Structure-based Similarity Measures Research Articles

Ultrasound 2D strain measurement for arm lymphedema using deformable registration: A feasibility study.

PurposeLymphedema, a swelling of the extremity, is a debilitating morbidity of cancer treatment. Current clinical evaluation of lymphedema is often based on medical history and physical examinations, which is subjective. In this paper, the authors report an objective, quantitative 2D strain imaging approach using a hybrid deformable registration to measure soft-tissue stiffness and assess the severity of lymphedema.MethodsThe authors have developed a new 2D strain imaging method using registration of pre- and post-compression ultrasound B-mode images, which combines the statistical intensity- and structure-based similarity measures using normalized mutual information (NMI) metric and normalized sum-of-squared-differences (NSSD), with an affine-based global and B-spline-based local transformation model. This 2D strain method was tested through a series of experiments using elastography phantom under various pressures. Clinical feasibility was tested with four participants: two patients with arm lymphedema following breast-cancer radiotherapy and two healthy volunteers.ResultsThe phantom experiments have shown that the proposed registration-based strain method significantly increased the signal-to-noise and contrast-to-noise ratio under various pressures as compared with the commonly used cross-correlation-based elastography method. In the pilot study, the strain images were successfully generated for all participants. The averaged strain values of the lymphedema affected arms were much higher than those of the normal arms.ConclusionsThe authors have developed a deformable registration-based 2D strain method for the evaluation of arm lymphedema. The initial findings are encouraging and a large clinical study is warranted to further evaluate this 2D ultrasound strain imaging technology.

Combined frequency and spatial domain-based patch propagation for image completion

Film and photography archives now have an accelerated rate of degradation. Because the preservation of cultural heritage plays an important role in our society, photograph and film restoration has recently drawn a substantial amount of attention. In this paper, an approach that involves exemplar-based inpainting, aimed at determining patch priority and patch matching, is proposed. Different image regions have different levels of importance for vision perception; hence, a priority score must be assigned. Patch priority is calculated by the energy of its distributed cosine transform coefficients (DCT term) and the edge term. The edge term prioritizes the edge patches and the energy of the DCT coefficients of the patch is used as a discriminator for patches with similar edge terms. Patch inpainting is performed by assessing the similarity between the patches in such a manner that the similarity measure is consistent with human visual judgment. Therefore, a structure-based similarity measure is developed. Further, the interpolated missing pixels at the patch are also considered for applying the structure-based patch matching criteria in finding the candidate patch. Experimental results on damaged digitized photographs and natural images are presented, which demonstrate the effectiveness of the image completion framework for tasks such as scratch/text, object removal and image inpainting.

Retrieval, alignment, and clustering of computational models based on semantic annotations

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Integrating multi-attribute similarity networks for robust representation of the protein space

A global view of the protein space is essential for functional and evolutionary analysis of proteins. In order to achieve this, a similarity network can be built using pairwise relationships among proteins. However, existing similarity networks employ a single similarity measure and therefore their utility depends highly on the quality of the selected measure. A more robust representation of the protein space can be realized if multiple sources of information are used. We propose a novel approach for analyzing multi-attribute similarity networks by combining random walks on graphs with Bayesian theory. A multi-attribute network is created by combining sequence and structure based similarity measures. For each attribute of the similarity network, one can compute a measure of affinity from a given protein to every other protein in the network using random walks. This process makes use of the implicit clustering information of the similarity network, and we show that it is superior to naive, local ranking methods. We then combine the computed affinities using a Bayesian framework. In particular, when we train a Bayesian model for automated classification of a novel protein, we achieve high classification accuracy and outperform single attribute networks. In addition, we demonstrate the effectiveness of our technique by comparison with a competing kernel-based information integration approach.