A novel privacy-supporting 2-class classification technique for brain MRI images

• Autorzy: Devi Swagatika , Sahoo Manmath Narayan , Bakshi Sambit

Identyfikatory

DOI

10.1016/j.bbe.2020.05.005

• Języki publikacji: EN

Abstrakty

EN

Developing automated Computer Aided Diagnosis (CAD) framework for assisting radiologists in a fast and effective classification of brain Magnetic Resonance (MR) images is of great importance, given plausible usage of Electronic Health Records (EHR) in healthcare system. This work aims at proposing two novel privacy supporting classifiers for automatic segregation of brain MR images. To ensure privacy, our article employs a spatial steganographic approach to hide patients sensitive health information in brain images itself. Proposed methods employ Discrete Wavelet Transform (DWT) for extracting relevant features from original and stego images. Subsequently, Symmetrical Uncertainty Ranking (SUR) and Probabilistic Principal Components Analysis (PPCA) are used to obtain a reduced feature vector for Support Vector Machine (SVM) and Filtered Classifier (FC) respectively. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate this work, the proposed schemes are experimented on both original and stego brain MR images and are compared against eight state-of-the-art classification techniques with respect to six standard parameters. The results reveal that the proposed techniques are robust and scalable with respect to the size of the datasets. Moreover, it is concluded that applying steganographic algorithm on brain MR images yield equally satisfactory classification rate.

Słowa kluczowe

EN

computer aided diagnosis; electronic health records; discrete wavelet transform; probabilistic principal components analysis; privacy preservation; MR image classification

PL

diagnoza wspomagana komputerowo; elektroniczna dokumentacja medyczna; dyskretna transformata falkowa; analiza składowych głównych; rezonans magnetyczny

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 1022--1035
• Opis fizyczny: Bibliogr. 64 poz., rys., tab., wykr.

Twórcy

autor

Devi Swagatika

• Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha , India

autor

Sahoo Manmath Narayan

• Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha , India

autor

Bakshi Sambit

• Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha 769008, India

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).