An Advanced Quantitative Echosound Methodology for Femoral Neck Densitometry.

The aim of this paper was to investigate the clinical feasibility and the accuracy in femoral neck densitometry of the Osteoporosis Score (O.S.), an ultrasound (US) parameter for osteoporosis diagnosis that has been recently introduced for lumbar spine applications. A total of 377 female patients (aged 61-70 y) underwent both a femoral dual X-ray absorptiometry (DXA) and an echographic scan of the proximal femur. Recruited patients were sub-divided into a reference database used for ultrasound spectral model construction and a study population for repeatability assessments and accuracy evaluations. Echographic images and radiofrequency signals were analyzed through a fully automatic algorithm that performed a series of combined spectral and statistical analyses, providing as a final output the O.S. value of the femoral neck. Assuming DXA as a gold standard reference, the accuracy of O.S.-based diagnoses resulted 94.7%, with k = 0.898 (p < 0.0001). Significant correlations were also found between O.S.-estimated bone mineral density and corresponding DXA values, with r(2) up to 0.79 and root mean square error = 5.9-7.4%. The reported accuracy levels, combined with the proven ease of use and very good measurement repeatability, provide the adopted method with a potential for clinical routine application in osteoporosis diagnosis.

Unsupervised eye pupil localization through differential geometry and local self-similarity matching.

The automatic detection and tracking of human eyes and, in particular, the precise localization of their centers (pupils), is a widely debated topic in the international scientific community. In fact, the extracted information can be effectively used in a large number of applications ranging from advanced interfaces to biometrics and including also the estimation of the gaze direction, the control of human attention and the early screening of neurological pathologies. Independently of the application domain, the detection and tracking of the eye centers are, currently, performed mainly using invasive devices. Cheaper and more versatile systems have been only recently introduced: they make use of image processing techniques working on periocular patches which can be specifically acquired or preliminarily cropped from facial images. In the latter cases the involved algorithms must work even in cases of non-ideal acquiring conditions (e.g in presence of noise, low spatial resolution, non-uniform lighting conditions, etc.) and without user's awareness (thus with possible variations of the eye in scale, rotation and/or translation). Getting satisfying results in pupils' localization in such a challenging operating conditions is still an open scientific topic in Computer Vision. Actually, the most performing solutions in the literature are, unfortunately, based on supervised machine learning algorithms which require initial sessions to set the working parameters and to train the embedded learning models of the eye: this way, experienced operators have to work on the system each time it is moved from an operational context to another. It follows that the use of unsupervised approaches is more and more desirable but, unfortunately, their performances are not still satisfactory and more investigations are required. To this end, this paper proposes a new unsupervised approach to automatically detect the center of the eye: its algorithmic core is a representation of the eye's shape that is obtained through a differential analysis of image intensities and the subsequent combination with the local variability of the appearance represented by self-similarity coefficients. The experimental evidence of the effectiveness of the method was demonstrated on challenging databases containing facial images. Moreover, its capabilities to accurately detect the centers of the eyes were also favourably compared with those of the leading state-of-the-art methods.

EIT forward problem parallel simulation environment with anisotropic tissue and realistic electrode models.

Electrical impedance tomography (EIT) is an imaging technology based on impedance measurements. To retrieve meaningful insights from these measurements, EIT relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of current flows therein. The nonhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeoff between physical accuracy and technical feasibility, which at present severely limits the capabilities of EIT. This work presents a complete algorithmic flow for an accurate EIT modeling environment featuring high anatomical fidelity with a spatial resolution equal to that provided by an MRI and a novel realistic complete electrode model implementation. At the same time, we demonstrate that current graphics processing unit (GPU)-based platforms provide enough computational power that a domain discretized with five million voxels can be numerically modeled in about 30 s.