Haiwei Pan

Work place: College of Computer Science and Technology Harbin Engineering University Harbin, China

E-mail: heaven_007cn@yahoo.com.cn


Research Interests: Computer systems and computational processes, Data Mining, Database Management System, Multimedia Information System, Data Compression, Data Structures and Algorithms


Haiwei Pan. I was born in July, 1974. I received my Ph.D. degree from the Department of Computer Science and Technology at Harbin Institute of Technology in 2006. My research interests include Parallel database, multimedia data mining, data warehouse and massive data process. I am currently an Assistant Professor in the College of Computer Science and Technology at Harbin Engineering University. I teach “Algorithm Design and Analysis” and “Graph Theory” for undergraduate students, and “Combinatorial Mathematics” for graduate students. I have published more than 20 Conference papers and Journal papers. My research has been supported by the National Natural Science Foundation of China, the Natural Science Foundation of Heilongjiang Province, the Fundamental Research Funds for the Central Universities.


Author Articles
A Domain Knowledge Based Approach for Medical Image Retrieval

By Haiwei Pan Xiaolei Tan Qilong Han Guisheng Yin

DOI: https://doi.org/10.5815/ijieeb.2011.03.03, Pub. Date: 8 Jun. 2011

The high incidence of brain disease, especially brain tumor, has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors’ diagnosis. Image mining is the important branch of data mining. It is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Image clustering and similarity retrieval are two basilic parts of image mining. In this paper, we introduce a notion of image sequence similarity patterns (ISSP) for medical image database. ISSP refer to the longest similar and continuous sub-patterns hidden in two objects each of which contains an image sequence. These patterns are significant in medical images because the similarity for two medical images is not important, but rather, it is the similarity of objects each of which has an image sequence that is meaningful. We design the new algorithms with the guidance of the domain knowledge to discover the possible Space-Occupying Lesion (PSO) in brain images and ISSP for similarity retrieval. Our experiments demonstrate that the results of similarity retrieval are meaningful and interesting to medical doctors.

[...] Read more.
Other Articles