计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (20): 151-154.

• 图形、图像、模式识别 • 上一篇    下一篇

利用灰度纹理分析方法识别蛋白质空间结构

施建宇1,雷朝霞2,方智裕3   

  1. 1.西北工业大学 生命科学院,西安 710072
    2.鄱阳县第一中学 计算机教研组,江西 上饶 333100
    3.鄱阳县广播电视台 多媒体管理中心,江西 上饶 333100
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2011-07-11 发布日期:2011-07-11

Spatial structure recognition of protein based on gray texture analysis

SHI Jianyu1,LEI Zhaoxia2,FANG Zhiyu3   

  1. 1.School of Life Science,Northwestern Polytechnical University,Xi’an 710072,China
    2.Group of Computer Science,No.1 High School of Poyang,Shangrao,Jiangxi 333100,China
    3.Administration of Multimedia,Broadcasting Station of Poyang,Shangrao,Jiangxi 333100,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-07-11 Published:2011-07-11

摘要: 蛋白质三维空间结构固有的复杂性给其结构分类带来了较大的困难。将蛋白质空间结构映射成为二维距离矩阵,并进一步视作灰度纹理图像,使用拉冬变换分析了该图像的纹理方向特性,基于灰度共生矩阵和Radon投影矩阵提出了一种低维的蛋白质结构特征提取方法。实验结果与对比表明,该方法不仅具有低维的特征,而且有效地实现了多类蛋白质结构分类识别。

关键词: 距离矩阵, 灰度共生矩阵, 拉冬变换, 支持向量机, 特征提取

Abstract: The intrinsic complexity of Protein Spatial Structure(PSS) brings a heavy difficulty to its structural classification.In this paper,PSS is mapped into 2-D distance matrix and further regarded as texture image of which textural directions are detected by Radon transform.Consequently,a novel method of feature extraction of PSS with low dimension is proposed based on gray level co-occurrence matrix and Radon transform project matrix.The experiments and the comparison results demonstrate that the presented method can not only product low-dimensional feature and also achieve effective classification of PSS.

Key words: distance matrix, gray level co-occurrence matrix, Radon transform, support vector machines, feature extraction