首页> 外国专利> METHOD AND SYSTEM FOR AUTOMATICALLY DETECTING LUNG NODULES FROM MULTI-SLICE HIGH RESOLUTION COMPUTED TOMOGRAPHY (MSHR CT) IMAGES

METHOD AND SYSTEM FOR AUTOMATICALLY DETECTING LUNG NODULES FROM MULTI-SLICE HIGH RESOLUTION COMPUTED TOMOGRAPHY (MSHR CT) IMAGES

机译:自动从多层高分辨率CT图像中自动检测肺结节的方法和系统

摘要

A method for automatically detecting lung nodules from MSHR CT images includes defining a volume of interest (VOI) for a lung volume in an MSHR CT image (314). The lung volume is examined using the VOI (316), including, determining a local histogram of intensity (316a) and adaptive threshold values for segmenting the VOI to obtain seeds (316d). Each seed is examined to detect lung nodules therefrom (318), including segmenting anatomical structures represented by the seed by applying a segmentation method that adaptively adjusts a segmentation threshold value based on histogram analysis of the seed to extract the structures based on three-dimensional connectivity and histogram intensity information (318a), and classifying each structure as a lung nodule or a non-nodule based on a priori knowledge corresponding to lung nodules and related structures (320). The lung nodules are displayed (326). The lung nodules are analyzed (328), including automatically quantifying lung nodule features to provide an automatic detection decision (328a).
机译:一种用于从MSHR CT图像中自动检测肺结节的方法,包括为MSHR CT图像中的肺体积定义感兴趣体积(VOI)(314)。使用VOI(316)检查肺体积,包括确定强度的局部直方图(316a)和用于分割VOI以获得种子的自适应阈值(316d)。检查每个种子以检测其中的肺结节(318),包括通过应用基于种子的直方图分析自适应地调整分割阈值的分割方法来分割由种子表示的解剖结构,以基于三维连通性提取结构直方图强度信息和直方图强度信息(318a),并基于与肺结节和相关结构相对应的先验知识将每个结构分为肺结节或非结节(320)。显示肺结节(326)。分析肺结节(328),包括自动量化肺结节特征以提供自动检测决策(328a)。

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