A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Novel feature descriptor based on microscopy image statistics
Tekijät: Bayramoglu N, Kannala J, Akerfelt M, Kaakinen M, Eklund L, Nees M, Heikkila J
Konferenssin vakiintunut nimi: IEEE International Conference on Image Processing (ICIP)
Kustannuspaikka: Quebec City, CANADA
Julkaisuvuosi: 2015
Journal: IEEE International Conference on Image Processing
Kokoomateoksen nimi: 2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Tietokannassa oleva lehden nimi: 2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Lehden akronyymi: IEEE IMAGE PROC
Aloitussivu: 2695
Lopetussivu: 2699
Sivujen määrä: 5
ISBN: 978-1-4799-8339-1
ISSN: 1522-4880
DOI: https://doi.org/10.1109/ICIP.2015.7351292
Tiivistelmä
In this paper, we propose a novel feature description algorithm based on image statistics. The pipeline first performs independent component analysis on training image patches to obtain basis vectors (filters) for a lower dimensional representation. Then for a given image, a set of filter responses at each pixel is computed. Finally, a histogram representation, which considers the signs and magnitudes of the responses as well as the number of filters, is applied on local image patches. We propose to apply this idea to a microscopy image pixel identification system based on a learning framework. Experimental results show that the proposed algorithm performs better than the state-of-the-art descriptors in biomedical images of different microscopy modalities.
In this paper, we propose a novel feature description algorithm based on image statistics. The pipeline first performs independent component analysis on training image patches to obtain basis vectors (filters) for a lower dimensional representation. Then for a given image, a set of filter responses at each pixel is computed. Finally, a histogram representation, which considers the signs and magnitudes of the responses as well as the number of filters, is applied on local image patches. We propose to apply this idea to a microscopy image pixel identification system based on a learning framework. Experimental results show that the proposed algorithm performs better than the state-of-the-art descriptors in biomedical images of different microscopy modalities.
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