CAJ | 학술논문

背景:基于内容的医学图像检索是一门涉及多领域的学科,由于各种医学图像的成像原理不同,产生的图像在颜色、纹理和形状等视觉特征方面存在差别,使得此方法的实现还存在许多需要解决的问题.目的:针对基于内容的医学图像检索中存在特征提取困难、检索时间长的问题,提出一种基于图割与粗糙集结合的相似图像检索方法.方法:为克服图割仅适用于较少象素的图像和倾向于小割集的缺陷,首先对图像进行聚类,然后构建图像的Gomory-Hu割树,按割值大小依次去掉值较小的边,提取出图像的特征子图并构建特征库.为实现快速检索,借助粗糙集对特征库中的特征进行约简,有效减少参与相似性比较的特征数量.并将此方法应用到MRI脑部肿瘤图像的检索.结果与结论:实验结果表明该方法能快速有效地检索出MRI脑部图像库中的肿瘤图像,检索的平均查准率为78.4%,平均查全率为62.9%.
배경:기우내용적의학도상검색시일문섭급다영역적학과,유우각충의학도상적성상원리불동,산생적도상재안색、문리화형상등시각특정방면존재차별,사득차방법적실현환존재허다수요해결적문제.목적:침대기우내용적의학도상검색중존재특정제취곤난、검색시간장적문제,제출일충기우도할여조조집결합적상사도상검색방법.방법:위극복도할부괄용우교소상소적도상화경향우소할집적결함,수선대도상진행취류,연후구건도상적Gomory-Hu할수,안할치대소의차거도치교소적변,제취출도상적특정자도병구건특정고.위실현쾌속검색,차조조조집대특정고중적특정진행약간,유효감소삼여상사성비교적특정수량.병장차방법응용도MRI뇌부종류도상적검색.결과여결론:실험결과표명해방법능쾌속유효지검색출MRI뇌부도상고중적종류도상,검색적평균사준솔위78.4%,평균사전솔위62.9%.
BACKGROUND: Content-based medical image retrieval involves multiple domains.Due to different imaging principles of various medical images,there are differences in color,texture,and shape,which should be resolved.OBJECTIVE: As in content-based medical image retrieval system,feature extraction from image is very difficult and the retrieval is very time-consuming,a similar image retrieval method based on graph-cuts and rough sets is proposed.METHODS: In order to overcome the defects that graph-cuts is only suitable for small image end easily leads to a small cut-sets,a clustering was applied to image,and the Gomory-Hu cuts tree of image was established.An image feature library was built by removing the edges of Gomory-Hu cuts tree for the value of cut.Reduction of features in library was obtained based on rough sets and the number of features in similar compare decrease.This method was applied to retrieve brain tumor image in MRI brain image database.RESULTS AND CONCLUSION: Results show that this method can effectively retrieve brain tumor images in the library.The average retrieval precision rate and the average recall rates were 78.4% and 62.9%,respectively.