The histomorphologic and passive biomechanical properties were studied in the mid-colon of 16 non-diabetic and 20 streptozotocin (STZ)-induced diabetic rats (50 mg/kg STZ, ip). The diabetic rats were divided into groups living 4 and 8 weeks after the induction of diabetes (n = 10 for each group). The mechanical test was a ramp distension of fluid into the colon in vitro. The colon diameter and length were obtained from digitized images of the segments at pre-selected pressures and at the no-load and zero-stress states. Circumferential and longitudinal stresses and strains were computed from the length, diameter, and pressure data and from the zero-stress state geometry. The blood glucose level increased 3-4-fold in the diabetic rats compared with the controls (P < 0.001). Diabetes generated pronounced increases in the colon weight per length, wall thickness, and wall cross-sectional area (P < 0.001). Histologically, the thickness of all layers was increased during diabetes (P < 0.05), especially the mucosa layer. The opening angle, and absolute values of residual strain increased in the diabetic group (P < 0.05 and P < 0.01, respectively). Furthermore, diabetes increased the circumferential and longitudinal stiffness of the colon wall (P < 0.001). The observed changes in residual strain, opening angle, and stress-strain relation may be contributing factors to colonic dysfunction and abdominal pain in diabetic patients.
In order to satisfy the requirements of the cancelable biometrics construct, cancelable biometrics techniques rely on other authentication factors such as password keys and/or user specific tokens in the transformation process. However, such multi-factor authentication techniques suffer from the same issues associated with traditional knowledge-based and token-based authentication systems. This paper presents a new one-factor cancelable biometrics scheme for protecting IrisCodes. The proposed method is based solely on IrisCodes; however, it satisfies the requirements of revocability, diversity and noninvertibility without deteriorating the recognition performance. Moreover, the transformation process is easy to implement and can be integrated simply with current iris matching systems. The impact of the proposed transformation process on the the recognition accuracy is discussed and its noninvertibility is analyzed. The effectiveness of the proposed method is confirmed experimentally using CASIA-IrisV3-Interval dataset.
Using a hyperspectral camera and extraction algorithm, the tongue color of the uncoated part was automatically extracted. This technique is suitable for tongue color analysis and may help non-trained users to identify "Mibyou".
SUMMARYDespite their usability advantages over traditional authentication systems, biometrics-based authentication systems suffer from inherent privacy violation and non-revocability issues. In order to address these issues, the concept of cancelable biometrics was introduced as a means of generating multiple, revocable, and noninvertible identities from true biometric templates. Apart from BioHashing, which is a two-factor cancelable biometrics technique based on mixing a set of tokenized userspecific random numbers with biometric features, cancelable biometrics techniques usually cannot preserve the recognition accuracy achieved using the unprotected biometric systems. However, as the employed token can be lost, shared, or stolen, BioHashing suffers from the same issues associated with token-based authentication systems. In this paper, a reliable tokenless cancelable biometrics scheme, referred to as BioEncoding, for protecting IrisCodes is presented. Unlike BioHashing, BioEncoding can be used as a one-factor authentication scheme that relies only on sole IrisCodes. A unique noninvertible compact bit-string, referred to as BioCode, is randomly derived from a true IrisCode. Rather than the true IrisCode, the derived BioCode can be used efficiently to verify the user identity without degrading the recognition accuracy obtained using original IrisCodes. Additionally, BioEncoding satisfies all the requirements of the cancelable biometrics construct. The performance of BioEncoding is compared with the performance of BioHashing in the stolen-token scenario and the experimental results show the superiority of the proposed method over BioHashing-based techniques.
Abstract. In this paper, we present a liver segmentation approach. In which, the relation between neighboring slices in CT images is utilized to estimate shape and statistical information of the liver. This information is then integrated with the graph cuts algorithm to segment the liver in each CT slice. This approach does not require prior models construction, and it uses single phase CT images; even so, it is talented to deal with complex shape and intensity variations. Moreover, it eliminates the burdens associated with model construction like data collection, manual segmentation, registration, and landmark correspondence. In contrast, it requires a low user interaction to determine the liver landmarks on a single CT slice only. The proposed approach has been evaluated on 10 CT images with several liver abnormalities, including tumors and cysts, and it achieved high average scores of 81.7 using MICCAI-2007 Grand Challenge scoring system. Compared to contemporary approaches, our approach requires significantly less interaction and processing time.
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