Aobing Sun

Content and semantic context based image retrieval for medical image grid

Physicians create Medical Image Libraries (MILs) to collect typical case images, and utilize CBIR (Content-based Image Retrieval) tools to search feature-similar samples within them to aid clinical intervention and diagnoses. This paper presents a CSBIR (Content and Semantic Context based Image Retrieval) scheme for MedlmGrid (Medical Image Grid) to tackle the sharing difficulties of special and heterogeneous MILs within wide areas. The scheme encapsulates distributed CBIR engines, MILs and their metadata as WS (Web Services), and links them in the grid environment. It combines CBIR and semantic context of images to automatically choose the optimal WS set to serve users. Our integrated-features based CSBIR engine for thorax CR (Computer Radiology Image) is related as one instance, which can merge the superiorities of randomly selected CBIR services. MedlmGrid CSBIR prototype is based on CGSP (ChinaGrid Supporting Platform) and its loosely coupled structure makes the integration of decentralized CBIR systems within or across hospitals more efficiently.

MIGP: medical image grid platform based on HL7 grid middleware

MIGP (Medical Image Grid Platform) realizes information retrieval and integration in extensive distributed medical information systems, which adapts to the essential requirement for the development of healthcare information infrastructure. But the existing MIGPs, which are constructed mostly based on database middleware, are very difficult to guarantee local hospital data security and remote accessing legality. In this paper, a MIGP based on the WSRF-compliant HL7 (Health Level 7) grid middleware is proposed, which aims to combine the existing HL7 protocol and grid technology to realize medical data and image retrieval through the communications and interoperations with different hospital information systems. We also design the architecture and bring forward a metadata-based scheduling mechanism for our grid platforms. At last, experimental MIGPs are constructed to evaluate the performance of our method.

Ontology-based semantic integration scheme for medical image grid

Ontology is becoming a key for grid platform to support the composition of heterogeneous resources by means of processing resource description and enactment. MedlmGrid (Medical Image Grid) aims to archive, access, and analyze medical data from distributed healthcare information systems to adapt to the development of healthcare information infrastructure. But the heterogeneities of those systems, especially the semantic gulfs, hamper their interoperations in grid environment. In this paper, we propose an OSIS (ontology-based semantic integration scheme) for MedlmGrid, which adopts a hybrid method to build MedlmGrid ontologies and unifies its information exchange model with HL7 (Health Level 7) v3 protocol. The MedlmGrid ontologies share the same vocabulary to simplify the knowledge discovery and semantic transformation within distributed environment. The rule-based ontology mapping components are also designed to support semantic operations of MedlmGrid. We test the performances of our scheme with simulation experiments to evaluate the feasibility of our approach.

VAST: Automatically Combining Keywords and Visual Features for Web Image Retrieval

A large-scale image retrieval system for the WWW, named VAST (VisuAl & SemanTic image search), is presented in this paper. Based on the existing inverted file and visual feature clusters, we form a semantic network on top of the keyword association on the visual feature clusters. The system is able to automatically combine keyword and visual features for retrieval by the semantic network. The combination is automatic, simple, and very fast, which is suitable for large-scale Web dataset. Meanwhile, the retrieval takes advantage of the semantic contents of the images in addition to the low-level features, which remarkably improves the retrieval precision. The experimental results demonstrate the superiority of the system.

Ontology-based Semantic Integration Scheme for Medical Image Grid

Ontology is becoming a key for grid platform to support the composition of heterogeneous resources by means of processing resource description and enactment. MedlmGrid (Medical Image Grid) aims to archive, access, and analyze medical data from distributed healthcare information systems to adapt to the development of healthcare information infrastructure. But the heterogeneities of those systems, especially the semantic gulfs, hamper their interoperations in grid environment. In this paper, we propose an OSIS (ontology-based semantic integration scheme) for MedlmGrid, which adopts a hybrid method to build MedlmGrid ontologies and unifies its information exchange model with HL7 (Health Level 7) v3 protocol. The MedlmGrid ontologies share the same vocabulary to simplify the knowledge discovery and semantic transformation within distributed environment. The rule-based ontology mapping components are also designed to support semantic operations of MedlmGrid. We test the performances of our scheme with simulation experiments to evaluate the feasibility of our approach.

UCIPE: ubiquitous context-based image processing engine for medical image grid

Medical diagnosis and intervention increasingly rely upon medical image processing tools that are bound with high-cost hardware, designed for special diseases, and incapable of being shared by common medical terminals. In this paper, we present our Ubiquitous Context-based Image Processing Engine (UCIPE) for MedImGrid (Medical Image Grid). It encapsulates image processing algorithms as WS (Web Services), and creates virtual algorithm barn to store their metadata. The user contexts are captured by UCIPE clients and used as clues to search the optimal WS from the algorithm barn. UCIPE supports all-weather accesses (anytime, anywhere, with any means) of numerous multiple terminals to guarantee the algorithm resources be accessed transparently. The UCIPE prototype for MedImGrid is based on CERNET2. The performances of UCIPE-based 3D reconstruction verify the feasibility of UCIPE and prove the improvement of IPv6 network to medical gird applications.

Unifying keywords and visual features within one-step search for web image retrieval

The multi-modal characteristics of Web image make it possible to unify keywords and visual features for image retrieval in Web context. Most of the existing methods about the integration of these two features focus on the interactive relevance feedback technique, which needs the user’s interaction (i.e. a two-step interactive search). In this paper, an approach based on association rule and clustering techniques is proposed to unify keywords and visual features in a different manner, which seamlessly implements the integration within one-step search. The proposed approach considers both Query By Keyword (QBK) mode and Query By Example (QBE) mode and need not the user’s interaction. The experiment results show the proposed approach remarkably improve the retrieval performance compared with the pure search only based on keywords or visual features, and achieve a retrieval performance approximate to the two-step interactive search without requiring the user’s additional interaction.

Pulmonary Disease Census Aiding System Based on Medical Image Grid

The large population exerts high burdens to Chinese health census works. In this paper, we propose our PDCAS (Pulmonary Disease Census Aiding System) based on Medical Image Grid, which aims to utilize the superiorities of grid technology to improve the efficiency of high-incidence and occupational pulmonary disease census. PDCAS integrates the individual medical information distributed in different hospitals’ information systems into Medical Information Centre of one area. The census records are classified through one risk rate based cross clustering model to direct the medical diagnosis and review. The main processing algorithms of PDCAS are subdivided and encapsulated as detachable web services with adapted granularity to support the grid workflow composition corresponding to different pulmonary diseases or aiding aims. The prototype of PDCAS proves the possible improvement of grid technology to diseases census and other data intensive medical applications.

Multiple-Access Channel Model for P2P Networks

Peer-to-peer (P2P) technology encounters serious methodological limitations to guarantee the quality of services (QoS) of P2P networks with very few peers. In this paper, we propose multiple-access channel model (MACM) to merge different P2P networks as a whole to share their data-transfer capability. The model abstracts the interlaced data links between peers as logical data channels according to their spatial-temporal relations. The channels can be subdivided based on multiplex technology and serve different applications at same time. MACM redefines P2P data-packet structure and organizes data packets into time-related groups according to cascade-packet rule to support controllable data-projection between data pools of peers. MACM-based P2P networks can be created as virtual sub-network or virtual-peer bridging strategy so as to improve the performance of P2P networks with few peers but high priorities within Internet protocol television (IPTV) and wireless sensor network (WSN) applications.