摘要:
A method for computer-assisted interpretation of medical images that factor in characteristics of an individual performing the interpretation. The method automatically determines and/or incorporates prevalence-based computer analysis based on an estimated likelihood of a pathological state, e.g., a malignancy. A system implementing the method includes the calculation of features or other characteristics of images in a known database, calculation of features of an unknown case, calculation of the probability (or likelihood) of disease state, calculation of the modified computer output that includes the internal prevalence (or internal decision-making process) of the user (or group of users), and output of the result.
摘要:
A method for automated analysis of abnormalities in the form of lesions and parenchymal distortions using digital images, including generating image data from respective of digital images derived from at least one selected portion of an object, for example, from mammographical digital images of the left and right breasts. The image data from each of the digital images are then correlated to produce correlated data in which normal anatomical structured background is removed. The correlated data is then searched using one or more predetermined criteria to identify in at least one of the digital images an abnormal region represented by a portion of the correlated data which meets the predetermined criteria. The location of the abnormal region is then indicated, and the indicated location is then subjected to classification processing to determine whether or not the abnormal region is benign or malignant. Classification is performed based on the degree of spiculations of the identified abnormal region. In order to enhance the process of searching for abnormal regions, in one embodiment the gray-level frequency-distributions of two or more images are matched by matching the cumulative gray-level histograms of the images in question.
摘要:
A method for in-room computer reconstruction of a three-dimensional (3-D) coronary arterial tree from routine biplane angiograms acquired at arbitrary angles and without using calibration objects. The method includes eight major steps: (1) acquiring biplane projection images of the coronary structure, (2) detecting, segmenting and identifying vessel centerlines and constructing a vessel hierarchy representation, (3) calculating bifurcation points and measuring vessel diameters in coronary angiograms if biplane imaging geometry data is not available, (4) determining biplane imaging parameters in terms of a rotation matrix R and a unit translation vector t based on the identified bifurcation points, (5) retrieving imaging parameters if biplane imaging geometry data is already known, (6) establishing the centerline correspondences of the two-dimensional arterial representations, (7) calculating and recovering the 3-D coronary arterial tree based on the calculated biplane imaging parameters, correspondences of vessel centerlines, and vessel diameters, and (8) rendering the reconstructed 3-D coronary tree and estimating an optimal view of the vasculature to minimize vessel overlap and vessel foreshortening.
摘要翻译:一种三维(3-D)冠状动脉树的室内计算机重构方法,用于从任意角度采集的常规双平面血管造影图像,而不使用校准对象。 该方法包括八个主要步骤:(1)获取冠状结构的双平面投影图像,(2)检测,分割和识别血管中心线并构建血管层级表示,(3)计算冠状动脉造影图中的分叉点和测量血管直径,如果 双平面成像几何数据不可用,(4)基于所识别的分叉点来确定旋转矩阵R和单位平移向量+ E,rar t + EE的双平面成像参数,(5)如果双平面成像 几何数据是已知的,(6)建立二维动脉表现的中心线对应关系,(7)基于计算的双平面成像参数,血管中心线对应关系和血管直径计算和恢复三维冠状动脉树 ,和(8)渲染重建的3-D冠状动脉树并估计脉管系统的最佳视图以使血管最小化 重叠和血管缩短。
摘要:
A novel method for determination of 3-D structure in biplane angiography, including determining the distance of a perpendicular line from the focal spots of respective x-ray sources to respective image planes and defining the origin of each biplane image as the point of intersection with the perpendicular line thereto, obtaining two biplane digital images at arbitrary orientations with respect to an object, identifying at least 8 points in both images which correspond to respective points in the object, determining the image coordinates of the 8 or more identified object points in the respective biplane images, constructing a set of linear equations in 8 unknowns based on the image coordinates of the object points and based on the known focal spot to image plane distances for the two biplane images; solving the linear equations to yield the 8 unknowns, which represent the fundamental geometric parameters of the biplane imaging system; using the fundamental parameters to calculate the 3-dimensional positions of the object points identified in the biplane images; and determination of the 3-D positions of the vessel segments between the object points.