TY - GEN
T1 - MinMax radon barcodes for medical image retrieval
AU - Tizhoosh, H. R.
AU - Zhu, Shujin
AU - Lo, Hanson
AU - Chaudhari, Varun
AU - Mehdi, Tahmid
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is “Radon barcodes” that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to “local thresholding” scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over thresholded Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.
AB - Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is “Radon barcodes” that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to “local thresholding” scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over thresholded Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.
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U2 - 10.1007/978-3-319-50835-1_55
DO - 10.1007/978-3-319-50835-1_55
M3 - Conference contribution
AN - SCOPUS:85007048908
SN - 9783319508344
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 617
EP - 627
BT - Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings
A2 - Bebis, George
A2 - Parvin, Bahram
A2 - Skaff, Sandra
A2 - Iwai, Daisuke
A2 - Boyle, Richard
A2 - Koracin, Darko
A2 - Porikli, Fatih
A2 - Scheidegger, Carlos
A2 - Entezari, Alireza
A2 - Min, Jianyuan
A2 - Sadagic, Amela
A2 - Isenberg, Tobias
PB - Springer Verlag
T2 - 12th International Symposium on Visual Computing, ISVC 2016
Y2 - 12 December 2016 through 14 December 2016
ER -