The accurate segmentation of medical images is crucial to medical care and research; however, many efficient supervised image segmentation methods require sufficient pixel level labels. Such requirement is difficult to meet in practice and even impossible in some cases, e.g., rare Pathoma images. Inspired by traditional unsupervised methods, we propose a novel Chan-Vese model based on the Markov chain for unsupervised medical image segmentation. It combines local information brought by superpixels with the global difference between the target tissue and the background. Based on the Chan-Vese model, we utilize weight maps generated by the Markov chain to model and solve the segmentation problem iteratively using the min-cut algorithm at the superpixel level. Our method exploits abundant boundary and local region information in segmentation and thus can handle images with intensity inhomogeneity and object sparsity. In our method, users gain the power of fine-tuning parameters to achieve satisfactory results for each segmentation. By contrast, the result from deep learning based methods is rigid. The performance of our method is assessed by using four Computerized Tomography (CT) datasets. Experimental results show that the proposed method outperforms traditional unsupervised segmentation techniques.

The rapid advancements in hardware, software, and computer networks have facilitated the shift of the computing paradigm from mainframe to cloud computing, in which users can get their desired services anytime, anywhere, and by any means. However, cloud computing also presents many challenges, one of which is the difficulty in allowing users to freely obtain desired services, such as heterogeneous OSes and applications, via different light-weight devices. We have proposed a new paradigm by spatio-temporally extending the von Neumann architecture, called transparent computing, to centrally store and manage the commodity programs including OS codes, while streaming them to be run in non-state clients. This leads to a service-centric computing environment, in which users can select the desired services on demand, without concern for these services’ administration, such as their installation, maintenance, management, and upgrade. In this paper, we introduce a novel concept, namely Meta OS, to support such program streaming through a distributed 4VP⁺ platform. Based on this platform, a pilot system has been implemented, which supports Windows and Linux environments. We verify the effectiveness of the platform through both real deployments and testbed experiments. The evaluation results suggest that the 4VP⁺ platform is a feasible and promising solution for the future computing infrastructure for cloud services.