Lanfen Lin

Developing manufacturing ontologies for knowledge reuse in distributed manufacturing environment

The use of ontologies for knowledge sharing and distributed collaboration has been widely recognised in the knowledge modelling community, but the lack of a systematic and constructive methodology for developing manufacturing ontologies has impeded their wide usage for knowledge reuse in distributed manufacturing environments. This paper presents a constructive, two-level knowledge modelling approach to systematically develop manufacturing ontologies using both software engineering and Semantic Web paradigms. The UML/OCL (Unified Modeling Language/Object Constraint Language)-based object modelling is used first to serve as a graphical and structured basis for conceptual communication between domain experts and knowledge engineers. The OWL/SWRL (Web Ontology Language/Semantic Web Rule Language)-based ontology modelling then extends the UML/OCL-based object models with added semantics using a progressive, semantics-oriented knowledge acquisition method. An illustrative example for manufacturing ontology development in the manufacturing industry for producing electronic connectors is used to demonstrate the practicality of the proposed approach.

Simultaneous Segmentation of Multiple Organs Using Random Walks

Random walks-based (RW) segmentation methods have been proven to have a potential application in segmenting the medical image with minimal interactive guidance. However, the approach leads to large-scale graphs due to number of nodes equal to voxel number. Also, segmentation is inaccurate because of the unavailability of appropriate initial seed points. It is a challenge to use the RW-based segmentation algorithm to segment organ regions from 3D medical images interactively. In this paper, a knowledge-based segmentation framework for multiple organs is proposed based on random walks. This method employs the previous segmented slice as prior knowledge (the shape and intensity constraints) for automatic segmentation of other slices, which can reduce the graph scale and significantly speed up the optimization procedure of the graph. To assess the efficiency of our proposed method, experiments were performed on liver tissues, spleen tissues and hepatic cancer and it was extensively evaluated both quantitatively and qualitatively. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for multi-organ segmentation (p < 0.001).

Segmentation of liver and spleen based on computational anatomy models

Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study because of the single reference. Taking these into consideration, a template matching framework based on an iterative probabilistic atlas for liver and spleen segmentation is presented in this paper. First, a bounding box based on human anatomical localization, which refers to the statistical geometric location of the organ, is detected for the candidate organ. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. We applied our method to 60 datasets including normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 0.930/0.870, the root-mean-squared error (RMSE) was 2.906mm. For the spleen, quantification led to 0.922 Dice/0.857 Tanimoto overlaps, 1.992mm RMSE. The algorithm is robust in segmenting normal and abnormal spleens and livers, such as the presence of tumors and large morphological changes. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multi-organs (p<0.00001).

Research on Ontology-Based Multi-source Engineering Information Retrieval in Integrated Environment of Enterprise

With the extensive use of computer aided systems in product design, manufacture and analysis, a mass of documents are produced. These documents provide engineering information in integrated environment of enterprise for engineers, but maintained by different systems in different forms. Current information retrieval approaches usually lack semantic supports for different kinds of documents, which leads to insufficiency of content representation and misunderstanding of query intention. To effectively search engineering information in various documents, and consider semantic information in engineering domain, an ontology-based information retrieval framework is presented. As the center of framework, ontologies are established to support document analysis and query processing through information representation in semantic level, and complete the mapping between user queries and document resources. The framework provides a unified platform for multi-source engineering information retrieval(EIR) from various documents in integrated environment.

Towards a general ontology of multidisciplinary collaborative design for Semantic Web applications

While much of multidisciplinary design knowledge can be found over the Internet, current engineering modelling techniques have limited capabilities of knowledge sharing and distributed problem solving because of a lack of a common understanding of design rationales across disciplines. This paper describes a preliminary attempt at using a Semantic Web paradigm as a step towards a general ontology of multidisciplinary collaborative design, which is needed to share, exchange and reuse multidisciplinary design knowledge in a distributed design environment. The ontology presented consists of a rich set of class constructs with cross-disciplinary mapping features for annotating the multidisciplinary engineering models on the Semantic Web so that the annotated models become machine understandable, facilitating the data consistency and knowledge reuse for collaborative work among multidisciplinary organisations. A case study for representing a general ontology of multidisciplinary design of automatic assembly systems for cross-disciplinary collaboration in the prototype system is shown to validate the implementation of the proposed approach. The developed ontology provides hierarchical conceptual interrelationships of diverse design concepts, so that a design consensus is fostered to facilitate semantic annotation, access and retrieval of engineering models across different disciplines.

Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images

PurposeThe bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis.MethodsThis paper presents a texture-specific BoVW method to represent focal liver lesions (FLLs). Pixels in the region of interest (ROI) are classified into nine texture categories using the rotation-invariant uniform local binary pattern method. The BoVW-based features are calculated for each texture category. In addition, a spatial cone matching (SCM)-based representation strategy is proposed to describe the spatial information of the visual words in the ROI. In a pilot study, eight radiologists with different clinical experience performed diagnoses for 20 cases with and without the top six retrieved results. A total of 132 multiphase computed tomography volumes including five pathological types were collected.ResultsThe texture-specific BoVW was compared to other BoVW-based methods using the constructed dataset of FLLs. The results show that our proposed model outperforms the other three BoVW methods in discriminating different lesions. The SCM method, which adds spatial information to the orderless BoVW model, impacted the retrieval performance. In the pilot trial, the average diagnosis accuracy of the radiologists was improved from 66 to 80% using the retrieval system.ConclusionThe preliminary results indicate that the texture-specific features and the SCM-based BoVW features can effectively characterize various liver lesions. The retrieval system has the potential to improve the diagnostic accuracy and the confidence of the radiologists.

Bag of temporal co-occurrence words for retrieval of focal liver lesions using 3D multiphase contrast-enhanced CT images

Computer-aided diagnosis (CAD) systems have been verified to have the potential to assist radiologists in clinical diagnosis to detect and characterize focal liver lesions (FLLs) based on single- or multiphase contrast-enhanced computed tomography (CT) images. Features extracted from multiphase contrast-enhanced CT images carry more important diagnostic information i.e. enhancement pattern and demonstrate much stronger discriminative ability compared to those of single-phase CT images. In this paper, we propose a new method for multiphase image feature generation called the bag of temporal co-occurrence words (BoTCoW). A temporal co-occurrence image connecting intensity from multiphase images is constructed. Then the bag of visual word (BoVW) model is employed on the temporal co-occurrence images to extract temporal features. The proposed method effectively captures temporal enhancement information and demonstrates the distribution of the evolution patterns. The effectiveness of this method is validated in a retrieval system using 132 FLLs with confirmed pathology type. The preliminary results show that the proposed BoTCoW method outperforms the previously proposed temporal features and multiphase features based on the BoVW model.

A semantic query expansion-based patent retrieval approach

Since patent documents are important technical resources, effective patent retrieval has become more and more crucial. Unlike common information retrieval, patent retrieval is a recall-oriented retrieval, and patent query inputs are usually long. However, current patent retrieval approaches cannot effectively capture user query intents and obtain good expansion terms, which lead to low retrieval effectiveness. To address this issue, this paper proposes a novel semantic query expansion-based patent retrieval approach according to patent-specific characteristics. Firstly, patent domain features are extracted by using a domain-dependent term frequency scheme. Based on domain features, query inputs are analyzed to determine query domains. Furthermore, query domain matching is employed to generate candidate expansion terms, and semantic-based similarity computation is adopted to select expansion terms. Experiment results show that our approach achieves better retrieval performance than other state-of-art approaches.

Improving Short Text Classification through Better Feature Space Selection

Nowadays people are overwhelmed by more and more short information from lots of different applications, especially with the rapid development of mobile systems. One way to alleviate this issue is an automatic classification of the short texts before they are delivered to users. Several methods have been proposed to classify the short texts, and they are largely based on expanding the short texts to longer ones with external resources to solve the sparseness problem. Different from these studies, we tackle the sparseness problem by selecting a better feature space in which the feature vectors of the short texts are denser, and our method needs no external resources at all. The experimental results on an open dataset show that this method can significantly improve the short text classification accuracy comparing with the baseline, especially when the dimension of the feature space is low.

Study of networked manufacturing oriented cooperative CAPP system

Networked manufacturing oriented cooperative CAPP system has become one of the hotspots of research and application. The paper mainly studies how to realize cooperative planning among heterogeneous CAPP systems in a networked manufacturing oriented environment. We put forward an idea for cooperative process planning. Then, we study some key technologies of it, including cooperative control command management technology and shared data processing. Finally an example of the networked manufacturing oriented cooperative CAPP system is introduced.