OBJECTIVETo individuate a novel sex-specific index, based on waist circumference, BMI, triglycerides, and HDL cholesterol, indirectly expressing visceral fat function.RESEARCH DESIGN AND METHODSVisceral adiposity index (VAI) was first modeled on 315 nonobese healthy subjects. Using two multiple logistic regression models, VAI was retrospectively validated in 1,498 primary care patients in comparison to classical cardio- and cerebrovascular risk factors.RESULTSAll components of metabolic syndrome increased significantly across VAI quintiles. VAI was independently associated with both cardiovascular (odd ratio [OR] 2.45; 95% CI 1.52–3.95; P < 0.001) and cerebrovascular (1.63; 1.06–2.50; P = 0.025) events. VAI also showed significant inverse correlation with insulin sensitivity during euglycemic-hyperinsulinemic clamp in a subgroup of patients (Rs = −0.721; P < 0.001). By contrast, no correlations were found for waist circumference and BMI.CONCLUSIONSOur study suggests VAI is a valuable indicator of “visceral adipose function” and insulin sensitivity, and its increase is strongly associated with cardiometabolic risk.

The basic aim of a biometric identification system is\ud to discriminate automatically between subjects in a reliable and\ud dependable way, according to a specific-target application. Multimodal\ud biometric identification systems aim to fuse two or more\ud physical or behavioral traits to provide optimal False Acceptance\ud Rate (FAR) and False Rejection Rate (FRR), thus improving system\ud accuracy and dependability. In this paper, an innovative multimodal\ud biometric identification system based on iris and fingerprint\ud traits is proposed. The paper is a state-of-the-art advancement\ud of multibiometrics, offering an innovative perspective on features\ud fusion. In greater detail, a frequency-based approach results in\ud a homogeneous biometric vector, integrating iris and fingerprint\ud data. Successively, a hamming-distance-based matching algorithm\ud deals with the unified homogenous biometric vector. The proposed\ud multimodal system achieves interesting results with several commonly\ud used databases. For example, we have obtained an interesting\ud working point with FAR = 0% and FRR = 5.71% using\ud the entire fingerprint verification competition (FVC) 2002 DB2B\ud database and a randomly extracted same-size subset of the BATH\ud database. At the same time, considering the BATH database and\ud the FVC2002 DB2A database, we have obtained a further interesting\ud working point with FAR = 0% and FRR = 7.28% ÷ 9.7%

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Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C-Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area-based and distance-based metrics, obtaining the following average values: Similarity Index=95.59%, Jaccard Index=91.86%, Sensitivity=97.39%, Specificity=94.30%, Mean Absolute Distance=0.246[pixels], Maximum Distance=1.050[pixels], and Hausdorff Distance=1.365[pixels]

Uterine fibroids are benign tumors that can affect female patients during reproductive years. Magnetic resonance-guided focused ultrasound (MRgFUS) represents a noninvasive approach that uses thermal ablation principles to treat symptomatic fibroids. During traditional treatment planning, uterus, fibroids, and surrounding organs at risk must be manually marked on MR images by an operator. After treatment, an operator must segment, again manually, treated areas to evaluate the non-perfused volume (NPV) inside the fibroids. Both pre- and post-treatment procedures are time-consuming and operator-dependent. This paper presents a novel method, based on an advanced direct region detection model, for fibroid segmentation in MR images to address MRgFUS post-treatment segmentation issues. An incremental procedure is proposed: split-and-merge algorithm results are employed as multiple seed-region selections by an adaptive region growing procedure. The proposed approach segments multiple fibroids with different pixel intensity, even in the same MR image. The method was evaluated using area-based and distance-based metrics and was compared with other similar works in the literature. Segmentation results, performed on 14 patients, demonstrated the effectiveness of the proposed approach showing a sensitivity of 84.05 %, a specificity of 92.84 %, and a speedup factor of 1.56× with respect to classic region growing implementations (average values).