Curvilinear Detector Research Articles

Omnidirectional stretchability of freestanding interconnects for stretchable electronics

Advanced stretchable electronics applications, such as health monitoring patches, high-density curvilinear detectors etc, not only require the interconnects in these devices to accommodate large uniaxial strains vvvvbut they should also be able to withstand complex multiaxial loading such as in-plane and out-of-plane shear loading for reliable and optimal device operation. However, only uniaxial stretchability of these stretchable interconnects is typically characterised and reported in the literature. Here, we compare three relatively similar and relatively simple freestanding interconnect geometries on the basis of their multiaxial stretchability to evaluate their feasibility for typical stretchable electronics applications. The three geometries include: (1) the (traditional) serpentine structure, that has been studied most extensively in the literature (2) the rotation out-of-plane elongation (ROPE) interconnect, designed to exploit buckling and out-of-plane rotation to enhance stretchability (Shafqat et al 2017 Micromachines 8 277) and the (3) the non-buckling in-plane elongation (IPE) interconnect, designed to fit a maximum number of beam members within the footprint area. To evaluate the multiaxial stretchability of the three designs finite element (FE) simulations were performed by loading the interconnects in the xy-, yz- and xz- planes under a range of loading angles. Furthermore, multiaxial loading experiments were performed on the ROPE interconnect samples by loading the samples in uniaxial in-plane stretching, in-plane shear and out-of-plane shear loading cases and compared with the FE simulations.

Automatic determination of aortic compliance based on MRI and adapted curvilinear detector

Abstract Parameters of aortic elasticity, such as aortic compliance or aortic distensibility, can be estimated from cine-MRI through the knowledge of the aortic contour on each image. In this context, a completely automatic method for the measurement of aortic elasticity is proposed in this study, and compared with previously published methods which are not fully automatic. An adaptation of a curvilinear region detector was used for the aortic wall detection over the entire cardiac cycle, to completely automatically evaluate the aortic stiffness in a pilot study including 40 volunteers. Near circular regions were detected (ascending and descending aorta cross-sections) in each image of the sequence using robust scale-space based method with removing of false positives using probabilistic approach. Robustness against noise was studied and an evaluation of area estimation was performed. A comparison between manual segmentation by two experts is provided on whole images from the patient dataset. The global mean relative errors for the area are 2.83 ± 1.88% and 1.44 ± 1.52% for the ascending and descending aorta, respectively. The global means of the Dice's coefficient are 0.97 ± 0.01 for the ascending aorta and 0.97 ± 0.01 for the descending aorta. These values are high and very stable. Finally, the Bland–Altman plots for compliance and distensibility values show good agreements between our method and experts, with a mean of difference always close to zero, and a low standard deviation. Then the proposed tool allows a precise and accurate automatic measurement of aortic stiffness from cine-MRI and can be applied in clinical practice.

Automatic analysis of diabetic peripheral neuropathy using multi-scale quantitative morphology of nerve fibres in corneal confocal microscopy imaging

Diabetic peripheral neuropathy (DPN) is one of the most common long term complications of diabetes. Corneal confocal microscopy (CCM) image analysis is a novel non-invasive technique which quantifies corneal nerve fibre damage and enables diagnosis of DPN. This paper presents an automatic analysis and classification system for detecting nerve fibres in CCM images based on a multi-scale adaptive dual-model detection algorithm. The algorithm exploits the curvilinear structure of the nerve fibres and adapts itself to the local image information. Detected nerve fibres are then quantified and used as feature vectors for classification using random forest (RF) and neural networks (NNT) classifiers. We show, in a comparative study with other well known curvilinear detectors, that the best performance is achieved by the multi-scale dual model in conjunction with the NNT classifier. An evaluation of clinical effectiveness shows that the performance of the automated system matches that of ground-truth defined by expert manual annotation.

Robust and Accurate Surface Measurement Using Structured Light

This paper proposes a robust and accurate method for measuring 3-D surfaces using a binocular system. To eliminate the effect caused by the distortion of projector lens, each structured light sheet is fitted to a conicoid. A curvilinear detector, which combines the zero-crossing detection algorithm with Steger's detector, is employed to detect the subpixel locations of the light stripes, thus preventing the Steger's curvilinear detector from failing to detect them in the endpoints of the stripes. The proposed coding method combines the information of the linked line with the gray code to avoid producing outliers caused by erroneous decoding and make the coding procedure more robust. Experiments showed that each structured light sheet fitted to a conicoid can effectively improve the measurement accuracy. The subpixel detection method can detect the exact subpixel locations of the stripes. Likewise, the encoding strategy results in the production of fewer outliers, while the reconstruction result becomes more perfect.