Radon features and barcodes for medical image retrieval via SVM

For more than two decades, research has been performed on content-based image retrieval (CBIR). By combining Radon projections and the support vector machines (SVM), a content-based medical image retrieval method is presented in this work. The proposed approach employs the normalized Radon projections with corresponding image category labels to build an SVM classifier, and the Radon barcode database which encodes every image in a binary format is also generated simultaneously to tag all images. To retrieve similar images when a query image is given, Radon projections and the barcode of the query image are generated. Subsequently, the k-nearest neighbor search method is applied to find the images with minimum Hamming distance of the Radon barcode within the same class predicted by the trained SVM classifier that uses Radon features. The performance of the proposed method is validated by using the IRMA 2009 dataset with 14,410 x-ray images in 57 categories. The results demonstrate that our method has the capacity to retrieve similar responses for the correctly identified query image and even for those mistakenly classified by SVM. The approach further is very fast and has low memory requirement.

[1]  Krishna Mohan Buddhiraju,et al.  Representation of desert sand dunes by surface orientations using Radon transform , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[2]  M. R. Vant,et al.  Application Of Radon Transform Techniques To Wake Detection In Seasat-A SAR Images , 1990 .

[3]  Michael Kohnen,et al.  The IRMA code for unique classification of medical images , 2003, SPIE Medical Imaging.

[4]  T M Lehmann,et al.  Content-based Image Retrieval in Medical Applications , 2004, Methods of Information in Medicine.

[5]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[6]  Hayit Greenspan,et al.  Addressing the ImageClef 2009 Challenge Using a Patch-based Visual Words Representation , 2009, CLEF.

[7]  Shiv Ram Dubey,et al.  Local Diagonal Extrema Pattern: A New and Efficient Feature Descriptor for CT Image Retrieval , 2015, IEEE Signal Processing Letters.

[8]  Hamid R. Tizhoosh,et al.  Autoencoding the retrieval relevance of medical images , 2015, 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA).

[9]  Thomas Deselaers,et al.  A Content-Based Approach to Image Retrieval in Medical Applications , 2006 .

[10]  Rangaraj M. Rangayyan,et al.  Content-based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback , 2013, Content-based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback.

[11]  Bo Zhang,et al.  Support vector machine learning for image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[12]  Hsuan-Tien Lin,et al.  Unsupervised Semantic Feature Discovery for Image Object Retrieval and Tag Refinement , 2012, IEEE Transactions on Multimedia.

[13]  Hamid R. Tizhoosh,et al.  Barcode annotations for medical image retrieval: A preliminary investigation , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[14]  Balasubramanian Raman,et al.  Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval , 2015, J. Vis. Commun. Image Represent..

[15]  Hayit Greenspan,et al.  X-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words , 2011, IEEE Transactions on Medical Imaging.

[16]  Hyeran Byun,et al.  SVM-based salient region(s) extraction method for image retrieval , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[17]  Ton Kalker,et al.  A robust image fingerprinting system using the Radon transform , 2004, Signal Process. Image Commun..

[18]  Barbara Caputo,et al.  Overview of the CLEF 2009 Medical Image Annotation Track , 2009, CLEF.

[19]  Min Chen,et al.  Image database retrieval utilizing affinity relationships , 2003, MMDB '03.

[20]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.

[21]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[22]  Shinji Ozawa,et al.  A hierarchical approach for region-based image retrieval , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[23]  Mehrdad J. Gangeh,et al.  Tumour ROI estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[24]  J. Radon.  On the determination of functions from their integral values along certain manifolds , 1986, IEEE Transactions on Medical Imaging.

[25]  Fuzhen Zhuang,et al.  Heterogeneous Multi-task Semantic Feature Learning for Classification , 2015, CIKM.

[26]  Hao Su,et al.  Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification , 2010, NIPS.

[27]  Raimondo Schettini,et al.  Image annotation using SVM , 2003, IS&T/SPIE Electronic Imaging.

[28]  Hayit Greenspan,et al.  Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework , 2007, IEEE Transactions on Information Technology in Biomedicine.

[29]  Hamid R. Tizhoosh,et al.  Medical Image Classification via SVM Using LBP Features from Saliency-Based Folded Data , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[30]  Marco Wiering,et al.  2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2011, IJCNN 2011.