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This study provides normative values for HRU, and it suggests that further research with age- and gender-specific distributions must be a key priority in the development of HRU for use as a diagnostic test for peripheral nerve diseases.

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Image matching based on local invariant features is crucial for many photogrammetric and remote sensing applications such as image registration and image mosaicking. In this paper, a novel local feature descriptor named adaptive binning scale-invariant feature transform (AB-SIFT) for fully automatic remote sensing image matching that is robust to local geometric distortions is proposed. The main idea of the proposed method is an adaptive binning strategy to compute the local feature descriptor. The proposed descriptor is computed on a normalized region defined by an improved version of the prominent Hessian affine feature extraction algorithm called the uniform robust Hessian affine algorithm. Unlike common distribution-based descriptors, the proposed descriptor uses an adaptive histogram quantization strategy for both location and gradient orientations, which is robust and actually resistant to a local viewpoint distortion and extremely increases the discriminability and robustness of the final AB-SIFT descriptor. In addition to the SIFT descriptor, the proposed adaptive quantization strategy can be easily extended for other distribution-based descriptors. Experimental results on both synthetic and real image pairs show that the proposed AB-SIFT matching method is more robust and accurate than state-of-the-art methods, including the SIFT, DAISY, the gradient location and orientation histogram, the local intensity order pattern, and the binary robust invariant scale keypoint. Index Terms-Adaptive binning scale-invariant feature transform (AB-SIFT), image matching, local feature descriptor, uniform robust Hessian affine (URHA).0196-2892 where he is also a member of the Center of Excellence for Geospatial Information Technology. His research interests include digital photogrammetry, information extraction from highresolution satellite and aerial imagery, and the integration of photogrammetry and geospatial information systems.

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Peripheral nerves are enlarged diffusely in diabetic patients, including sites not susceptible to bony compression. The number of enlarged CSA values can help predict the presence of DSP. Muscle Nerve, 2016 Muscle Nerve 55: 171-178, 2017.

OBJECTIVEMild demyelination may contribute more to the pathophysiology of nerve fiber injury in diabetic sensorimotor polyneuropathy (DSP) than previously thought. We investigated the clinical and electrodiagnostic classifications of nerve injury in diabetic patients to detect evidence of conduction slowing in DSP.RESEARCH DESIGN AND METHODSType 1 diabetic subjects (n = 62) and type 2 diabetic subjects (n = 111) with a broad spectrum of DSP underwent clinical examination and nerve conduction studies (NCS). Patients were classified as having axonal (group A), conduction slowing (group D), or combined (group C) DSP based on electrodiagnostic criteria. Patients with chronic immune-mediated neuropathies were not included. The groups were compared using ANOVA, contingency tables, and Kruskal-Wallis analyses.RESULTSOf the 173 type 1 and type 2 diabetic subjects with a mean age of 59.1 ± 13.6 years and hemoglobin A1c (HbA1c) of 8.0 ± 1.8% (64 ± 19.7 mmol/mol), 46% were in group A, 32% were in group D, and 22% were in group C. The severity of DSP increased across groups A, D, and C, respectively, based on clinical and NCS parameters. The mean HbA1c for group D subjects (8.9 ± 2.3% [74 ± 25.1 mmol/mol]) was higher than for group A and group C subjects (7.7 ± 1.4% [61 ± 15.3 mmol/mol] and 7.5 ± 1.3% [58 ± 14.2 mmol/mol]; P = 0.003), and this difference was observed in those with type 1 diabetes.CONCLUSIONSThe presence of conduction slowing in patients with suboptimally controlled type 1 diabetes indicates the possibility that this stage of DSP may be amenable to intervention via improved glycemic control.