Abbe School of Photonics, Friedrich-Schiller University Jena, Germany; Leibniz-Institute of Photonic Technology (IPHT) Jena e.v., Albert-Einstein-Str. 9, 07745 Jena, Germany.
Leibniz-Institute of Photonic Technology (IPHT) Jena e.v., Albert-Einstein-Str. 9, 07745 Jena, Germany.
Comput Med Imaging Graph. 2015 Jul;43:36-43. doi: 10.1016/j.compmedimag.2015.02.010. Epub 2015 Mar 7.
Coherent anti-Stokes Raman scattering (CARS) microscopy is a powerful tool for fast label-free tissue imaging, which is promising for early medical diagnostics. To facilitate the diagnostic process, automatic image analysis algorithms, which are capable of extracting relevant features from the image content, are needed. In this contribution we perform an automated classification of healthy and tumor areas in CARS images of basal cell carcinoma (BCC) skin samples. The classification is based on extraction of texture features from image regions and subsequent classification of these regions into healthy and cancerous with a perceptron algorithm. The developed approach is capable of an accurate classification of texture types with high sensitivity and specificity, which is an important step towards an automated tumor detection procedure.
相干反斯托克斯拉曼散射（CARS）显微镜是一种快速无标记组织成像的强大工具，有望用于早期医学诊断。为了促进诊断过程，需要自动图像分析算法，这些算法能够从图像内容中提取相关特征。在本贡献中，我们对基底细胞癌（BCC）皮肤样本的 CARS 图像中的健康和肿瘤区域进行了自动分类。分类是基于从图像区域提取纹理特征，并随后使用感知器算法将这些区域分类为健康和癌变。所开发的方法能够以高灵敏度和特异性对纹理类型进行准确分类，这是实现自动肿瘤检测程序的重要步骤。
