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Journal of Imaging Informatics in Medicine

Erschienen in:

24.08.2017

Medical Image Retrieval Using Multi-Texton Assignment

verfasst von: Qiling Tang, Jirong Yang, Xianfu Xia

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 1/2018

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Abstract

In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance.

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Titel: Medical Image Retrieval Using Multi-Texton Assignment
verfasst von: Qiling Tang
Jirong Yang
Xianfu Xia
Publikationsdatum: 24.08.2017
Verlag: Springer International Publishing
Erschienen in: Journal of Imaging Informatics in Medicine / Ausgabe 1/2018
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-017-0017-z

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Medical Image Retrieval Using Multi-Texton Assignment

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