Search Results (5)

Great advances in stitching high-quality retinal images have been made in recent years. On the other hand, very few studies have been carried out on low-resolution retinal imaging. This work investigates the challenges of low-resolution retinal images obtained by the D-EYE smartphone-based fundus camera. The proposed method uses homography estimation to register and stitch low-quality retinal images into a cohesive mosaic. First, a Siamese neural network extracts features from a pair of images, after which the correlation of their feature maps is computed. This correlation map is fed through four independent CNNs to estimate the homography parameters, each specializing in different corner coordinates. Our model was trained on a synthetic dataset generated from the Microsoft Common Objects in Context (MSCOCO) dataset; this work added an important data augmentation phase to improve the quality of the model. Then, the same is evaluated on the FIRE retina and D-EYE datasets for performance measurement using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The obtained results are promising: the average PSNR was 26.14 dB, with an SSIM of 0.96 on the D-EYE dataset. Compared to the method that uses a single neural network for homography calculations, our approach improves the PSNR by 7.96 dB and achieves a 7.86% higher SSIM score. Full article

The use of motion-analysis devices that can measure the progress of rehabilitation exercises for nerve paralysis is increasing because of the need to confirm the effectiveness of treatment for sports injuries. This study developed a new motion-analysis device that can be easily handled compared with the existing VICON motion-analysis device. Motion analysis of the human body (specifically, hip flexion, knee flexion, and trunk rotation) performed simultaneously with the new device and the existing VICON device was compared. Five healthy young men voluntarily participated in this study. Various joint angles were captured using a marker-less multi-view image-based motion-analysis system and a VICON motion capture system with markers during lower-extremity work. Intra-class correlation coefficient (ICC) analysis was used to examine simultaneous- and angular-limit validity and the intra-joint reliability of multi-point image-based motion-analysis systems. Simultaneous validity analysis showed that the highest ICCs for hip flexion, knee flexion, and trunk rotation were 0.924–0.998, 0.842–0.989 or higher, and 0.795–0.962, respectively. We confirmed that this new marker-less motion-analysis system has high accuracy and reliability in measuring joint kinematics in the lower extremities during rehabilitation and in monitoring the performance of athletes in training facilities. Full article

As the number of musculoskeletal disorders caused by smartphone usage, sedentary lifestyles, and active sports activities increases, there is a growing demand for precise and accurate measurement and evaluation of issues such as incorrect compensation patterns, asymmetrical posture, and limited joint operation range. Urgent development of new inspection equipment is necessary to address issues such as convenience, economic feasibility, and post-processing difficulties. Using 4DEYE^®, a new multi-view red, green, and blue (RGB) sensor-based motion analysis equipment, and the VICON^® ratio, which are infrared-based markers, we conducted a comparative analysis of the simultaneous validity of the joint angle (trajectory) and reliability. In this study, five healthy participants who could perform movements were selected for the pilot study and two movements (Y-balance and side dip) were analyzed. In addition, the ICC (Intraclass Correlation Coefficient) was analyzed using the SPSS (Statistical Package for the Social Sciences) V.18 while the number of data frames of each equipment was equalized using the MATLAB program. The results revealed that side dips, which are open kinetic chain exercises (intraclass correlation coefficient ICC(2.1), 0.895–0.996), showed very high concordance with the Y-balance test, a closed kinetic chain exercise (ICC(2.1), 0.678–0.990). The joint measurement results were similar regardless of the movement in the open or closed kinetic chain exercise, confirming the high reliability of the newly developed multiview RGB sensor. This is of great significance because we obtained important and fundamental results that can be used in various patterns of exercise movements in the future. Full article

Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the retinal location by applying a neural network, which is one of the methods reported in the literature with good performance for object detection, the YOLO v4, which was demonstrated to be the preferred method to apply. A mosaicing technique was implemented from the relevant retina regions to obtain a more informative single image with a higher field of view. It was divided into two stages: the GLAMpoints neural network was applied to extract relevant points in the first stage. Some homography transformations are carried out to have in the same referential the overlap of common regions of the images. In the second stage, a smoothing process was performed in the transition between images. Full article

Hard exudates are one of the most characteristic and dangerous signs of diabetic retinopathy. They can be marked during the routine ophthalmological examination and seen in color fundus photographs (i.e., using a fundus camera). The purpose of this paper is to introduce an algorithm that can extract pathological changes (i.e., hard exudates) in diabetic retinopathy. This was a retrospective, nonrandomized study. A total of 100 photos were included in the analysis—50 sick and 50 normal eyes. Small lesions in diabetic retinopathy could be automatically diagnosed by the system with an accuracy of 98%. During the experiments, the authors used classical image processing methods such as binarization or median filtration, and data was read from the d-Eye sensor. Sixty-seven patients (39 females and 28 males with ages ranging between 50 and 64) were examined. The results have shown that the proposed solution accuracy level equals 98%. Moreover, the algorithm returns correct classification decisions for high quality images and low quality samples. Furthermore, we consider taking retina photos using mobile phones rather than fundus cameras, which is more practical. The paper presents an innovative approach. The results are introduced and the algorithm is described. Full article