The inspection of retinal fundus images allows medical doctors to diagnose various pathologies. Computer-aided diagnosis systems can be used to assist in this process. As a first step, such systems delineate the vessel tree from the background. We propose a method for the delineation of blood vessels in retinal images that is effective for vessels of different thickness. In the proposed method, we employ a set of B-COSFIRE filters selective for vessels and vessel-endings. Such a set is determined in an automatic selection process and can adapt to different applications. We compare the performance of different selection methods based upon machine learning and information theory. The results that we achieve by performing experiments on two public benchmark data sets, namely DRIVE and STARE, demonstrate the effectiveness of the proposed approach.

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Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters

Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering

No-reference image quality assessment algorithms: A survey

Local gradient patterns (LGP): An effective local-statistical-feature extraction scheme for no-reference image quality assessment

Early detection of induction-motor faults has been an increasing matter of research in the last few years. The reliable identification of broken rotor bars (BRB) is still under investigation as it is one of the most common and difficult-to-detect faults in induction motors. Many methods have been proposed to deal with this issue. Recent approaches combine techniques looking for improving the performance of the diagnosis. Their major disadvantage is the high computational requirements, which restrains them from being used in online detection. The contribution in this paper is twofold. The first one is a novel methodology for induction motor BRB detection and the fault severity classification using homogeneity as index, which, to the best of our knowledge, has never been used as an indicator for fault diagnosis, analyzing one phase of the induction motor startup-transient current. Because of the low computational complexity in homogeneity calculation, the second contribution of this paper is a hardware-processing unit based on a field programmable gate array device for online detection and classification of BRB. Obtained results demonstrate the high efficiency of the proposed methodology as a deterministic technique for incipient BRB diagnosis in induction motors, which can detect and differentiate among half, one, or two BRBs with a certainty greater than 99.7%.

Novel FPGA-based Methodology for Early Broken Rotor Bar Detection and Classification Through Homogeneity Estimation

Retinal image registration using topological vascular tree segmentation and bifurcation structures

Blind noisy image quality assessment using block homogeneity

The objective of blind noisy image quality assessment is to evaluate the quality of the degraded noisy image without the knowledge of the ground truth image. Its performance relies on the accuracy of the noise statistics estimated from homogenous blocks. The major challenge of block-based approaches lies in the block size selection, as it affects the local noise derivation. To tackle this challenge, a deformable ant colony optimization (DACO) approach is proposed in this paper to adaptively adjust the ant size for image block selection. The proposed DACO approach considers that the size of the ant is adjustable during foraging. For the smooth image blocks, more pheromone is deposited, and then the size of ant is increased. Therefore, this strategy enables the ants to have dynamic food-search capability, leading to more accurate selection of homogeneous blocks. Furthermore, the regression analysis is used to obtain image quality score by exploiting the above-estimated noise statistics. Experimental results are provided to justify that the proposed approach outperforms conventional approaches to provide more accurate noise statistics estimation and achieve a consistent image quality evaluation performance for both the artificially generated and real-world noisy images.

Blind noisy image quality evaluation using a deformable ant colony algorithm

Blind noisy image estimation is useful in many visual processing systems. The challenge lies in accurately estimating the image noise level without any priori information of the image. To tackle this challenge, an iterative texture-based eigenvalue analysis approach is proposed in this paper. The proposed approach utilizes the eigenvalue analysis to mathematically derive a new noise level estimator based on weak-textured image patches. Furthermore, a new texture strength measure is proposed to adaptively select weak-textured patches from the noisy image. Experimental results are provided to demonstrate that the proposed image noise level estimation approach yields superior accuracy and stability performance to that of conventional noise level estimation approaches, so that to improve the performance of image denoising algorithm.

Blind image noise level estimation using texture-based eigenvalue analysis

In low temperature multi-effect distillation (LT-MED) seawater desalination system, the (super-)hydrophilic surface modification of aluminum alloy distillation desalination tube is effective in improving the performance of the desalination system and reducing the cost. However, the poor corrosion resistance of desalination tubes under (super-)hydrophilic surface modification is a bottleneck that limits the application of desalination tubes. This research breaks this technical bottleneck and proposes a simple, economical and industrially applicable method that jointly enhances hydrophilicity and corrosion resistance on aluminum alloy distillation desalination tubes. This method combines a concentrated anodizing electrolyte, pyrophosphoric acid, with an eco-friendly corrosion inhibitor , Na 2 MoO 4 , and prepares a superhydrophilic anodic oxide film with substantially enhanced corrosion resistance. The research shows that the concentration of Na 2 MoO 4 in electrolyte plays a crucial role in the corrosion resistance of the superhydrophilic anodic oxide films: under the condition of ensuring the superhydrophilic surface of aluminum alloy (contact angle <5°), as Na 2 MoO 4 concentration (0– 0.5 mol/L) increases, the higher the concentration of Na 2 MoO 4 in the electrolyte, the better the corrosion resistance of the prepared superhydrophilic anodic oxide film. When adding 0.5 mol/L Na 2 MoO 4 to 70 wt% pyrophosphoric acid electrolyte, compared with the superhydrophilic anodic oxide film without the addition of Na 2 MoO 4 , the prepared superhydrophilic film has its corrosion potential increased by 0.451 V, corrosion current density reduced by nearly four times, and corrosion inhibition efficiency increased from 25 % to 80 %. This research reveals the mechanism by which the corrosion inhibitor Na 2 MoO 4 enhances the corrosion resistance of superhydrophilic anodic oxide film through adsorption of the porous layer, thickening of the barrier layer and Mo(+VI) incorporating. This paper provides a new idea and method for developing aluminum alloy desalination tubes with both stable superhydrophilicity and excellent corrosion resistance. • Break bottleneck of poor corrosion resistance of superhydrophilic desalination tubes • Propose new method of co-enhancing hydrophilicity and anticorrosion of aluminum alloy • Add Na 2 MoO 4 to H 4 P 2 O 7 electrolyte improves film inhibition efficiency from 25 % to 80 % • Explore mechanism of Na 2 MoO 4 enhancing anticorrosion of superhydrophilic anodic film 

Xiaotong Huang

Papers

Retinal image registration using topological vascular tree segmentation and bifurcation structures

Blind noisy image quality assessment using block homogeneity

Blind noisy image quality evaluation using a deformable ant colony algorithm

Blind image noise level estimation using texture-based eigenvalue analysis

Study on the superhydrophilic modification and enhanced corrosion resistance method of aluminum alloy distillation desalination tubes