Keypoint matching networks for longitudinal fundus image affine registration

2021 ◽  
Author(s):  
Yunzhen Peng ◽  
Xinjian Chen ◽  
Dehui Xiang ◽  
Gaohui Luo ◽  
Mulin Cai
Keyword(s):  
Fundus Image ◽  
Keypoint Matching ◽  
Affine Registration ◽  
Matching Networks

Affine Registration Based on Chord Height Point and Genetic Algorithm

2014 ◽  
Vol 39 (9) ◽  
pp. 1447-1457
Author(s):  
Gui-Mei ZHANG ◽  
Shao-Bo JIANG ◽  
Jun CHU
Keyword(s):  
Genetic Algorithm ◽  
Affine Registration

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

scholarly journals Optimum power transfer in RF front end systems using adaptive impedance matching technique

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Alibakhshikenari ◽  
Bal S. Virdee ◽  
Leyre Azpilicueta ◽  
Chan H. See ◽  
Raed Abd-Alhameed ◽  
...  
Keyword(s):  
Impedance Matching ◽  
Adaptive Antenna ◽  
Power Transfer ◽  
Control Loops ◽  
Matching Networks ◽  
Front End ◽  
Rf Front End ◽  
Matching Technique ◽  
Digital Circuitry ◽  
Communications System

AbstractMatching the antenna’s impedance to the RF-front-end of a wireless communications system is challenging as the impedance varies with its surround environment. Autonomously matching the antenna to the RF-front-end is therefore essential to optimize power transfer and thereby maintain the antenna’s radiation efficiency. This paper presents a theoretical technique for automatically tuning an LC impedance matching network that compensates antenna mismatch presented to the RF-front-end. The proposed technique converges to a matching point without the need of complex mathematical modelling of the system comprising of non-linear control elements. Digital circuitry is used to implement the required matching circuit. Reliable convergence is achieved within the tuning range of the LC-network using control-loops that can independently control the LC impedance. An algorithm based on the proposed technique was used to verify its effectiveness with various antenna loads. Mismatch error of the technique is less than 0.2%. The technique enables speedy convergence (< 5 µs) and is highly accurate for autonomous adaptive antenna matching networks.

scholarly journals Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases

2021 ◽  
Vol 11 (5) ◽  
pp. 321
Author(s):  
Kyoung Min Kim ◽  
Tae-Young Heo ◽  
Aesul Kim ◽  
Joohee Kim ◽  
Kyu Jin Han ◽  
...  
Keyword(s):  
Network Models ◽  
Retinal Disease ◽  
Age Related Macular Degeneration ◽  
Diagnostic Tools ◽  
Retinal Images ◽  
Dense Layer ◽  
Retinal Diseases ◽  
Fundus Image ◽  
Neural Network Models ◽  
Age Related

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

scholarly journals Improvement of Small Signal Equivalent Simulations for Power and Efficiency Matching of GaN HEMTs

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 263
Author(s):  
Roberto Quaglia
Keyword(s):  
Power Amplifier ◽  
High Electron Mobility Transistor ◽  
High Electron ◽  
Large Signal ◽  
Signal Model ◽  
Or Efficiency ◽  
Summary Table ◽  
Matching Networks ◽  
Amplifier Design ◽  
Frequency Power

In high-frequency power-amplifier design, it is common practice to approach the design of reactive matching networks using linear simulators and targeting a reflection loss limit (referenced to the target impedance). It is well known that this is only a first-pass design technique, since output power or efficiency contours do not correspond to mismatch circles. This paper presents a method to improve the accuracy of this approach in the case of matching network design for power amplifiers based on gallium nitride (GaN) technology. Equivalent mismatch circles, which lay within the power or efficiency contours targeted by the design, are analytically obtained thanks to geometrical considerations. A summary table providing the parameters to use for typical contours is provided. The technique is demonstrated on two examples of power-amplifier design on the 6–12 GHz band using the non-linear large-signal model of a GaN High Electron Mobility Transistor (HEMT).

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