Deep learning technology has been extensively explored in pattern recognition and image processing areas. A multi-mode medical image fusion with deep learning will be proposed, according to the characters of multi-modal medical image, medical diagnostic technology and practical implementation, according to the practical needs for medical diagnosis. It cannot be only made up for the deficiencies of MRI, CT and SPECT image fusion, but also can be implemented in different types of multi-modal medical image fusion problems in batch processing mode, and can be effectively overcome the limitation of only one-page processing. The proposed method can greatly improve the fusion effect, image detail clarity and time efficiency. The experiments on multi-modal medical images are implemented to analyze performance, algorithm stability and so on. The experimental results prove the superiority of our proposed method in terms of visual quality and a variety of quantitative evaluation criteria.

Medical image fusion method by deep learning

Interactive panoramic systems are currently on the rise. However, one of the major challenges in such a system is the overhead involved in transferring a full-quality panorama to the client when only a part of the panorama is used to extract a virtual view. Thus, such a system should maximize the user experience while simultaneously minimizing the bandwidth required. In this paper, we apply tiling to deliver different quality levels for different parts of the panorama. Tiling has traditionally been applied to the delivery of very high-resolution content to clients. Here, we apply similar ideas in a real-time interactive panoramic video system. A major challenge lies in the movement of such a virtual view, for which clients’ regions of interest change dynamically and independently from each other. We show that our algorithms, which progressively increase in quality toward the point of the view, manage to (i) reduce the bandwidth requirement and (ii) provide a similar quality of experience (QoE) compared to a full panorama system.

Tiling in Interactive Panoramic Video: Approaches and Evaluation

Virtual reality (VR)/ augmented reality (AR) applications allow users to view artificial content of a surrounding space simulating presence effect with a help of special applications or devices. Synthetic contents production is well known process form computer graphics domain and pipeline has been already fixed in the industry. However emerging multimedia formats for immersive entertainment applications such as free-viewpoint television (FTV) or spherical panoramic video require different approaches in content management and quality assessment. The international standardization on FTV has been promoted by MPEG. This paper is dedicated to discussion of immersive media distribution format and quality estimation process. Accuracy and reliability of the proposed objective quality estimation method had been verified with spherical panoramic images demonstrating good correlation results with subjective quality estimation held by a group of experts.

Quality metric for spherical panoramic video

Ensemble of CNN for multi-focus image fusion

Multi-focus image fusion: A Survey of the state of the art

Multi-focus image fusion using Content Adaptive Blurring

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

https://www.mdpi.com/2076-3417/11/5/2284/pdf

Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

Knee issues are very frequent among people of all ages, and osteoarthritis is one of the most common reasons behind them. The primary feature in observing extremity and advancement of osteoarthritis is joint space narrowing (cartilage loss) which is manually computed on knee x-rays by a radiologist. Such manual inspections require an expert radiologist to analyze the x-ray image; moreover, it is a tedious and time-consuming task. In this paper, we present a computer-vision-based system that can assist the radiologists by analyzing the radiological symptoms in knee x-rays for osteoarthritis. Different image processing techniques have been applied on knee radiographs to enhance their quality. The knee region is extracted automatically using template matching. The knee joint space width is calculated, and the radiographs are classified based on the comparison with the standard normal knee joint space width. The experimental evaluation performed on a large knee x-ray dataset shows that our method is able to efficiently detect osteoarthritis, achieving more than 97% detection accuracy.

X-ray image analysis for automated knee osteoarthritis detection

Malaria is caused by Plasmodium parasite. It is transmitted by female Anopheles bite. Thick and thin blood smears of the patient are manually examined by an expert pathologist with the help of a microscope to diagnose the disease. Such expert pathologists may not be available in many parts of the world due to poor health facilities. Moreover, manual inspection requires full concentration of the pathologist and it is a tedious and time consuming way to detect the malaria. Therefore, development of automated systems is momentous for a quick and reliable detection of malaria. It can reduce the false negative rate and it can help in detecting the disease at early stages where it can be cured effectively. In this paper, we present a computer aided design to automatically detect malarial parasite from microscopic blood images. The proposed method uses bilateral filtering to remove the noise and enhance the image quality. Adaptive thresholding and morphological image processing algorithms are used to detect the malaria parasites inside individual cell. To measure the efficiency of the proposed algorithm, we have tested our method on a NIH Malaria dataset and also compared the results with existing similar methods. Our method achieved the detection accuracy of more than 91% outperforming the competing methods. The results show that the proposed algorithm is reliable and can be of great assistance to the pathologists and hematologists for accurate malaria parasite detection.

Automatic detection of Plasmodium parasites from microscopic blood images

Gait is a novel biometric feature that offers human identification at a distance and without physical interaction with the imaging device. Moreover, it performs well even in low resolution which makes it ideal for use in numerous human identification applications, e.g.,visual surveillance, monitoring and access control systems. Most existing gait-based human identification solutions extract human body silhouettes, contours or shapes from the images and construct gait features. Therefore, the performance of such algorithms highly depends upon the accuracy of human body segmentation, which is still a challenging problem in the literature. In this paper, we propose a new gait recognition algorithm which uses the spatial and temporal motion characteristics of human gait for individual identification without needing the silhouette extraction. The proposed algorithm extracts a set of spatiotemporal local descriptors from the gait video sequences. The extracted descriptors are encoded using the Fisher vector encoding and Gaussian mixture model-based codebook. The encoded features are classified using a simple linear support vector machine to recognize the individuals. The proposed gait recognition method is evaluated on five widely used gait databases, including indoor (CMU MoBo, CASIA-B) and outdoor (NLPR, CASIA-C, TUM GAID) gait databases. The results reveal that our method showed excellent performance on all five databases and outperformed the state-of-the-art gait recognition approaches.

Muhammad Shahid Farid

Papers

Multi-focus image fusion using Content Adaptive Blurring

Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

X-ray image analysis for automated knee osteoarthritis detection

Automatic detection of Plasmodium parasites from microscopic blood images

Spatiotemporal features of human motion for gait recognition