Tissue Recognition on Microscopic Images of Histological Sections Using Sequences of Zernike Moments
| Autor: | Ewa Skubalska-Rafajłowicz, Aneta Górniak |
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| Rok vydání: | 2018 |
| Předmět: |
Scale (ratio)
Contextual image classification Zernike polynomials business.industry Computer science Physics::Medical Physics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image processing Translation (geometry) 030218 nuclear medicine & medical imaging 03 medical and health sciences symbols.namesake 0302 clinical medicine Software Computer Science::Computer Vision and Pattern Recognition 030220 oncology & carcinogenesis symbols Artificial intelligence Invariant (mathematics) business Rotation (mathematics) |
| Zdroj: | Computer Information Systems and Industrial Management ISBN: 9783319999531 CISIM |
| DOI: | 10.1007/978-3-319-99954-8_2 |
| Popis: | In this paper, we propose an approach in microscopic image classification for histological sections of human tissues. The method is based on image descriptors composed of vectors of accumulated Zernike moments. The goal is to construct a robust and precise method of image recognition and classification that can be applied in the case of histological tissue samples. Thanks to their properties Zernike moments fit these requirements. Additionally, processed Zernike moments can be made scale, translation, and rotation invariant. In a series of experiments, we verify the effectiveness of the method and its application to the presented problem of medical image classification. The results are obtained with the help of predefined classifiers provided by dedicated software. The paper presents a comparison of results and proposes an example method of improving the approach. |
| Databáze: | OpenAIRE |
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