法。由于正常皮肤组织和色素沉着组织之间的差异（甚至色素沉着发展的变化）可以通过它们的光学特性来检测，因此本文提出了一种新的、潜在的非侵入性皮肤癌检测方法。由于每个波长对组织的穿透深度不同，吸收也不同，因此目标是检查从五种不同波长获得的信息的组合，与仅使用一种波长相比，是否可以增加检测概率并降低误报概率。在用几个激光照射测试区域并通过几个刺激频率的受控振动源对表面施加周期性振动时产生的背反射二级散斑图案的时间跟踪。作为我们实验室之前进行的研究色素病变与一个光源相互作用的工作的续集，这项工作涉及增加同时观察和考虑的参数数量。使用五个波长、9 个振动频率和 19 个信号分析参数，对猪皮肤组织进行了离体实验，并使用人工智能工具进行了分析，该工具可以单独检测每个波长的强特征。与每个波长的结果相比，组合波长产生了令人印象深刻的结果：两种类型的错误，假阳性和假阴性，都减少到不到 2%。这种对患者影响的显着变化显示了这种方法的价值。本文展示了利用多光谱优化的优势将正常皮肤与色素沉着组织光学分离的可能性。这是一个必要的概念证明，作为我们未来实验的初步步骤，可以区分不同类型的色素沉着，甚至是恶性和良性组织。本文展示了利用多光谱优化的优势将正常皮肤与色素沉着组织光学分离的可能性。这是一个必要的概念证明，作为我们未来实验的初步步骤，可以区分不同类型的色素沉着，甚至是恶性和良性组织。本文展示了利用多光谱优化的优势将正常皮肤与色素沉着组织光学分离的可能性。这是一个必要的概念证明，作为我们未来实验的初步步骤，可以区分不同类型的色素沉着，甚至是恶性和良性组织。