1,720,961 research outputs found
An Automatic Segmentation Method for MRI Multiparametric Volumes
No full textPurpose 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
An Optimal and Automatic Graph Cut Method for Biomedical Images Using Compactness Measure
No full textThis work aims to achieve an automatic and optimal graph cut phase based on a segmentation method presented in a previous paper. A graph-based segmentation algorithm, starting from a seed point belonging to the region of interest (ROI), is able to find the Minimum Path Spanning Tree (MPST) by using a new cost function and an optimal aggregation criterion. In order to extract the ROI, a graph-cut of the obtained tree is absolutely necessary.
By definition, the main drawback of the graph-based segmentation methods is the loss of spatial and contextual information. To overcome this problem, a new method based on compactness measure is here proposed The present approach is applied to the biomedical field, considering Magnetic Resonance Imaging (MRI) volumes of the hand and neurological districts
Semi-Automatic Segmentation of Multiple Sclerosis Lesion in 4D Modality
No full textThe automatic and computerized recognition of Regions of Interest (ROI) is a crucial step in the process and analysis of medical images. The reasons are many and include the increase of available medical image data, the wide variety of devices and methods for image acquisition and the need to provide mechanisms making the analysis more accurate and the clinicians’ job faster. Within the study on multiple sclerosis, the goal is the recognition of the
damaged brain areas by processing images captured through magnetic resonance imaging. In this context, the proposed work is a study on the relationship between brain images obtained by magnetic resonance imaging, using different types of acquisitions. The goal is to understand whether it is somehow possible to identify the different regions of the brain, through a process of segmentation, using a method which allows the user’s independence. The employed volumes are acquired in three different modalities T1-weighted, T2-weighted, and PD for synthetic database; T1-weighted, T2-weighted and FLAIR for real database. The purpose of this paper is to provide the doctor with a tool helping with diagnosis and detecting the possible areas of doubt. Two databases were taken into account, a synthetic one and a real one, and for the synthetic database the parameters of the confusion matrix have been calculated
Automatic MPST-cut for segmentation of carpal bones from MR volumes
No full textIn the context of rheumatic diseases, several studies suggest that Magnetic Resonance Imaging (MRI) allows the detection of the three main signs of Rheumatoid Arthritis (RA) at higher sensitivities than available through conventional radiology. The rapid, accurate segmentation of bones is an essential preliminary step for quantitative diagnosis, erosion evaluation, and multi-temporal data fusion. In the present paper, a new, semi-automatic, 3D graph-based segmentation method to extract carpal bone data is proposed. The method is unsupervised, does not employ any a priori model or knowledge, and is adaptive to the individual variability of the acquired data. After selecting one source point inside the Region of Interest (ROI), a segmentation process is initiated, which consists of two automatic stages: a cost-labeling phase and a graph-cutting phase. The algorithm finds optimal paths based on a new cost function by creating a Minimum Path Spanning Tree (MPST). To extract. the region, a cut of the obtained tree is necessary. A new criterion of the MPST-cut based on compactness shape factor was conceived and developed.The proposed approach is applied to a large database of 96 Ti-weighted MR bone volumes. Performance quality is evaluated by comparing the results with gold-standard bone volumes manually defined by rheumatologists through the computation of metrics extracted from the confusion matrix. Furthermore, comparisons with the existing literature are carried out. The results show that this method is efficient and provides satisfactory performance for bone segmentation on low-field MR volumes
Infrastructure for data management and user centered rehabilitation in Rehab@Home project
No full textIn 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
Technical Concept of Health Data Collection and Integration Data Analysis for Gaining Meaningful Medical Information
No full textThe collection of data for therapeutic purposes when using Serious Games in a home environment is essential to help therapists and medical doctors deliver better therapy. It is necessary to use different sensors for collecting such data. This requires using standards as HL71 as much as possible to provide a general purpose approach. On the other hand, the collected data needs an extensive evaluation and interpretation and tools are required to provide the therapist and medical doctor with meaningful information. In this chapter we offer a closer look to standards and propose tools for the analysis of collected data for later use by therapists and medical doctors as a result of the Rehab@Home project
Optimizing and Evaluating a Graph-Based Segmentation of MRI Wrist Bones
No full textIn 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
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