TY - GEN
T1 - Autoencoding the retrieval relevance of medical images
AU - Çamlica, Zehra
AU - Tizhoosh, H. R.
AU - Khalvati, Farzad
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/28
Y1 - 2015/12/28
N2 - Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a n/p/n autoencoder (p < n). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.
AB - Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a n/p/n autoencoder (p < n). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.
UR - http://www.scopus.com/inward/record.url?scp=84963811277&partnerID=8YFLogxK
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U2 - 10.1109/IPTA.2015.7367208
DO - 10.1109/IPTA.2015.7367208
M3 - Conference contribution
AN - SCOPUS:84963811277
T3 - 5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015
SP - 550
EP - 555
BT - 5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015
A2 - Jennane, Rachid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015
Y2 - 10 November 2015 through 13 November 2015
ER -