Automatic classification of colorectal and prostatic histologic tumor images using multiscale multispectral local binary pattern texture features and stacked generalization

Peyret, Remy, Bouridane, Ahmed, Khelifi, Fouad, Tahir, Muhammad Atif and Al-Maadeed, Somaya (2017) Automatic classification of colorectal and prostatic histologic tumor images using multiscale multispectral local binary pattern texture features and stacked generalization. Neurocomputing. ISSN 0925-2312 (In Press)

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Official URL: https://doi.org/10.1016/j.neucom.2017.05.010

Abstract

This paper proposes a new multispectral multiscale local binary pattern feature extraction method for automatic classification of colorectal and prostatic tumor biopsies samples. A multilevel stacked generalization classification technique is also proposed and the key idea of the paper considers a grade diagnostic problem rather than a simple malignant versus tumorous tissue problem using the concept of multispectral imagery in both the visible and near infrared spectra. To validate the proposed algorithm performances, a comparative study against related works using multispectral imagery is conducted including an evaluation on three different multiclass datasets of multispectral histology images: two representing images of colorectal biopsies - one dataset was acquired in the visible spectrum while the second captures near-infrared spectra. The proposed algorithm achieves an accuracy of 99.6% on the different datasets. The results obtained demonstrate the advantages of infrared wavelengths to capture more efficiently the most discriminative information. The results obtained show that our proposed algorithm outperforms other similar methods.

Item Type: Article
Uncontrolled Keywords: Multiscale Multispectral Local Binary Pattern; Stacked generalization; Histology; Colorectal cancer; Prostate cancer; Automatic diagnosis
Subjects: B800 Medical Technology
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 12 Jun 2017 10:29
Last Modified: 20 Jun 2017 17:14
URI: http://nrl.northumbria.ac.uk/id/eprint/31063

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