物理学报
物理學報
물이학보
2013年
8期
088702-1-088702-11
,共1页
姚畅?%陈后金%Yang*Yong-Yi%李艳凤%*韩振中%*张胜君
姚暢?%陳後金%Yang*Yong-Yi%李豔鳳%*韓振中%*張勝君
요창?%진후금%Yang*Yong-Yi%리염봉%*한진중%*장성군
乳腺X线图像%微钙化点簇%相关向量机%自适应核学习
乳腺X線圖像%微鈣化點簇%相關嚮量機%自適應覈學習
유선X선도상%미개화점족%상관향량궤%자괄응핵학습
mammogram%microcalcification cluster%relevance vector machine%adaptive kernel learning
采用自适应核学习相关向量机方法,结合形态学滤波和Kallergi分簇标准,研究了乳腺X线图像中微钙化点簇的处理.首先将微钙化点检测看作一个监督学习问题,然后应用自适应核学习相关向量机作为分类器判断图像中每一个位置是否为微钙化点并采用形态学处理滤除干扰噪声,最后对获得的微钙化点采用Kallergi标准进行分簇.为提高运算速度,在微钙化点检测时将整个图像分解为多个子图像并行运算,实现了一种基于自适应核学习相关向量机的微钙化点簇快速处理方法.实验结果和分析表明,自适应核学习相关向量机方法算法性能优于相关向量机方法,特别是实现的快速方法能进一步降低微钙化点簇的处理时间.
採用自適應覈學習相關嚮量機方法,結閤形態學濾波和Kallergi分簇標準,研究瞭乳腺X線圖像中微鈣化點簇的處理.首先將微鈣化點檢測看作一箇鑑督學習問題,然後應用自適應覈學習相關嚮量機作為分類器判斷圖像中每一箇位置是否為微鈣化點併採用形態學處理濾除榦擾譟聲,最後對穫得的微鈣化點採用Kallergi標準進行分簇.為提高運算速度,在微鈣化點檢測時將整箇圖像分解為多箇子圖像併行運算,實現瞭一種基于自適應覈學習相關嚮量機的微鈣化點簇快速處理方法.實驗結果和分析錶明,自適應覈學習相關嚮量機方法算法性能優于相關嚮量機方法,特彆是實現的快速方法能進一步降低微鈣化點簇的處理時間.
채용자괄응핵학습상관향량궤방법,결합형태학려파화Kallergi분족표준,연구료유선X선도상중미개화점족적처리.수선장미개화점검측간작일개감독학습문제,연후응용자괄응핵학습상관향량궤작위분류기판단도상중매일개위치시부위미개화점병채용형태학처리려제간우조성,최후대획득적미개화점채용Kallergi표준진행분족.위제고운산속도,재미개화점검측시장정개도상분해위다개자도상병행운산,실현료일충기우자괄응핵학습상관향량궤적미개화점족쾌속처리방법.실험결과화분석표명,자괄응핵학습상관향량궤방법산법성능우우상관향량궤방법,특별시실현적쾌속방법능진일보강저미개화점족적처리시간.
@@@@Using the method of adaptive kernel learning based relevance vector machine (ARVM) and combining the morphological filtering and the clustering criterion recommended by Kallergi, a new algorithm for microcalcification (MC) clusters processing in mammo-grams is investigated. Firstly, the detection of MC is formulated as a supervised-learning problem. Then the ARVM is used as a classifier to determine whether an MC object is present at each location in the mammogram and a morphological processing is used to remove the isolated spurious pixels. Finally, the identified MC clusters are obtained by Kallergi criterion. To improve the compu-tational speed, a fast processing method based on ARVM is developed, in which the whole image is decomposed first into sub-image blocks for parallel operation. Experimental results indicate that the ARVM method outperforms the RVM method and, in particular, the fast processing method could greatly reduce the testing time.
