SBILab, Department of ECE, IIIT-Delhi, New Delhi, 110020, India.
SBILab, Department of ECE, IIIT-Delhi, New Delhi, 110020, India.
Med Image Anal. 2020 Oct;65:101788. doi: 10.1016/j.media.2020.101788. Epub 2020 Jul 21.
Stain normalization of microscopic images is the first pre-processing step in any computer-assisted automated diagnostic tool. This paper proposes Geometry-inspired Chemical-invariant and Tissue Invariant Stain Normalization method, namely GCTI-SN, for microscopic medical images. The proposed GCTI-SN method corrects for illumination variation, stain chemical, and stain quantity variation in a unified framework by exploiting the underlying color vector space's geometry. While existing stain normalization methods have demonstrated their results on a single tissue and stain type, GCTI-SN is benchmarked on three cancer datasets of three cell/tissue types prepared with two different stain chemicals. GCTI-SN method is also benchmarked against the existing methods via quantitative and qualitative results, validating its robustness for stain chemical and cell/tissue type. Further, the utility and the efficacy of the proposed GCTI-SN stain normalization method is demonstrated diagnostically in the application of breast cancer detection via a CNN-based classifier.
显微镜图像的染色归一化是任何计算机辅助自动诊断工具的第一步预处理步骤。本文提出了基于几何的化学不变量和组织不变量染色归一化方法，即 GCTI-SN，用于显微镜医学图像。所提出的 GCTI-SN 方法通过利用底层颜色向量空间的几何形状，在统一的框架内纠正照明变化、染色化学和染色量变化。虽然现有的染色归一化方法已经在单一组织和染色类型上展示了它们的结果，但 GCTI-SN 是在三个用两种不同染色化学物质制备的三种癌细胞/组织类型的癌症数据集上进行基准测试的。GCTI-SN 方法还通过定量和定性结果与现有方法进行了基准测试，验证了其对染色化学和细胞/组织类型的鲁棒性。此外，通过基于 CNN 的分类器在乳腺癌检测中的应用，演示了所提出的 GCTI-SN 染色归一化方法在诊断上的实用性和有效性。
