| 摘要: |
| 随着脑部疾病(尤其是脑瘤)发生率的逐年上升，通过挖掘脑部医学图像来发现知识对辅助医生的诊断变得越来越重要.图像挖掘是数据挖掘的重要分支，它不仅仅是数据挖掘简单的扩展到图像领域，而是一个多学科交叉的研究方向周像的聚类和相似性搜索是图像挖掘的两个非常重要的领域．针对医学图像数据库引入了图像序列相似模式(ISSP)的概念，对于各自包含一个图像序列的两个对象，ISSP是指隐藏在他们中的最长相似连续子模式．这些模式在医学图像中是很有意义的，因为对医生来说两个对象相似要比两个图像相似更有意义．设计了新的基于领域知识指导下的算法来发现可能性占位(Pso)和ISSP以支持相似性索引．实验表明，该研究对医生的辅助诊断有比较好的效果． |
| 关键词: 数据库 数据挖掘 图像挖掘 聚类 相似性搜索 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No．60273082(国家自然科学基金)；the National high-Tech Research and Development Plan of China under Grant No．2002AA444110(国家高技术研究发展计划(863))；the National Grand Fundamental Research 973 Program ofChina under Grant No．G1999032704(国家重点基础研究发展规划(973)) |
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| Mining Image Sequence Similarity Patterns in Brain Images |
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PAN Hai-Wei,LI Jian-Zhong,ZHANG Wei
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| Abstract: |
| 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 aiddoctors’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 ofthe domain knowledge to discover the possible space occupying(PSO)in bain images and ISSP for similarity retrieval．The experimental results demonstrate that the results of similarity retrieval are meaningful and interesting to medical doctors． |
| Key words: database data mining image mining clustering similarity retrieval |
