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首页> 外文会议>Biomedical applications of light scattering VII >Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction
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Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction

机译：线性分类器和光学散射图像的纹理分析用于乳腺癌提取过程中的肿瘤分类

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Texture analysis of light scattering in tissue is proposed to obtain diagnostic information from breast cancer specimens. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology to guide the surgeon in resection surgeries. The usability of scatter signatures acquired with a micro-sampling reflectance spectral imaging system was improved utilizing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. Co-occurrence analysis is then applied to the scattering power images to extract the textural features. A statistical analysis of the features demonstrated the suitability of the autocorrelation for the classification of not-malignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue, since it reveals morphological information of tissue. Non-malignant tissue shows higher autocorrelation values while adipose tissue presents a very low autocorrelation on its scatter texture, being malignant the middle ground. Consequently, a fast linear classifier based on the consideration of just one straightforward feature is enough for providing relevant diagnostic information. A leave-one-out validation of the linear classifier on 29 samples with 48 regions of interest showed classification accuracies of 98.74% on adipose tissue, 82.67% on non-malignant tissue and 72.37% on malignant tissue, in comparison with the biopsy H&E gold standard. This demonstrates that autocorrelation analysis of scatter signatures is a very computationally efficient and automated approach to provide pathological information in real-time to guide surgeon during tissue resection.
机译：提出了组织中光散射的纹理分析，以从乳腺癌标本中获得诊断信息。光散射测量是微创的，可以估计组织形态以指导外科医师进行切除手术。利用微采样反射光谱成像系统获取的散射特征的可用性得到了改善，这是通过对Mie理论的经验近似来估计每个像素的散射功率。然后将共现分析应用于散射功率图像以提取纹理特征。对特征的统计分析表明，自相关可用于分类非恶性（正常上皮和间质，良性上皮和间质，炎症），恶性（DCIS，IDC，ILC）和脂肪组织，因为它揭示了形态学信息组织。非恶性组织显示出较高的自相关值，而脂肪组织的散布纹理呈现出非常低的自相关性，中间为恶性。因此，仅考虑一个简单特征的快速线性分类器就足以提供相关的诊断信息。与活检H＆E金相比，线性分类器在29个具有48个感兴趣区域的样本上的一劳永逸验证证实了脂肪组织的分类准确度为98.74％，非恶性组织的分类准确度为82.67％，恶性组织的分类准确度为72.37％。标准。这表明散点图的自相关分析是一种非常有效的计算方法，并且是一种自动化的方法，可以实时提供病理信息，以在组织切除过程中指导外科医生。

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