Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern

International audienceUltrasound in regional anesthesia (RA) has increased in pop-ularity over the last years. The nerve localization presents a key step for RA practice, it is therefore valuable to develop a tool able to facilitate this practice. The nerve detection in the ultrasound images is a challeng-ing task, since the noise and other artifacts corrupt the visual properties of such kind of tissue. In this paper we propose a new method to address this problem. The proposed technique operates in two steps. As the me-dian nerve belongs to a hyperechoic region, the first step consists in the segmentation of this type of region using the k-means algorithm. The second step is more critical; it deals with nerve structure detection in noisy data. For that purpose, a new descriptor is developed. It combines tow methods median binary pattern (MBP) and Gabor filter to obtain the median Gabor binary pattern (MGBP). The method was tested on 173 ultrasound images of the median nerve obtained from three patients. The results showed that the proposed approach achieves better accuracy than the original MBP, Gabor descriptor and other popular descriptors

Topological descriptors for 3D surface analysis

We investigate topological descriptors for 3D surface analysis, i.e. the classification of surfaces according to their geometric fine structure. On a dataset of high-resolution 3D surface reconstructions we compute persistence diagrams for a 2D cubical filtration. In the next step we investigate different topological descriptors and measure their ability to discriminate structurally different 3D surface patches. We evaluate their sensitivity to different parameters and compare the performance of the resulting topological descriptors to alternative (non-topological) descriptors. We present a comprehensive evaluation that shows that topological descriptors are (i) robust, (ii) yield state-of-the-art performance for the task of 3D surface analysis and (iii) improve classification performance when combined with non-topological descriptors.Comment: 12 pages, 3 figures, CTIC 201

Classifying and Grouping Mammography Images into Communities Using Fisher Information Networks to Assist the Diagnosis of Breast Cancer

© 2020, Springer Nature Switzerland AG. The aim of this paper is to build a computer based clinical decision support tool using a semi-supervised framework, the Fisher Information Network (FIN), for visualization of a set of mammographic images. The FIN organizes the images into a similarity network from which, for any new image, reference images that are closely related can be identified. This enables clinicians to review not just the reference images but also ancillary information e.g. about response to therapy. The Fisher information metric defines a Riemannian space where distances reflect similarity with respect to a given probability distribution. This metric is informed about generative properties of data, and hence assesses the importance of directions in space of parameters. It automatically performs feature relevance detection. This approach focusses on the interpretability of the model from the standpoint of the clinical user. Model predictions were validated using the prevalence of classes in each of the clusters identified by the FIN

Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population

Dementia is a devastating disease, and has severe implications on affected individuals, their family and wider society. A growing body of literature is studying the association of retinal microvasculature measurement with dementia. We present a pilot study testing the strength of groups of conventional (semantic) and texture-based (non-semantic) measurements extracted from retinal fundus camera images to classify patients with and without dementia. We performed a 500-trial bootstrap analysis with regularized logistic regression on a cohort of 1,742 elderly diabetic individuals (median age 72.2). Age was the strongest predictor for this elderly cohort. Semantic retinal measurements featured in up to 81% of the bootstrap trials, with arterial caliber and optic disk size chosen most often, suggesting that they do complement age when selected together in a classifier. Textural features were able to train classifiers that match the performance of age, suggesting they are potentially a rich source of information for dementia outcome classification

Augmented reality applications for cultural heritage using Kinect

AbstractThis paper explores the use of data from the Kinect sensor for performing augmented reality, with emphasis on cultural heritage applications. It is shown that the combination of depth and image correspondences from the Kinect can yield a reliable estimate of the location and pose of the camera, though noise from the depth sensor introduces an unpleasant jittering of the rendered view. Kalman filtering of the camera position was found to yield a much more stable view. Results show that the system is accurate enough for in situ augmented reality applications. Skeleton tracking using Kinect data allows the appearance of participants to be augmented, and together these facilitate the development of cultural heritage applications.</jats:p

Protein crystallization analysis on the World Community Grid

We have developed an image-analysis and classification system for automatically scoring images from high-throughput protein crystallization trials. Image analysis for this system is performed by the Help Conquer Cancer (HCC) project on the World Community Grid. HCC calculates 12,375 distinct image features on microbatch-under-oil images from the Hauptman-Woodward Medical Research Institute’s High-Throughput Screening Laboratory. Using HCC-computed image features and a massive training set of 165,351 hand-scored images, we have trained multiple Random Forest classifiers that accurately recognize multiple crystallization outcomes, including crystals, clear drops, precipitate, and others. The system successfully recognizes 80% of crystal-bearing images, 89% of precipitate images, and 98% of clear drops

Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans

Author's accepted manuscript.Available from 18/06/2021.acceptedVersio

A distributed optimization method for the geographically distributed data centres problem

The geographically distributed data centres problem (GDDC) is a naturally distributed resource allocation problem. The problem involves allocating a set of virtual machines (VM) amongst the data centres (DC) in each time period of an operating horizon. The goal is to optimize the allocation of workload across a set of DCs such that the energy cost is minimized, while respecting limitations on data centre capacities, migrations of VMs, etc. In this paper, we propose a distributed optimization method for GDDC using the distributed constraint optimization (DCOP) framework. First, we develop a new model of the GDDC as a DCOP where each DC operator is represented by an agent. Secondly, since traditional DCOP approaches are unsuited to these types of large-scale problem with multiple variables per agent and global constraints, we introduce a novel semi-asynchronous distributed algorithm for solving such DCOPs. Preliminary results illustrate the benefits of the new method