Haiwei Pan

Brain CT Image Similarity Retrieval Method Based on Uncertain Location Graph

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

A Clustering Scheme for Trajectories in Road Networks

Trajectories of moving objects contain numerous information to analyze and exploit. Clustering analysis is an important approach to deal with trajectories in data mining technology. However, existing trajectory clustering algorithm barely considers temporal information during clustering procedure. This paper proposes a clustering scheme for trajectories in road network environment. A trajectory on road network is represented by a sequence of interest points that each point indicates a real location on road segment. We define a distance measure to compute the spatial similarity between trajectories, then we propose a clustering algorithm based on DBSCAN and add temporal factor into it at the same time.

A ROI-Based Mining Method with Medical Domain Knowledge Guidance

Image mining is a growing research focus and is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Mining association rules in medical images is an important part in domain- specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly incorporate the domain knowledge into the ROI extraction algorithm and ROI clustering algorithm, then we extend the concept of association rule based on ROI and image in medical images, and propose two algorithms to discover frequent item-sets and mine interesting association rules from medical images. Some interesting results are obtained by our program and we believe many of the problems we come across are likely to appear in other domains.

OFDP: a distributed algorithm for finding disjoint paths with minimum total length in wireless sensor networks

This paper investigates the MINimum-length-

A Concurrency Control Algorithm Access to Temporal Data in Real-Time Database Systems

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Semi-supervised Multimodal Clustering Algorithm Integrating Label Signals for Social Event Detection

k-Disjoint-Paths (MIN-

An empirical study of filter-based feature selection algorithms using noisy training data

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