计算机研究与发展
計算機研究與髮展
계산궤연구여발전
Journal of Computer Research and Development
2015年
9期
2033-2045
,共13页
李鹏远%潘海为%李青%韩启龙%谢晓芹%张志强
李鵬遠%潘海為%李青%韓啟龍%謝曉芹%張誌彊
리붕원%반해위%리청%한계룡%사효근%장지강
关联图%T op-k查询%游走策略%图像检索%医学图像
關聯圖%T op-k查詢%遊走策略%圖像檢索%醫學圖像
관련도%T op-k사순%유주책략%도상검색%의학도상
association graph%Top-k query%walk strategy%image retrieval%medical image
找到与病人具有相似纹理特征的医学图像,有助于医生结合历史病历信息对病人作出更为准确的诊断。基于此,大量的研究工作围绕如何提高基于内容的医学图像检索技术的准确性展开。然而,现有的基于内容的医学图像检索技术均是基于查询图像与数据库中图像的逐张匹配过程,面对迅速增长的医学图像数量,查询等待时间过长成为医学图像检索领域的另一主要问题。鉴于用户往往只对前 k (Top‐k)个检索结果感兴趣,提出了一种基于关联图模型的医学图像 Top‐k查询方法。首先,提出一种关联图模型,使用该模型可以有效地刻画医学图像之间关联关系的模糊性;继而利用关联图模型,提出一系列关联性度量计算方法,从而使得仅需对图像匹配一次即可更新所有图像与查询图像之间的相似度范围。由此,提出Top‐k查询方法以及基于游走的查询优化策略。实验证明提出的方法可以有效地减少图像匹配次数,降低时间复杂度。
找到與病人具有相似紋理特徵的醫學圖像,有助于醫生結閤歷史病歷信息對病人作齣更為準確的診斷。基于此,大量的研究工作圍繞如何提高基于內容的醫學圖像檢索技術的準確性展開。然而,現有的基于內容的醫學圖像檢索技術均是基于查詢圖像與數據庫中圖像的逐張匹配過程,麵對迅速增長的醫學圖像數量,查詢等待時間過長成為醫學圖像檢索領域的另一主要問題。鑒于用戶往往隻對前 k (Top‐k)箇檢索結果感興趣,提齣瞭一種基于關聯圖模型的醫學圖像 Top‐k查詢方法。首先,提齣一種關聯圖模型,使用該模型可以有效地刻畫醫學圖像之間關聯關繫的模糊性;繼而利用關聯圖模型,提齣一繫列關聯性度量計算方法,從而使得僅需對圖像匹配一次即可更新所有圖像與查詢圖像之間的相似度範圍。由此,提齣Top‐k查詢方法以及基于遊走的查詢優化策略。實驗證明提齣的方法可以有效地減少圖像匹配次數,降低時間複雜度。
조도여병인구유상사문리특정적의학도상,유조우의생결합역사병력신식대병인작출경위준학적진단。기우차,대량적연구공작위요여하제고기우내용적의학도상검색기술적준학성전개。연이,현유적기우내용적의학도상검색기술균시기우사순도상여수거고중도상적축장필배과정,면대신속증장적의학도상수량,사순등대시간과장성위의학도상검색영역적령일주요문제。감우용호왕왕지대전 k (Top‐k)개검색결과감흥취,제출료일충기우관련도모형적의학도상 Top‐k사순방법。수선,제출일충관련도모형,사용해모형가이유효지각화의학도상지간관련관계적모호성;계이이용관련도모형,제출일계렬관련성도량계산방법,종이사득부수대도상필배일차즉가경신소유도상여사순도상지간적상사도범위。유차,제출Top‐k사순방법이급기우유주적사순우화책략。실험증명제출적방법가이유효지감소도상필배차수,강저시간복잡도。
Patient‐to‐patient comparison , especially image‐to‐image comparison plays an important role in the medical domain since doctors invariably make diagnoses based on prior experiences of similar cases .It is very significant for doctors to find similar medical images from the database as similar pathological changes in prior patients’ images and corresponding reports can assist doctors to make diagnoses for current patients . T herefore , advanced medical image retrieval techniques have been widely studied to improve the accuracy in recent years . However , the processing time has become another problem in medical image retrieval domain because of the increasing number of medical images .As doctors are only interested in the most similar k results ,a novel model of association graph is proposed for medical image top‐k query in this paper .The fuzzy expression in a association graph can describe the similarity between images effectively . Moreover , a series of correlation measurements are proposed for similarity reasoning .Then the medical image top‐k query method is represented based on the characters of correlation measurements .Furthermore ,four walk strategies are studied to accelerate and stabilize the top‐k process .Experimental results show that its efficiency and effectiveness are higher in comparison with state of the art .