An Effective Approach Based on Improved Convolutionary Neural Network Classifier for the Detection of Diabetic Retinopathy

In diabetic individuals, diabetic retinopathy (DR) causes blindness. Therefore, detecting diabetic retinopathy at an early stage decreases vision loss. An successful approach for diabetic retinopathy prediction is discussed in this article. In the beginning, the input pictures of human retinal fundus images are preprocessed using histogram equalisation followed by Gabor filtering to reduce noise for enhancement. Then, using the Watershed method, segmentation is performed, and the features are retrieved through feature extraction. The best optimum features are selected using PCA (principal component analysis) approach. The morphological based post processing scheme was employed to further enhance the quality of selected features. At last, the classification approach is carried with the utilization of Google NET CNN classifier to classify/predict the retinal image as normal, abnormal, and severe. Google NET CNN has been developed with limited preprocessing step to distinguish visual features directly from image pixels. The findings are then evaluated and the efficacy of the new method is contrasted with other current methods. The quantitative findings were evaluated for Accuracy, precision, reliability, positive predictive levels and false predictive levels in parameters and were seen to deliver better results than current techniques.

ANALYSIS OF ULTRASOUND IMAGE BIOLOGICAL IMAGE ALGORITHM IN THE RESTORATION OF MUSCLE GROUP MOVEMENT FUNCTION

ABSTRACT Objective: By studying the recognition effect of ultrasonic biological image data analysis on muscle group motion function, the evaluation value and significance of ultrasonic biomedical image combination algorithm on muscle group motion function are discussed. Methods: A Gabor filtering algorithm is proposed to smooth the original image. The MVEF algorithm is used to enhance the ultrasonic image and binary further the image again. Using the principle of the Hove transform, the thickness of the muscle is automatically estimated. Results: The square of correlation coefficients of the manual measurement method, Gabor filtering algorithm and MVEF algorithm are 91.3%, 91.3% and 87.8%, respectively. The difference between the manual measurement and the estimation based on the Gabor filtering algorithm is 1.45 ± 0.48mm. The difference between the results of manual measurement and the MVEF algorithm is 1.38 ± 0.56mm. The computation time of the MVEF algorithm and Gabor algorithm are 5 seconds and 0.3 seconds, respectively. Conclusions: The algorithm proposed in this study can effectively measure the muscle thickness, fast, convenient and accurate, and can reflect the contractility of skeletal muscle well, which is of great value for the recognition and evaluation of muscle group movement function. Level of evidence II; Therapeutic studies - investigation of treatment results.

Improvement and Assessment of a Blind Image Deblurring Algorithm Based on Independent Component Analysis

The aim of the present paper is to improve an existing blind image deblurring algorithm, based on an independent component learning paradigm, by manifold calculus. The original technique is based on an independent component analysis algorithm applied to a set of pseudo-images obtained by Gabor-filtering a blurred image and is based on an adapt-and-project paradigm. A comparison between the original technique and the improved method shows that independent component learning on the unit hypersphere by a Riemannian-gradient algorithm outperforms the adapt-and-project strategy. A comprehensive set of numerical tests evidenced the strengths and weaknesses of the discussed deblurring technique.

Applying Ateb–Gabor Filters to Biometric Imaging Problems

This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well.

Histological classification of non-small cell lung cancer using Gabor filtering Minkowski functionals and Neighborhood Component Analysis on CT images.

Information regarding the histological types of non-small cell lung cancer is essential to determine the treatment strategy. Although several radiomics studies using almost similar feature variables were reported, a considerable variation in the performances has been observed. In this study, as novel radiomic features, 2D Gabor filtering Minkowski functionals were used. They were calculated in rotational invariant and both scale and rotational invariant ways using circular shift operations of Gabor filters on nonenhanced computed tomographic images. Eighty-six patients (47 adenocarcinomas, 39 squamous cell carcinomas) were analyzed. Two independent observers manually delineated a single slice segmentation of a tumor. Feature selection was made by neighborhood component analysis. Among various classifiers, 1-nearest neighbor gave a promising performance. The observer-averaged accuracy of rotational invariant analysis was 86.28% and that of both scale and rotational invariant one was 88.27%. However, there was no common feature among the ten top-ranked features of each observer with the identical Gabor filtering type. Hence further study of the robustness is necessary to create a more reliable model.

High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

An image enhancement method based on gabor filtering in wavelet domain and adaptive histogram equalization

<p><span>The images are not always good enough to convey the proper information. The image may be very bright or very dark sometime or it may be low contrast or high contrast. Because of these reasons image enhancement plays important role in digital image processing. In this paper we proposed an image enhancement technique in which Gabor and median filtering is performed in wavelet domain and Adaptive Histogram Equalization is performed in spatial domain. Brightness and contrast are the two parameters used for analyzing the performance of the proposed method</span></p>

A Review of Computer Vision Techniques in the Detection of Metal Failures

This paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems are used by manufacturers to perform non-destructive testing and inspection of components at high speeds [1]; providing better error detection than traditional human visual inspection, and lower costs [2]. This is a review of the computer vision system comparing different mathematical analysis in order to illustrate the strengths and weaknesses relative to the nature of the defect. It includes exemplar that histograms and statistical analysis operate best with significant contrast between the defect and background, that co-occurrence matrix and Gabor filtering are computationally expensive, that structural analysis is useful when there are repeated patterns, that Fourier transforms, applied to spatial data, need windowing to capture localized issues, and that neural networks can be utilized after training.