GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images
| Autor: | Anvit Mangal, Shiv Gehlot, Anubha Gupta, Rahul Duggal, Nisarg Thakkar, Lalit Kumar, Devprakash Satpathy, Ritu Gupta |
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| Rok vydání: | 2020 |
| Předmět: |
Staining and Labeling
Radiological and Ultrasound Technology Computer science Normalization (image processing) Color Health Informatics Geometry Computer Graphics and Computer-Aided Design Stain 030218 nuclear medicine & medical imaging Color vector 03 medical and health sciences 0302 clinical medicine Neoplasms Image Processing Computer-Assisted Humans Tissue type Radiology Nuclear Medicine and imaging Computer Vision and Pattern Recognition Invariant (mathematics) Coloring Agents 030217 neurology & neurosurgery |
| Zdroj: | Medical Image Analysis. 65:101788 |
| ISSN: | 1361-8415 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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