Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements.
Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements.
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DOI：
10.1364/boe.4.001104
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发表时间：
2013-07-01
影响因子：
3.4
通讯作者：
Conde OM
中科院分区：
文献类型：
--
作者：
Eguizabal A;Laughney AM;García-Allende PB;Krishnaswamy V;Wells WA;Paulsen KD;Pogue BW;Lopez-Higuera JM;Conde OM
利用局部反射测量的主成分分析（PCA）和独立成分分析（ICA）盲目识别乳腺肿瘤。不需要假设反射的特定理论模型，而由此产生的特征被证明具有乳腺病理的鉴别能力。对乳房肿瘤切除标本中的正常、良性和恶性乳腺组织类型进行离体成像，并提出了一种外科医生指导的系统校准方法，以克服盲分析的局限性。提出了一种简单、快速、线性的分类器，该分类器的诊断不需要训练信息。一组29例乳腺组织标本在鉴别良恶性病变时的敏感性为96%，特异性为95%。提出的PCA- ica混合组合增强了诊断辨别，提供了肿瘤概率图，中间PCA参数反映了组织光学特性。
Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.