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This paper presents a novel, fully automatic method to grayscale images colorization using a scene guided neural network. In our proposed method, given a training set of both grayscale images and their corresponding color images, we first extract features of each grayscale pixel. These features, together with the corresponding RGB values of that pixel are input to train a colorization neural network for each given scene. To improve the performance of colorization, in both speed and results, we further classify the input and training images into different scene classes. We adopt a linear image classification method to generate a scene guided codebook and use it to determine the scene class of the input image. The preliminary colorization result is then generated by the corresponding trained neural network of the scene class of the input image. Finally, an image guided filter is used to refine colorized images. Inspired by the recent success in deep learning techniques which provide stabilizing modeling of large-scale medical image data, the proposed paper formulating the enhancement and colorization problem, so that colorization techniques can be directly used to ensure medical images with high quality. The experimental results on a broad range of images demonstrate that our method has better colorization performance as compared to that of the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications introduce a set of stringent requirements (e.g., low latency, high bandwidth) to network and computing paradigm. 5G networks are faced with great challenges for supporting IoT services. The centralized cloud computing paradigm also becomes inefficient for those stringent requirements. Only extending spectrum resources cannot solve the problem effectively. Mobile edge computing offers an IT service environment at the Radio Access Network edge and presents great opportunities for the development of IoT applications. With the capability to reduce latency and offer an improved user experience, mobile edge computing becomes a key technology toward 5G. To achieve abundant sharing, complex IoT applications have been implemented as a set of lightweight micro-services that are distributed among containers over the mobile edge network. How to produce the optimal collocation of suitable micro-service for an application in mobile edge computing environment is an important issue that should be addressed. To address this issue, we propose a latency-aware micro-service mashup approach in this paper. Firstly, the problem is formulated into an integer nonlinear programming. Then, we prove the NP-hardness of the problem by reducing it into the delay constrained least cost problem. Finally, we propose an approximation latency-aware micro-service mashup approach to solve the problem. Experiment results show that the proposed approach achieves a substantial reduction in network resource consumption while still ensuring the latency constraint. [ABSTRACT FROM AUTHOR]

Deformable contours are widely applied in medical image segmentation, which are usually derived from appearance cues in medical images. However, the performance of deformed contour is suppressed in ultrasonic image segmentation by the weak, misleading boundaries and the complex shapes of lesion regions. In this paper, a novel deformable contour model is proposed for segmenting ultrasound image sequences, which aims to utilize the powerful ability of deep learning network in learning of image features to help the deformable contour model resist weaknessses of ultrasound images. The deep learning network is designed as a densely connected siamese architecture. It trains a contrastive loss that serves as a boundary searching metric of a deformable contour to segment ultrasound image sequences. In this network, the densely residual blocks and the attention focused blocks are designed to make the network efficiently propagate features and focus on the lesion region, and the feature memory module stores and generates the prior features to aid the evolution of a deformable contour. Moreover, for resisting the impact of misleading or weak boundary, the shape similarity of lesion regions is used to as a shape prior and integrated into the framework of deformable contour to constrain the change of contours. The experimental results for the clinical ultrasound image sequences demonstrate that compared to the state-of-the-art methods, the proposed method can provide more accurate results in HIFU ultrasound images. [ABSTRACT FROM AUTHOR]

Rib segmentation based on chest X-ray images is essential in the computer-aided diagnosis systems of lung cancer, which serves as an important step in the quantitative analysis of various types of lung diseases. However, the traditional methods are unable to segment ribs effectively due to the unclear edges and overlapping regions in X-ray images. A novel rib segmentation framework based on Unpaired Sample Augmentation and Multi-Scale Network is presented in this paper, aiming to improve the accuracy of ribs segmentation with limited labeled samples. First, the algorithm learns pneumonia-related texture changes via unpaired chest x-ray images and generates various augmented samples. Then, a multi-scale network attempts to learn hierarchical features using global supervision. Finally, the refined segmentation result of each organ is achieved by using a deep separation module and a comprehensive loss function. Specifically, the hierarchical features can greatly improve the robustness of multi-organ segmentation networks. The complex multi-organ segmentation task with limited labeled data is simplified with the designed deep separation module. We justify the proposed framework through extensive experiments. It achieves good performance with DSC, Precision, Recall, and Jaccard of 88.03, 88.25, 88.36, and 79.02%, respectively. The DSC value increases nearly by 3% compared to other popular methods. The experimental results show that our algorithm presents better segmentation performance for the overlapping region and fuzzy region of multiple organs, which holds research value and prospects for application. [ABSTRACT FROM AUTHOR]