利用ITK工具完成了医学数字成像及通信(Digital Imaging and Communications in Medicine,DICOM)标准中的RT结构集(Radiotherapy Structure Set)对象的创建,实现了从带有病灶的DICOM图像(以CT图像为例)到RT结构集文件创建整个过程,建立...利用ITK工具完成了医学数字成像及通信(Digital Imaging and Communications in Medicine,DICOM)标准中的RT结构集(Radiotherapy Structure Set)对象的创建,实现了从带有病灶的DICOM图像(以CT图像为例)到RT结构集文件创建整个过程,建立了整个流程模型。DICOM标准中的RT结构集对象主要用于传送病人结构和相关数据,在结构集信息实体中,其主要包含结构集模块、医生感兴趣区域轮廓模块(ROI Contour)和感兴趣区域观察模块(ROI Observations)等。可以用ITK工具实现自动勾画医生感兴趣区域的操作,从而抽取出轮廓数据(Contour data),创建RT结构集文件,为进一步实现整个治疗计划系统开发打下坚实基础。展开更多
We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active con...We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard devia- tion textural feature and a 5x5 kernel, our curve evolution was able to produce results close to the minimal area error rate （namely 8.88% for breast images and 10.82% for liver images）. The image resolution was evaluated using the con- trast-to-gradient method. The experiments showed promising segmentation results.展开更多
基金supported by the Project SOP HRD-EFICIENT 61445/2009 of University Dunarea de Jos of Galati,Romania
文摘We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard devia- tion textural feature and a 5x5 kernel, our curve evolution was able to produce results close to the minimal area error rate （namely 8.88% for breast images and 10.82% for liver images）. The image resolution was evaluated using the con- trast-to-gradient method. The experiments showed promising segmentation results.
