基于CT图像的肾脏肿瘤纹理特征提取  

Extraction of Renal Tumor Texture Features Based on CT Image

作　　者：高岩[1] GAO Yan

机构地区：[1]厦门大学信息科学与技术学院

出　　处：《中国数字医学》2019年第4期66-68,共3页China Digital Medicine

摘　　要：目前计算机辅助检测肿瘤都是基于病变形态学变化的分析,且这些算法的效果难以满足现状。从这些算法所忽略的图像纹理特征出发,从不同病人的198张、5类不同的良恶性肿瘤的CT图像中,基于灰度共生矩阵和灰度梯度共生矩阵,综合考虑肾脏肿瘤没有明显方向性及细纹理的特性,并依据可区分性、唯一性、不相关性以及为避免后续肿瘤识别过程复杂化,通过分析作出了有效性选择,首次提取出最能体现5种肿瘤的27个特征并验证其有效性,作为后续计算机辅助识别肾脏肿瘤研究的基础。At present, computer-aided detection of tumors is based on the analysis of pathological changes, and the effect of these algorithms is unsatisfactory. Starting from the image texture features neglected by these algorithms, based on the gray level co-occurrence matrix and the gray level gradient co-occurrence matrix, from 198 CT images of different patients of 5 different types of benign and malignant tumors, the characteristics of renal tumors with no obvious orientation and fine texture are considered comprehensively, and according to distinguishability, uniqueness and irrelevance, the complexity of the subsequent tumor recognition process is avoided. Through the analysis, the effectiveness selection is made. First, 27 features of five types of tumors are extracted, and their effectiveness is verified as basis for the research on the follow-up computer-aided recognition of kidney tumors.

关 键 词：CT图像 肾脏肿瘤 纹理特征提取 灰度共生矩阵 灰度梯度共生矩阵 

分 类 号：R318[医药卫生—生物医学工程] TP391[医药卫生—基础医学]