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通过堵漏法进行超声下乳腺肿块的弥散边界提取。

Diffuse boundary extraction of breast masses on ultrasound by leak plugging.

作者信息

Cary T W, Conant E F, Arger P H, Sehgal C M

机构信息

Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

出版信息

Med Phys. 2005 Nov;32(11):3318-28. doi: 10.1118/1.2012967.

Abstract

We propose a semiautomated seeded boundary extraction algorithm that delineates diffuse region boundaries by finding and plugging their leaks. The algorithm not only extracts boundaries that are partially diffuse, but in the process finds and quantifies those parts of the boundary that are diffuse, computing local sharpness measurements for possible use in computer-aided diagnosis. The method treats a manually drawn seed region as a wellspring of pixel "fluid" that flows from the seed out towards the boundary. At indistinct or porous sections of the boundary, the growing region will leak into surrounding tissue. By changing the size of structuring elements used for growing, the algorithm changes leak properties. Since larger elements cannot leak as far from the seed, they produce compact, less detailed boundary approximations; conversely, growing from smaller elements results in less constrained boundaries with more local detail. This implementation of the leak plugging algorithm decrements the radius of structuring disks and then compares the regions grown from them as they increase in both area and boundary detail. Leaks are identified if the outflows between grown regions are large compared to the areas of the disks. The boundary is plugged by masking out leaked pixels, and the process continues until one-pixel-radius resolution. When tested against manual delineation on scans of 40 benign masses and 40 malignant tumors, the plugged boundaries overlapped and correlated well in area with manual tracings, with mean overlap of 0.69 and area correlation R2 of 0.86, but the algorithm's results were more reproducible.

摘要

我们提出了一种半自动种子边界提取算法,该算法通过查找和填补扩散区域边界的漏洞来描绘其边界。该算法不仅能提取部分扩散的边界,还能在这个过程中找到并量化边界中扩散的部分,计算局部清晰度测量值,以供计算机辅助诊断使用。该方法将手动绘制的种子区域视为像素“流体”的源泉,这种流体从种子向外流向边界。在边界不清晰或多孔的部分,生长区域会渗入周围组织。通过改变用于生长的结构元素的大小,算法可以改变渗漏特性。由于较大的元素不会从种子处渗漏太远,它们会产生紧凑、细节较少的边界近似;相反,从小元素开始生长会导致边界限制较少,局部细节更多。这种渗漏填补算法的实现方式是减小结构圆盘的半径,然后比较从它们生长出来的区域,这些区域在面积和边界细节上都会增加。如果生长区域之间的流出量与圆盘面积相比很大,则可识别出渗漏。通过屏蔽渗漏像素来填补边界,这个过程会持续到达到单像素半径分辨率。在对40个良性肿块和40个恶性肿瘤的扫描图像上与手动描绘进行测试时,填补后的边界在面积上与手动描绘重叠且相关性良好,平均重叠度为0.69,面积相关性R2为0.86,但该算法的结果更具可重复性。

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