다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review

Image registration is the process of matching, aligning and overlaying two or more images of a scene, which are captured from different viewpoints. It is extensively used in numerous vision based applications. Image registration has five main stages: Feature Detection and Description; Feature Matching; Outlier Rejection; Derivation of Transformation Function; and Image Reconstruction. Timing and accuracy of feature-based Image Registration mainly depend on computational efficiency and robustness of the selected feature-detector-descriptor, respectively. Therefore, choice of feature-detector-descriptor is a critical decision in feature-matching applications. This article presents a comprehensive comparison of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK algorithms. It also elucidates a critical dilemma: Which algorithm is more invariant to scale, rotation and viewpoint changes? To investigate this problem, image matching has been performed with these features to match the scaled versions (5% to 500%), rotated versions (0° to 360°), and perspective-transformed versions of standard images with the original ones. Experiments have been conducted on diverse images taken from benchmark datasets: University of OXFORD, MATLAB, VLFeat, and OpenCV. Nearest-Neighbor-Distance-Ratio has been used as the feature-matching strategy while RANSAC has been applied for rejecting outliers and fitting the transformation models. Results are presented in terms of quantitative comparison, feature-detection-description time, feature-matching time, time of outlier-rejection and model fitting, repeatability, and error in recovered results as compared to the ground-truths. SIFT and BRISK are found to be the most accurate algorithms while ORB and BRISK are most efficient. The article comprises rich information that will be very useful for making important decisions in vision based applications and main aim of this work is to set a benchmark for researchers, regardless of any particular area.

A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK

In recent years, the classification of breast cancer has been the topic of interest in the field of Healthcare informatics, because it is the second main cause of cancer-related deaths in women. Breast cancer can be identified using a biopsy where tissue is removed and studied under microscope. The diagnosis is based on the qualification of the histopathologist, who will look for abnormal cells. However, if the histopathologist is not well-trained, this may lead to wrong diagnosis. With the recent advances in image processing and machine learning, there is an interest in attempting to develop a reliable pattern recognition based systems to improve the quality of diagnosis. In this paper, we compare two machine learning approaches for the automatic classification of breast cancer histology images into benign and malignant and into benign and malignant sub-classes. The first approach is based on the extraction of a set of handcrafted features encoded by two coding models (bag of words and locality constrained linear coding) and trained by support vector machines, while the second approach is based on the design of convolutional neural networks. We have also experimentally tested dataset augmentation techniques to enhance the accuracy of the convolutional neural network as well as “handcrafted features + convolutional neural network” and “ convolutional neural network features + classifier” configurations. The results show convolutional neural networks outperformed the handcrafted feature based classifier, where we achieved accuracy between 96.15% and 98.33% for the binary classification and 83.31% and 88.23% for the multi-class classification.

Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks

Feature detection, description and matching are essential components of various computer vision applications, thus they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of definitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection and description algorithms.

Image Features Detection, Description and Matching

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.

Identity recognition using local features extracted from ear images has recently attracted a great deal of attention in the intelligent biometric systems community. The rich and reliable information of the human ear and its stable structure over a long period of time present ear recognition technology as an appealing choice for identifying individuals and verifying their identities. This paper considers the ear recognition problem using local binary patterns (LBP) features. Where, the LBP-like features characterize the spatial structure of the image texture based on the assumption that this texture has a pattern and its strength (amplitude)-two locally complementary aspects. Their high discriminative power, invariance to monotonic gray-scale changes and computational efficiency properties make the LBP-like features suitable for the ear recognition problem. Thus, the performance of several recent LBP variants introduced in the literature as feature extraction techniques is investigated to determine how can they be best utilized for ear recognition. To this end, we carry out a comprehensive comparative study on the identification and verification scenarios separately. Besides, a new variant of the traditional LBP operator named averaged local binary patterns (ALBP) is proposed and its ability in representing texture of ear images is compared with the other LBP variants. The ear identification and verification experiments are extensively conducted on five publicly available constrained and unconstrained benchmark ear datasets stressing various imaging conditions; namely IIT Delhi (I), IIT Delhi (II), AMI, WPUT and AWE. The obtained results for both identification and verification indicate that the current LBP texture descriptors are successful feature extraction candidates for ear recognition systems in the case of constrained imaging conditions and can achieve recognition rates reaching up to 99%; while, their performance faces difficulties when the level of distortions increases. Moreover, it is noted that the tested LBP variants achieve almost close performance on ear recognition. Thus, further studies on other applications are needed to verify this close performance. We believe that the presented study has significant insights and can benefit researchers in choosing between LBP variants as well as acting as a connection between previous studies and future work in utilizing LBP-like features in ear recognition systems.

Ear recognition using local binary patterns: A comparative experimental study

Diabetic retinopathy (DR) is a diabetes complication that affects the eye and can cause damage from mild vision problems to complete blindness. It has been observed that the eye fundus images show various kinds of color aberrations and irrelevant illuminations, which degrade the diagnostic analysis and may hinder the results. In this research, we present a methodology to eliminate these unnecessary reflectance properties of the images using a novel image processing schema and a stacked deep learning technique for the diagnosis. For the luminosity normalization of the image, the gray world color constancy algorithm is implemented which does image desaturation and improves the overall image quality. The effectiveness of the proposed image enhancement technique is evaluated based on the peak signal to noise ratio (PSNR) and mean squared error (MSE) of the normalized image. To develop a deep learning based computer-aided diagnostic system, we present a novel methodology of stacked generalization of convolution neural networks (CNN). Three custom CNN model weights are fed on the top of a single meta-learner classifier, which combines the most optimum weights of the three sub-neural networks to obtain superior metrics of evaluation and robust prediction results. The proposed stacked model reports an overall test accuracy of 97.92% (binary classification) and 87.45% (multi-class classification). Extensive experimental results in terms of accuracy, F-measure, sensitivity, specificity, recall and precision reveal that the proposed methodology of illumination normalization greatly facilitated the deep learning model and yields better results than various state-of-art techniques.

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Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models

The recognition performance of visual recognition systems is highly dependent on extracting and representing the discriminative characteristics of image data. Convolutional neural networks (CNNs) have shown unprecedented success in a variety of visual recognition tasks due to their capability to provide in-depth representations exploiting visual image features of appearance, color, and texture. This paper presents a novel system for ear recognition based on ensembles of deep CNN-based models and more specifically the Visual Geometry Group (VGG)-like network architectures for extracting discriminative deep features from ear images. We began by training different networks of increasing depth on ear images with random weight initialization. Then, we examined pretrained models as feature extractors as well as fine-tuning them on ear images. After that, we built ensembles of the best models to further improve the recognition performance. We evaluated the proposed ensembles through identification experiments using ear images acquired under controlled and uncontrolled conditions from mathematical analysis of images (AMI), AMI cropped (AMIC) (introduced here), and West Pomeranian University of Technology (WPUT) ear datasets. The experimental results indicate that our ensembles of models yield the best performance with significant improvements over the recently published results. Moreover, we provide visual explanations of the learned features by highlighting the relevant image regions utilized by the models for making decisions or predictions.

Hammam A. Alshazly

Papers

Image Features Detection, Description and Matching

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.

Ear recognition using local binary patterns: A comparative experimental study

Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models

Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition.