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Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation, and boundary finding, often suffer from a variety of limitations. Here the authors propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. The authors' approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity. A number of experiments were performed both on synthetic and real medical images of the brain and heart to evaluate the new approach, and it is shown that the integrated method typically performs better when compared to conventional gradient-based deformable boundary finding. Further, this method yields these improvements with little increase in computational overhead, an advantage derived from the application of the Green's theorem.

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We consider here the problem of detecting changes in the status of switching devices, circuit breakers in particular, in distribution networks. The lack of measurements in distribution networks compared to transmission networks is the main challenge of this problem. Using expected values of power consumption, and their variance, we are able to quickly calculate the confidence level of identifying the correct topology, or the current status of switching devices, using any given configuration of real time measurements. This allows to compare between different configurations in order to select the optimal one. The main approach we propose relies on approximating the measurements as normal distributed random variables, and applying the maximum likelihood principle. We also discuss an alternative based on support vectors. Results are demonstrated using the IEEE 123 buses distribution test case.

Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of interpretability.Here we propose an interpretable network trained on the jet spectrum S 2 (R) which is a two-point correlation function of the jet constituents. The spectrum can be derived from a functional Taylor series of an arbitrary jet classifier function of energy flows. An interpretable network can be obtained by truncating the series. The intermediate feature of the network is an infrared and collinear safe C-correlator which allows us to estimate the importance of an S 2 (R) deposit at an angular scale R in the classification. The performance of the architecture is comparable to that of a convolutional neural network (CNN) trained on jet images, although the number of inputs and complexity of the architecture is significantly simpler than the CNN classifier. We consider two examples: one is the classification of two-prong jets which differ in color charge of the mother particle, and the other is a comparison between Pythia 8 and Herwig 7 generated jets.

Experimental results on both synthetic data and real microarray images demonstrate that the TV+L1 model gives the restored intensity that is closer to the true data than morphological opening. As a result, this method can serve an important role in the preprocessing of cDNA microarray data.

Background Super‐resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown. Purpose To evaluate MRI super‐resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring. Study Type Retrospective. Population In all, 176 MRI studies of subjects at varying stages of osteoarthritis. Field Strength/Sequence Original‐resolution 3D double‐echo steady‐state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super‐resolution and tricubic interpolation (TCI) at 3T. Assessment A quantitative comparison of femoral cartilage morphometry was performed for the original‐resolution DESS, the super‐resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans. Statistical Tests Mann–Whitney U‐tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super‐resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super‐resolution and TCI images, with the original‐resolution as a reference. Results DC for the original‐resolution (90.2 ± 1.7%) and super‐resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super‐resolution with the original‐resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through‐plane quantitative blur factor for super‐resolution images, was significantly (P < 0.001) better than TCI. Super‐resolution osteophyte detection sensitivity of 80% (76–82%), specificity of 93% (92–94%), and DOR of 32 (22–46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69–76%), specificity of 90% (89–91%), and DOR of 17 (13–22). Data Conclusion Super‐resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768–779.