Crawford D C, Bell D S, Bamber J C
Institute of Cancer Research, Royal Marsden Hospital, Sutton, UK.
Ultrasound Med Biol. 1993;19(6):469-85. doi: 10.1016/0301-5629(93)90123-6.
A systematic method to compensate for nonlinear amplification of individual ultrasound B-scanners has been investigated in order to optimise performance of an adaptive speckle reduction (ASR) filter for a wide range of clinical ultrasonic imaging equipment. Three potential methods have been investigated: (1) a method involving an appropriate selection of the speckle recognition feature was successful when the scanner signal processing executes simple logarithmic compressions; (2) an inverse transform (decompression) of the B-mode image was effective in correcting for the measured characteristics of image data compression when the algorithm was implemented in full floating point arithmetic; (3) characterising the behaviour of the statistical speckle recognition feature under conditions of speckle noise was found to be the method of choice for implementation of the adaptive speckle reduction algorithm in limited precision integer arithmetic. In this example, the statistical features of variance and mean were investigated. The third method may be implemented on commercially available fast image processing hardware and is also better suited for transfer into dedicated hardware to facilitate real-time adaptive speckle reduction. A systematic method is described for obtaining ASR calibration data from B-mode images of a speckle producing phantom.
为了优化适用于广泛临床超声成像设备的自适应散斑减少（ASR）滤波器的性能，研究了一种补偿单个超声B型扫描仪非线性放大的系统方法。研究了三种潜在方法：（1）当扫描仪信号处理执行简单对数压缩时，一种涉及适当选择散斑识别特征的方法是成功的；（2）当算法以全浮点运算实现时，B模式图像的逆变换（解压缩）有效地校正了图像数据压缩的测量特性；（3）发现在散斑噪声条件下表征统计散斑识别特征的行为是在有限精度整数运算中实现自适应散斑减少算法的首选方法。在这个例子中，研究了方差和均值的统计特征。第三种方法可以在市售的快速图像处理硬件上实现，也更适合转移到专用硬件中以促进实时自适应散斑减少。描述了一种从散斑产生体模的B模式图像中获取ASR校准数据的系统方法。
