Experimental analysis of the quantitative ultrasonographic findings in maxillo-facial soft tissue structures

这种新的纹理分析方法是基于这样一种直觉，即人类视觉系统的重要纹理信息包含在强度局部极值的相对频率中。在这个过程中的主要措施是确定的数量的局部灰度最大值和最小值沿着一维扫描方向。灰度值首先通过平滑处理发送，该平滑处理消除小幅度的反转，从而仅保留主要极值。平滑算法是熟悉的模拟机械过程，称为齿轮齿隙的数字等价物，它最初被描述为字符recognization.The的组织模仿幻影，正常腮腺和下颌下腺，涎腺炎和唾液腺肿瘤的超声图像的预处理方法。图像被扫描，数字化。在数字化图像中，使用一维Max-Mm测度分析所选ROI。二维扩展测量ROI中的max-mm特征，结果是阈值增加，检测到的极值较少，当阈值超过图像的像素密度范围时，检测不到极值。每幅图像产生一条特征曲线，可用于识别纹理。体模基底材料中相同ROI的每个不同密度图像产生略微不同的曲线。显然，ROT的更大尺寸的区域将给出更小的特征方差和更准确的分类结果。

The new measure for texture analysis is based on the intuition that the important texture information for the human visual system is contained in the relative frequency of local extremas in intensity. The principal measurement in this process is the determination of the number of local gray level maximum and minimum along a one-dimensional scan direction. The gray level values are first sent through a smoothing process which eliminates reversals of small amplitude, thereby retaining only the principal extrema. The smoothing algorithm is the digital equivalent of the familiar analog mechanical process known as gear backlash and it was originally described as a preprocessing method for character recognition.The ultrasonographic images of the tissue-mimicking phantom, normal parotid and submandibular glands, sialadenitis and salivary gland tumors were used. The images were scanned, digitized. In the digitized images, the selected ROI was analysed with the Max-Mm Measure in one dimension. The two-dimensional extension measured the max-mm features in the ROI.As the results, the threshold was increased, fewer extrema were detected and when the threshold exceeded the pixel density range of the image, no extrema were detected. Each image produced a characteristic curve which can be used to identify the texture. Each different density image of the same ROI in the base-material of the phantom produced a slightly different curve. Obviously, a larger size region of the ROT would give less variance in the features and more accurate classification results.