In this paper, a quantitative evaluation of the graph-based segmentation method presented in a previous work is performed. The algorithm, starting from a single source element belonging to a region of interest, aims at finding the optimal path minimizing a new cost function for all elements of a digital volume. The method is an adaptive, unsupervised, and semi-automatic approach.For the assessment, a training phase and a testing phase are considered. The system is able to learn and adapt to the ground truth. The performance of the method is estimated by computing classical indices from the confusion matrix, similarity measures, and distance measures.Our work is based on the segmentation and 3D reconstructions of carpal bones derived from Magnetic Resonance Imaging (MRI) volumetric data of patients affected by rheumatic diseases.
In this paper, we describe the Rehab@Home Operational Infrastructure which functioning essentially relies on the acquisition, processing, exchange and interpretation of a large set of heterogeneous data and information. These data are coming from existing clinical data records, rehabilitation workflow structure, user-system interaction, and explicit user feedback, basic information about expected and actual rehabilitation progress, biophysical sensors, ambient and contextual sensors. What in a more precise and detailed way has been described and analyzed is the specification and development of data protocol and data integration devoted to the acquisition, processing, exchange and interpretation of a large set of heterogeneous data and information coming from biophysical sensors, ambient and contextual sensors, existing clinical data records. It has been carried a study of user profiling and personalization, which will be exploited to adapt process and services with the aim of enhancing user satisfaction. Thanks to personalization of the user-system interaction, the explicit user feedback, the basic information about expected and actual rehabilitation progress are made available in the best way. Case-based reasoning further improves the extraction of useful information from a single patient and from compared analysis. Identification of the most relevant risk factors related to the rehabilitation process and the monitoring of the whole rehabilitation process was another field of study
Abstract-Purpose of this work is the design and implementation of an automated method for digital volume segmentation, based on multi-parametric densities, fuzzy topology, and adaptive growth mechanism. The processing objective is the global segmentation of the digital volume, that is its partitioning into significant connected subsets, in a fully automatic way. The main advantage consists in the very nature of the algorithm that enables the automatic segmentation by running an iterative process that adapts to the volume at hand and does not require any user intervention. The designed method can be applied to multi-parametric volumes where different characteristics are available to analyze the same target. The robustness of the method has been evaluated and verified through statistical parameters, that will be discussed below, after application on volumes of biomedical images obtained through Magnetic Resonance Imaging.
IndexTerms-Segmentation, fuzzy processing, connectedness, multi-parametric data fusion.
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