【作者】 于劲；

【导师】 许向阳；

【作者基本信息】 华中科技大学 ， 计算机软件与理论， 2007， 硕士

【摘要】 图像拼接技术是数字图像处理中的一个重要研究内容,可用于解决视域局限问题。借助显微镜观察肝脏切片是诊断肝脏疾病的一种常用方法,为了更准确的诊断疾病,需要对肝脏病理切片的一些特征进行量化分析,肝脏病理切片显微图像拼接是对整个切片量化分析的基础。为了得到较高的拼接精度和较快的拼接速度,采用了由粗到精的拼接框架,即先拼接抽样的子图像,然后再实现原图像的拼接。子图像拼接采用基于特征的图像拼接方法,针对获取的图像中存在的旋转、模糊等问题,采用具有尺度不变性的方法检测特征点,通过主方向标定等手段保证提取的特征点描述符具有旋转不变性,根据特征描述符的相似性得到初步的匹配点对集合。以仿射变换为运动模型,使用随机抽样一致性检验从匹配点对集合中拟合出两幅图像的几何变换参数,得出子图像的大致重合位置之后对原始图像的几何变换参数进行精确估计,最后根据几何变换参数对图像进行对齐,并对对齐后的图像进行羽化处理得到最终的拼接图像。对150个病例切片(一千多幅图像)的拼接实验表明,系统具有较高的健壮性和适应性,拼接效果良好。更多还原

【Abstract】 Image stitching plays an important role in digital image processing field, which used to solve the limitation of the viewfield. To make diagnosis of liver disease more accurately, the quantification analysis based on liver biopsy micrograph is often necessary. But because of the limitation of the microscope, the complete liver biopsy micrograph can not be taken by only one time, so the liver biopsy micrograph stitching is essential in quantification analysis.To get better accuracy and speed, a coarse-to-fine framework is used in the architecture. First we stitch the sampled image to get the coarse transforming parameter, then the parameter is refined in original image. To solve the image blurring between two adjoining images, we use scale-invariant feature detecting algorithm to get the interest point, and use orientation labeling method to make the feature descriptor rotation invariant, in the next step, we get the initial matching points set based on the similarity of the feature descriptor. Then use affine transforming as base model, use random sampling consensus to estimate the transforming parameter between two images, after get the approximate overlapping area between the two images, then we refining the parameter on the original image. Based on the parameter, the sequential image will be aligned and blended to get the final image with complete viewfield.Experiment on 150 cases (more than 1000 images) shows that the system is robust and adaptive, the result is good on the whole.更多还原