Rapid automated diagnosis of diabetic peripheral neuropathy with in vivo corneal confocal microscopy

Purpose. To assess the diagnostic validity of a fully automated image analysis algorithm of in vivo confocal microscopy images in quantifying corneal subbasal nerves to diagnose diabetic neuropathy. Methods. One hundred eighty-six patients with type 1 and type 2 diabetes mellitus (T1/ T2DM) and 55 age-matched controls underwent assessment of neuropathy and bilateral in vivo corneal confocal microscopy (IVCCM). Corneal nerve fiber density (CNFD), branch density (CNBD), and length (CNFL) were quantified with expert, manual, and fully-automated analysis. The areas under the curve (AUC), odds ratios (OR), and optimal thresholds to rule out neuropathy were estimated for both analysis methods. Results. Neuropathy was detected in 53% of patients with diabetes. A significant reduction in manual and automated CNBD (P <0.001) and CNFD (P <0.0001), and CNFL (P <0.0001) occurred with increasing neuropathic severity. Manual and automated analysis methods were highly correlated for CNFD (r = 0.9, P <0.0001), CNFL (r = 0.89, P <0.0001), and CNBD (r = 0.75, P <0.0001). Manual CNFD and automated CNFL were associated with the highest AUC, sensitivity/specificity and OR to rule out neuropathy. Conclusions. Diabetic peripheral neuropathy is associated with significant corneal nerve loss detected with IVCCM. Fully automated corneal nerve quantification provides an objective and reproducible means to detect human diabetic neuropathy. © 2014 The Association for Research in Vision and Ophthalmology, Inc

Computation efficiency for core-based fingerprint recognition algorithm

This paper presents a new computationally efficient fingerprint algorithm for automatic recognition (CEFAR). The algorithm uses 41% of the original fingerprint image for recognition and reduces more than 60% of the computations for detecting the singularity point (SP). Analytical results have shown that the CEFAR maintains high accuracy over benchmark algorithms, EER less than 5% and average accuracy improvement is 337.7%, together with dramatic reduction in computation steps, leading to an efficient performance. CEFAR uses the Gabor filter to enhance the fingerprint image after performing pre-processing operations, such as segmentation and normalisation. Fingerprint features are then extracted with reference to the SP forming what is called the star structure using the Conditional Number concept that is applied to the skeleton of the fingerprint. This structure is invariant with respect to global rotations and translations on the fingerprint due to the consistency of its formation, which benefits the fingerprint matching procedure. For SP detection, core type was detected by using complex filtering applied to the orientation tensor field; this algorithm has been modified to reduce computational complexity, although it has a high accuracy performance, where results have shown that more than 95% of the SP's have been successfully detected

Computation efficiency for core-based fingerprint recognition algorithm

This paper presents a new computationally efficient fingerprint algorithm for automatic recognition (CEFAR). The algorithm uses 41% of the original fingerprint image for recognition and reduces more than 60% of the computations for detecting the singularity point (SP). Analytical results have shown that the CEFAR maintains high accuracy over benchmark algorithms, EER less than 5% and average accuracy improvement is 337.7%, together with dramatic reduction in computation steps, leading to an efficient performance. CEFAR uses the Gabor filter to enhance the fingerprint image after performing pre-processing operations, such as segmentation and normalisation. Fingerprint features are then extracted with reference to the SP forming what is called the star structure using the Conditional Number concept that is applied to the skeleton of the fingerprint. This structure is invariant with respect to global rotations and translations on the fingerprint due to the consistency of its formation, which benefits the fingerprint matching procedure. For SP detection, core type was detected by using complex filtering applied to the orientation tensor field; this algorithm has been modified to reduce computational complexity, although it has a high accuracy performance, where results have shown that more than 95% of the SP's have been successfully detected

Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy

Neuropad is currently a categorical visual screening test that identifies diabetic patients at risk of foot ulceration. The diagnostic performance of Neuropad was compared between the categorical and continuous (image-analysis (Sudometrics)) outputs to diagnose diabetic peripheral neuropathy (DPN). 110 subjects with type 1 and 2 diabetes underwent assessment with Neuropad, Neuropathy Disability Score (NDS), peroneal motor nerve conduction velocity (PMNCV), sural nerve action potential (SNAP), Deep Breathing-Heart Rate Variability (DB-HRV), intraepidermal nerve fibre density (IENFD), and corneal confocal microscopy (CCM). 46/110 patients had DPN according to the Toronto consensus. The continuous output displayed high sensitivity and specificity for DB-HRV (91%, 83%), CNFD (88%, 78%), and SNAP (88%, 83%), whereas the categorical output showed high sensitivity but low specificity. The optimal cut-off points were 90% for the detection of autonomic dysfunction (DB-HRV) and 80% for small fibre neuropathy (CNFD). The diagnostic efficacy of the continuous Neuropad output for abnormal DB-HRV (AUC: 91%, P=0.0003) and CNFD (AUC: 82%, P=0.01) was better than for PMNCV (AUC: 60%). The categorical output showed no significant difference in diagnostic efficacy for these same measures. An image analysis algorithm generating a continuous output (Sudometrics) improved the diagnostic ability of Neuropad, particularly in detecting autonomic and small fibre neuropathy