The precision of medical diagnoses based on images is inextricably linked to the quality and clarity of the images. Poor image quality can impede accurate diagnosis and pose challenges for both physicians and machine learning algorithms in interpreting images. By automating the process of fundus image quality assessment during capture, we can ensure that only high-quality images are used, improving diagnostic accuracy. Accordingly, we propose a novel approach for automatically assessing the quality of fundus images using deep learning techniques. Our method incorporates retinal vessel segmentation into RGB images to create four-channel images and then trains a deep learning model on these images to identify the image focus and quality of the images. It can evaluate the quality of fundus images and request image recapture with the adjustment of specific parameters that are classified as poor quality. Our proposed method has the potential to improve the diagnostic accuracy and efficiency of retinal disease diagnosis, particularly in telemedicine settings. By automating the process of fundus image quality assessment, we can ensure that only high-quality images are used for diagnosis, thus improving diagnostic precision. It can serve as an efficient screening tool in the initial stage of acquiring high-quality fundus images.

People with metamorphopsia suffer from perceiving things in a distorted way. Various methods for examining metamorphopsia have been suggested in the current literature, with the most advanced techniques demonstrating the ability to yield quantitative measurements. However, these cutting-edge methods necessitate extended examination durations and impose challenging manipulations on patients. In this study, our objective is to enhance the time efficiency of the inspection process and alleviate the burden placed on the user. We propose a novel user-friendly quantitative inspection system which utilizes interactive reinforcement learning. Instead of having users directly operate the system, we ask them to evaluate the stimuli generated by the system. Based on their evaluations, the system gradually refines the deformation map representing the distortion perceived by the user. The reinforcement learning scheme is implemented using relevance feedback approach based on optimum-path forest classifier. To evaluate the effectiveness of the proposed system, subjective evaluation experiments involving simulated and real metamorphopsia participants were conducted in this study. The experimental findings reveal that, when compared to the state-of-the-art method, our proposed system yields comparable inspection out-comes while significantly reducing both the inspection duration and the mental workload.

Several image recoloring methods have been proposed to compensate for the loss of contrast caused by color vision deficiency (CVD). However, these methods only work for dichromacy (a case in which one of the three types of cone cells loses its function completely), while the majority of CVD is anomalous trichromacy (another case in which one of the three types of cone cells partially loses its function). In this paper, a novel degree-adaptable recoloring algorithm is presented, which recolors images by minimizing an objective function constrained by contrast enhancement and naturalness preservation. To assess the effectiveness of the proposed method, a quantitative evaluation using common metrics and subjective studies involving 14 volunteers with varying degrees of CVD are conducted. The results of the evaluation experiment show that the proposed personalized recoloring method outperforms the state-of-the-art methods, achieving desirable contrast enhancement adapted to different degrees of CVD while preserving naturalness as much as possible.

Metamorphopsia is a symptom caused by abnormalities in the retina, and people with metamorphopsia experience distortions in their field of view. To compensate for this disorder, computer-based distortion assessment and compensation methods have been proposed. In the state-of-the-art method, compensation is carried out by dynamically deforming the image with a manipulation map, which is bound to the symptom of an individual user with metamorphopsia, according to the user's gaze data captured by an eye tracker. However, this method suffers from the instability and inaccuracy of the eye tracker, which leads to a poor compensation effect. In this paper, we propose a novel method for metamorphopsia compensation without using an eye tracker. The proposed method generates a compensation image by simultaneously applying multiple manipulation maps to the input image. To evaluate the effectiveness of the proposed method, a preliminary subjective experiment using a reading task and involving 10 participants with normal vision was conducted. The evaluation results show that the proposed method can compensate for visual distortion during reading tasks.

Worldwide, 79.6 million people experience glaucoma, which can cause visual field loss to the individuals. Visual field examination plays an important role in the detection of glaucoma. However, current visual field examination approaches have disadvantages, such as requirements for expensive equipment, long testing time, location restrictions, and more. In the present study, we propose an assessment based on saccadic reaction time (SRT) to overcome the issues in the existing approaches. To confirm the effectiveness of our method, we simulated different stages of glaucoma using a visual field defect simulation system. The result of the visual experiment showed that the discrimination method using SRT can distinguish different symptoms with less testing time.

Homonymous hemianopia (HH) is one kind of visual field defect that the right or left half of the visual fields of both eyes are missing. HH is caused by damage to the neural pathways of the brain and a complete recovery is usually difficult. This study proposes a new information compensation method for HH patients using optical-see-through head mounted display. To avoid causing the occlusion, the proposed technique uses indicators placed at the boundary between the lost and remaining sides of visual field to notify patients about the changes in the lost side. Experiment involving simulated HH participants was conducted to verify the effectiveness of the proposed method and compare with the existing method using overlaid overview window. Objective and subjective evaluation results show that the proposed method can partially alleviate the occlusion problem of overlaid overview window approach and has lower physical and mental demand.

Visual field defects (VFD) can be caused by a variety of conditions. Checking and tracking the progression of VFD is an important part of an eye assessment. Although the use of standard automatic perimetry (SAP) is very popular for VFD diagnosis, it limits the population because of its high requirement for patients. We used a video-watching task as a replacement modality, which precludes the long period of fixation and uses the on-screen gaze to replace the button response. We developed a simulation system to mimic the different types of VFD in people with a normal pattern.We hypothesize that patients with VFD need more eye movement to compensate for the unseen area. We proposed a metric that indicates the gross eye movements toward a specific direction and found a significant difference between the VFD and normal pattern. Furthermore, we found videos that show the unique eye movement pattern in different eye conditions.

The number of individuals with Age-related Macular Degeneration (AMD) is rapidly increasing. One of the main symptoms of AMD is "metamorphopsia," or distorted vision, which not only makes it difficult for individuals with AMD to do detailed-oriented tasks but also makes sufferers more vulnerable to certain risks in day-to-day life. Traditional clinical approaches to assess metamorphopsia have lacked mechanisms for quantifying the degree of distortion in space, making it impossible to know exactly how individuals with the condition see things. This paper proposes a new method for quantifying distortion in space and visualizing AMD patients' distorted views via line manipulation. By visualizing the distorted views stemming from metamorphopsia, the method gives doctors and others an intuitive picture of how patients see the world and thereby enables a broad range of options for treatment and support.

Visual field defect (VFD) refers to a symptom in which a patient loses part of his/her field of view (FoV). Medical therapy can halt the progression of VFD, but complete recovery is impossible. In this paper, we propose a computational method for alleviating the restricted FoV with an optical see-through head-mounted display (OST-HMD), where an overview scene captured by the installed camera is overlaid on the persisting FoV. Since the overview window occludes with the real world scene, there is a trade-off between the augmented contextual information and the local unscreened information. We hypothesized that such a trade-off can be resolved by taking into consideration the size of the overview window and its displacement from the center of the unimpaired FoV. We therefore conducted an empirical evaluation through a Whac-A-Mole type of task with ten VFD-imitative subjects, where three sizes of an overview window with a fixed aspect ratio and seven positions in terms of elevation and azimuth were used combinatorially on an OST-HMD to find the best size and position of the overview window. It was statistically proven that for left-sided homonymous VFD-imitative subjects, the performance of the task was better when the medium-sized overview window was placed in the lower right position. The obtained result can legitimate default settings for the proposed VFD alleviation method.

More than 100 million individuals around the world suffer from color vision deficiency (CVD). Image recoloring algorithms have been proposed to compensate for CVD. This study has proposed a new recoloring algorithm to make up shortages of contrast enhancement and naturalness preservation of the state-of-the-art methods. The recoloring task is formulated as an optimization problem that is solved by using the colors in a simulated CVD color space to maximize contrast and to preserve the original color as much as possible. In addition, the dominant colors are extracted for recoloring. They are then propagated to the whole image so that the optimization problem could be solved at a reasonable cost independent of the image size. In the quantitative evaluation, the results of the proposed method are competitive with those of the best existing method. The evaluation involving subjects with CVD demonstrates that the proposed method outperforms the state-of-the-art method in preserving both the information and the naturalness of images.

Color vision deficiency (CVD) is caused by anomalies in the cone cells of the human retina. It affects approximately 200 million individuals throughout the world. Although previous studies have proposed compensation methods, contrast and naturalness preservation have not been adequately and simultaneously addressed in the state-of-the-art studies. This paper focuses on red–green dichromats’ compensation and proposes a recoloring algorithm that combines contrast enhancement and naturalness preservation in a unified optimization model. In this implementation, representative color extraction and edit propagation methods are introduced to maintain global and local information in the recolored image. The quantitative evaluation results showed that the proposed method is competitive with state-of-the-art methods. A subjective experiment was also conducted and the evaluation results revealed that the proposed method obtained the best scores in preserving both naturalness and information for individuals with severe red–green CVD.