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Event-driven visual sensors have attracted interest from a number of different research communities. They provide visual information in quite a different way from conventional video systems consisting of sequences of still images rendered at a given "frame rate." Event-driven vision sensors take inspiration from biology. Each pixel sends out an event(More)
This article investigates the suitability of local intensity distributions to analyze six emphysema classes in 342 CT scans obtained from 16 sites hosting scanners by 3 vendors and a total of 9 specific models in subjects with Chronic Obstructive Pulmonary Disease (COPD). We propose using kernel density estimation to deal with the inherent sparsity of local(More)
We propose a new method to detect microcalcifications in mammograms. The method is based on region growing with pre-filtering and a seed selection procedure based on two-dimensional linear prediction error. The procedures are designed to reduce false positives, to improve detection capability, and to reduce computational time. When compared to a previous(More)
Address-event representation (AER) is an emergent hardware technology which shows a high potential for providing in the near future a solid technological substrate for emulating brain-like processing structures. When used for vision, AER sensors and processors are not restricted to capturing and processing still image frames, as in commercial frame-based(More)
In this paper we introduce the importance of scale invariance in properly discriminating some of the typical patterns found in melanocytic lesions, by dermatoscopic image analysis. Pattern discrimination is a necessary step before pattern irregularity (an indicator of malignancy) can be quantified. We propose a set of features that allows for the(More)
OBJECTIVE A virtual reality tool, called VirSSPA, was developed to optimize the planning of surgical processes. METHODS Segmentation algorithms for Computed Tomography (CT) images: a region growing procedure was used for soft tissues and a thresholding algorithm was implemented to segment bones. The algorithms operate semiautomati- cally since they only(More)
In this paper we propose a fully self-assessed adaptive region growing airway segmentation algorithm. We rely on a standardized and self-assessed region-based approach to deal with varying imaging conditions. Initialization of the algorithm requires prior knowledge of trachea location. This can be provided either by manual seeding or by automatic trachea(More)
BACKGROUND The diagnosis of neuromuscular diseases is strongly based on the histological characterization of muscle biopsies. However, this morphological analysis is mostly a subjective process and difficult to quantify. We have tested if network science can provide a novel framework to extract useful information from muscle biopsies, developing a novel(More)