Bartels P H, Bartels H G, Montironi R, Hamilton P W, Thompson D
Optical Sciences Center, University of Arizona, Tucson 85721, USA.
Anal Quant Cytol Histol. 1998 Oct;20(5):358-64.
To explore the utility of N-gram encoding for the automated detection and delineation of regions of histologic abnormality in tissue sections of prostate.
Digitized imagery of tissue sections from normal prostate glandular tissue, stroma and regions of well- and poorly differentiated lesions was recorded and successively subdivided into square subregions of 256 x 256 to 16 x 16 pixels. N-grams of N = 2 to N = 6 were computed, with each element assuming a value representing an optical density interval 0.30 units wide, covering the range from optical density = 0.0 to 1.80. Then, from a large database, prototype frequency histograms of the different N-grams were established. For each subregion the Euclidean distances to the different prototype histograms were computed and defined as "distance to prototype" features. Standard discriminant analyses and a nonparametric classifier were used to assign subregions to the different tissue categories.
Classification of subregions was achieved for most discrimination tasks at a correct recognition rate ranging from 85% to 100% on both training set and test set data, with a few exceptions. N-grams of N > 4 had considerable discriminatory power.
N-gram encoding has the potential to provide highly discriminating, texture-based characterization of subregions of digitized imagery of prostate lesions and may be very useful in the development of decision procedures for the automated detection of prostate lesions by a machine vision system.
探讨N元语法编码在前列腺组织切片中组织学异常区域自动检测和描绘方面的效用。
记录正常前列腺腺组织、基质以及高分化和低分化病变区域的组织切片数字化图像，并依次将其细分为256×256至16×16像素的方形子区域。计算N=2至N=6的N元语法，每个元素取一个代表宽度为0.30单位光密度区间的值，覆盖光密度从0.0至1.80的范围。然后，从一个大型数据库中建立不同N元语法的原型频率直方图。对于每个子区域，计算其与不同原型直方图的欧几里得距离，并将其定义为“到原型的距离”特征。使用标准判别分析和非参数分类器将子区域分配到不同的组织类别。
在训练集和测试集数据上，大多数判别任务的子区域分类正确率在85%至100%之间，有少数例外。N>4的N元语法具有相当大的判别力。
N元语法编码有可能为前列腺病变数字化图像的子区域提供高度判别性的、基于纹理的特征描述，并且在机器视觉系统自动检测前列腺病变的决策程序开发中可能非常有用。
