膀胱赘生物影像学特征的计算机辅助诊断  

Computer-aided Diagnosis of Bladder Neoplasm by Using Imaging Features

作　　者：史正星[1] 崔光彬[2] 张国鹏[1] 卢虹冰[1] 

机构地区：[1]第四军医大学生物医学工程系,西安710032 [2]第四军医大学唐都医院,西安710038

出　　处：《医疗卫生装备》2009年第8期6-8,共3页Chinese Medical Equipment Journal

基　　金：国家自然科学基金(60772020)

摘　　要：目的:从影像图像中挖掘深层次诊断信息,为膀胱赘生物的计算机辅助诊断提供理论依据。方法:对膀胱赘生物磁共振图像予以降噪、锐化等处理,选取平均灰度强度、熵、均匀度作为图像纹理特征;计算感兴趣区域中膀胱赘生物与正常膀胱壁组织的纹理特征,最后将数据进行统计分析。结果:纹理特征中熵与均匀度t检验P<0.05,提示这些纹理特征在膀胱赘生物(肿瘤)和正常膀胱壁组织之间存在显著性差异,而平均灰度强度t检验P>0.05,故不能认为该特征在两种组织间存在显著性差异。结论:上述纹理特征在两种组织之间明显不同,使它们可用于膀胱肿瘤浸润膀胱壁深度的判断,进而可确定肿瘤的分级。Objective To provide theoretical evidences for diagnosis of bladder neoplasm by using more information of imaging features. Methods MRI images of bladder neoplasm were collected for this research. The ROI （region of interest） area was selected manually, and noise reduction and sharpening were applied to the ROI area by using LoG （Laplaeian of Gaussian） filter. The texture features of bladder neoplasm and normal bladder wall （smooth muscle）, such as mean grey- level intensity, entropy, uniformity were calculated. A statistical analysis was made at last. Results The values of texture features were analyzed by t-test. Entropy and uniformity show significant differences between the two groups. But Mean grey-level intensity hasn＇t indicated this difference. Conclusion This texture features may be applied to decide the iuvasive depth of bladder neoplasm, it also means that the stage of bladder neoplasm may be fixed by this system.

关 键 词：膀胱赘生物 影像学特征 计算机辅助诊断 

分 类 号：R737.14[医药卫生—肿瘤]