In this work we propose a comparative study between different descriptors in analysing histological images. In particular, our study is focused on measuring the accuracy of moments (Hu, Legendre, Zernike), Local Binary Patterns and co-occurrence matrices in classifying histological images. The experimentation has been conducted on well known public datasets: HistologyDS, Pap-smear, Lymphoma, Liver Aging Female, Liver Aging Male, Liver Gender AL and Liver Gender CR. The comparison results show that when combined with co-occurrence matrices and extracted from the RGB images, the orthogonal moments improve the classification performance considerably, imposing themselves as very powerful descriptors for histological image analysis.

Histological image analysis by invariant descriptors

DI RUBERTO, CECILIA;LODDO, ANDREA;PUTZU, LORENZO
2017-01-01

Abstract

In this work we propose a comparative study between different descriptors in analysing histological images. In particular, our study is focused on measuring the accuracy of moments (Hu, Legendre, Zernike), Local Binary Patterns and co-occurrence matrices in classifying histological images. The experimentation has been conducted on well known public datasets: HistologyDS, Pap-smear, Lymphoma, Liver Aging Female, Liver Aging Male, Liver Gender AL and Liver Gender CR. The comparison results show that when combined with co-occurrence matrices and extracted from the RGB images, the orthogonal moments improve the classification performance considerably, imposing themselves as very powerful descriptors for histological image analysis.
9783319685595
Classification; Co-occurence matrix; Local binary pattern; Medical image analysis; Moments; Texture descriptors; Theoretical computer science; Computer science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/227799
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