A Novel Technique for Online Partial Discharge Pattern Recognition in Large Electrical Motors

In this paper, a fully automated system for source detection of the partial discharges (PD) as an online diagnosis test in rotating machineries is proposed. This technique uses a modified version of the Expectation Maximization-based (EM) clustering technique to separate the multi source Phase-Resolved Partial Discharge (PRPD) measurements into multiple single-source clusters. Afterwards, the fuzzy rule-based classifier determines the degree of membership of individual clusters to the possible PD origins based on the extracted features and exploiting expert knowledge. For the first time, the concept of cluster analysis is introduced for separation of PD data coming from different sources. Interestingly, the results demonstrate the robustness of the proposed technique in classifying multi-source data even in presence of strong noise in online measurements. Among 5 available datasets with multiple PD sources, the proposed technique were successful in correct classification of 90% of the sources

Analysis of vessel connectivities in retinal images by cortically inspired spectral clustering

\u3cp\u3eRetinal images provide early signs of diabetic retinopathy, glaucoma, and hypertension. These signs can be investigated based on microaneurysms or smaller vessels. The diagnostic biomarkers are the change of vessel widths and angles especially at junctions, which are investigated using the vessel segmentation or tracking. Vessel paths may also be interrupted; crossings and bifurcations may be disconnected. This paper addresses a novel contextual method based on the geometry of the primary visual cortex (V1) to study these difficulties. We have analyzed the specific problems at junctions with a connectivity kernel obtained as the fundamental solution of the Fokker–Planck equation, which is usually used to represent the geometrical structure of multi-orientation cortical connectivity. Using the spectral clustering on a large local affinity matrix constructed by both the connectivity kernel and the feature of intensity, the vessels are identified successfully in a hierarchical topology each representing an individual perceptual unit.\u3c/p\u3

Curvature Integration in a 5D Kernel for Extracting Vessel Connections in Retinal Images

Tree-like structures, such as retinal images, are widely studied in computer-aided diagnosis systems for large-scale screening programs. Despite several segmentation and tracking methods proposed in the literature, there still exist several limitations specifically when two or more curvilinear structures cross or bifurcate, or in the presence of interrupted lines or highly curved blood vessels. In this paper, we propose a novel approach based on multi-orientation scores augmented with a contextual affinity matrix, which both are inspired by the geometry of the primary visual cortex (V1) and their contextual connections. The connectivity is described with a 5D kernel obtained as the fundamental solution of the Fokker-Planck equation modeling the cortical connectivity in the lifted space of positions, orientations, curvatures, and intensity. It is further used in a self-tuning spectral clustering step to identify the main perceptual units in the stimuli. The proposed method has been validated on several easy as well as challenging structures in a set of artificial images and actual retinal patches. Supported by quantitative and qualitative results, the method is capable of overcoming the limitations of current state-of-the-art techniques

Exploratory study on direct prediction of diabetes using deep residual networks

\u3cp\u3eDiabetes is threatening the health of many people in the world. People may be diagnosed with diabetes only when symptoms or complications such as diabetic retinopathy start to appear. Retinal images reflect the health of the circulatory system and they are considered as a cheap and patient-friendly source of information for diagnosis purposes. Convolutional neural networks have enhanced the performance of conventional image processing techniques significantly by neglecting inconsistent feature extraction pipelines and learning informative features automatically from data. In this work we explore the possibility of using the deep residual networks as one of the state-of-the-art convolutional networks to diagnose diabetes directly from retinal images, without using any blood glucose information. The results indicate that convolutional networks are able to capture informative differences between healthy and diabetic patients and it is possible to differentiate between these two groups using only the retinal images. The performance of the proposed method is significantly higher than human experts.\u3c/p\u3

Boosted Exudate Segmentation in Retinal Images using Residual Nets

Exudates in retinal images are one of the early signs of the vision-threatening diabetic retinopathy and diabetic macular edema. Early diagnosis is very helpful in preventing the progression of the disease. In this work, we propose a fully automatic exudate segmentation method based on the state-of-the-art residual learning framework. With our proposed end-to-end architecture the training is done on small patches, but at the test time, the full sized segmentation is obtained at once. The small number of exudates in the training set and the presence of other bright regions are the limiting factors, which are tackled by our proposed importance sampling approach. This technique selects the misleading normal patches with a higher priority, and at the same time avoids the network to overfit to those samples. Thus, no additional post-processing is needed. The method was evaluated on three public datasets for both detecting and segmenting the exudates and outperformed the state-of-the-art techniques