Nielsen B, Albregtsen F, Kildal W, Danielsen H E
Department of Informatics, University of Oslo, P.O.Box 1080 Blindern, N-0316 Oslo, Norway.
Anal Cell Pathol. 2001;23(2):75-88. doi: 10.1155/2001/683747.
In order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sections. Each light microscopy nucleus image was divided into a peripheral and a central part, representing 30% and 70% of the total area of the nucleus, respectively. Textural features were then extracted from the peripheral and central parts of the nuclei images.The adaptive feature extraction was based on Class Difference Matrices and Class Distance Matrices. These matrices were useful to illustrate the difference in chromatin texture between the good and bad prognosis classes of ovarian samples. Class Difference and Distance Matrices also clearly illustrated the difference in texture between the peripheral and central parts of cell nuclei. Both when working with nuclei images from monolayers and from histological sections it seems useful to extract separate features from the peripheral and central parts of the nuclei images.
为了研究对早期卵巢癌患者细胞核染色质结构进行量化的预后价值，从单层细胞核图像和组织切片中提取了低维自适应分形和灰度共生矩阵纹理特征向量。每张光学显微镜细胞核图像被分为外周部分和中央部分，分别占细胞核总面积的30%和70%。然后从细胞核图像的外周和中央部分提取纹理特征。自适应特征提取基于类差异矩阵和类距离矩阵。这些矩阵有助于说明卵巢样本中预后良好和预后不良类别之间染色质纹理的差异。类差异矩阵和距离矩阵也清楚地说明了细胞核外周和中央部分之间的纹理差异。无论是处理单层细胞核图像还是组织切片的细胞核图像，从细胞核图像的外周和中央部分提取单独的特征似乎都是有用的。
