Evaluation of the accuracy of cone-beam computed tomography image segmentation of isolated tooth roots based on the dynamic threshold method.

OBJECTIVE: Accurate quantification of the root surface area (RSA) plays a decisive role in the advancement of periodontal, orthodontic, and restorative treatment modalities. In this study, we aimed to develop a dynamic threshold-based computer-aided system for segmentation and calculation of the RSA of isolated teeth on cone-beam computed tomography (CBCT) and to assess the accuracy of the measured data. METHOD: We selected 24 teeth to be extracted, including single-rooted and multi-rooted teeth, from 22 patients who required tooth extraction. In the experimental group, we scanned 24 isolated teeth using CBCT with a voxel size of 0.3 mm. We designed a computer-aided system based on a personalized dynamic threshold algorithm to automatically segment the roots of 24 isolated teeth in CBCT images and calculate the RSA. In the control group, we employed digital intraoral scanner devices to perform optical scanning on 24 isolated teeth and subsequently manually segmented the roots using 3-matic software to calculate the RSA. We used the paired t-test (P < 0.05) and Bland-Altman plots to analyze the consistency of the two measurement methods. RESULTS: The results of the paired t-test showed that there was no significant difference in the RSAs obtained using the dynamic threshold method and the optical scanning image reconstruction (t = 1.005, P = 0.325 > 0.05). As per the Bland-Altman plot, the results were evenly distributed within the region of ± 1.96 standard deviations of the mean, with no increasing or decreasing trends and good consistency. CONCLUSION: In this study, we designed a computer-aided root segmentation system based on a personalized dynamic threshold algorithm to automatically segment the roots of isolated teeth in CBCT images with a voxel size of 0.3 mm. We found that the RSA calculated using this approach was highly accurate, and a voxel of 0.3 mm in size could accurately display the surface area data in CBCT images. Overall, our findings in this study provide a foundation for future work on accurate automatic segmentation of tooth roots in full-mouth CBCT images and the computation of RSA.

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions.

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers' personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.

Different methods, different results? Threshold-based versus conventional contouring techniques in clinical practice.

BACKGROUND: The quantitative differences of left and right ventricular (LV, RV) parameters of using different cardiac MRI (CMR) post-processing techniques and their clinical impact are less studied. We aimed to assess the differences and their clinical impact between the conventional contouring (CC) and the threshold-based (TB) methods using 70% and 50% thresholds in different hypertrabeculated conditions. METHODS: This retrospective study included 30 dilated cardiomyopathy, 30 left ventricular non-compaction (LVNC), 30 arrhythmogenic cardiomyopathy patients, 30 healthy athletes and 30 healthy volunteers. All participants underwent CMR imaging on 1.5 T. Cine sequences were used to derive measures of the cardiac volumes, function, total muscle mass (TMi) and trabeculae and papillary muscle mass (TPMi) using CC and TB segmentation methods. RESULTS: Comparing the CC and the 70% and 50% threshold TB methods, the LV and RV volumes were significantly lower, the ejection fraction (EF) and the TMi were significantly higher with the TB methods. Between the two threshold setups, only TPMi was significantly higher with the 70% threshold. Regarding the clinical benefits, the LVNC was the only group in whom all the diagnostic and therapeutic decisions and risk stratification were influenced using the TB method. Diagnostic changes occurred in three-quarters of the population, and all the cardiomyopathy groups were affected regarding the decision-making about pharmaco- and device therapy. CONCLUSIONS: Using the TB method, only TPMi was significantly higher with the 70% threshold than the 50% setup, and both of them differed significantly from the CC technique, with relevant clinical impacts in all patient groups.

Smartphone application for emergency signal detection.

Currently, a number of studies focus on the study and design of new healthcare technologies to improve elderly health and quality of life. Taking advantage of the popularity, portability, and inherent technology of smartphones, we present an emergency application for smartphones, designated as knock-to-panic (KTP). This innovative and novel system enables users to simply hit their devices in order to send an alarm signal to an emergency service. This application is a complete and autonomous emergency system, and can provide an economic, reliable, and unobtrusive method for elderly monitoring or safety protection. Moreover, the simple and fast activation of KTP makes it a viable and potentially superior alternative to traditional ambient assisted living emergency calls. Furthermore, KTP can be further extended to the general population as well and not just be limited for elderly persons. The proposed method is a threshold-based algorithm and is designed to require a low battery power consumption. The evaluation of the performance of the algorithm in collected data indicates that both sensitivity and specificity are above 90%.