Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation

This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types. A multitude of efficient feature-based image retrieval methods already exist that can assign a query image to a certain image class. Visual annotations may help to increase the retrieval accuracy if combined with existing feature-based classification paradigms. Whereas with annotations we usually mean textual descriptions, in this paper barcode annotations are proposed. In particular, Radon barcodes (RBC) are introduced. As well, local binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test images is used to verify how barcodes could facilitate image retrieval.Comment: To be published in proceedings of The IEEE International Conference on Image Processing (ICIP 2015), September 27-30, 2015, Quebec City, Canad

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Clinical feasibility of quantitative ultrasound texture analysis: A robustness study using fetal lung ultrasound images

OBJECTIVES: To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool. METHODS: We compared, ranked, and validated the robustness of 5 texture-based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non-US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot-Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non-US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images. RESULTS: Three of the 5 methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) had favorably robust performance when using the non-US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification). CONCLUSIONS: Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool