[1]孙荣荣,杨舒一,单 飞,等.基于视觉和灰度共生矩阵的医学影像质量评价[J].计算机技术与发展,2021,31(增刊):146-150.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 029]
　SUN Rong-rong,YANG Shu-yi,SHAN Fei,et al.Pulmonary Medical Image Quality Assessment Based on Human VisualCharacteristics and Gray Level Co-occurrence Matrix[J].,2021,31(增刊):146-150.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 029]
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基于视觉和灰度共生矩阵的医学影像质量评价()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年增刊
页码:
146-150
栏目:
应用前沿与综合
出版日期:
2021-12-31

文章信息/Info

Title:
Pulmonary Medical Image Quality Assessment Based on Human VisualCharacteristics and Gray Level Co-occurrence Matrix
文章编号:
1673-629X(2021)S0146-05
作者:
孙荣荣1 ; 杨舒一2 ; 单 飞2 ; 高林峰3 ; 史维雅2 ; 叶 雯2 ; 赵 芳2 ; 姚 杰3
1. 上海市计量测试技术研究院,上海 201203;
2. 上海市公共卫生临床中心,上海 201508;
3. 上海市疾病预防控制中心,上海 200336
Author(s):
SUN Rong-rong1 ; YANG Shu-yi2 ; SHAN Fei2 ; GAO Lin-feng3 ; SHI Wei-ya2 ; YE Wen2 ; ZHAO Fang2 ; YAO Jie3
1. Shanghai Institute of Measurement and Testing Technology,Shanghai 201203;
2. Shanghai Public Health Clinical Center,Shanghai 201508;
3. Shanghai Municipal Center for Disease Control and Prevention,Shanghai 200336
关键词:
医学影像质量评价; 灰度共生矩阵; 肺部影像; 人类视觉特性; 支持向量回归; 纹理
Keywords:
medical image quality assessment; gray level co-occurrence matrix; pulmonary image; human visual characteristics; supportvector regression; texture
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. S. 029
摘要:
研究医学图像质量评价算法具有重要的意义,但医学影像因其多样、复杂等特性对其质量评价有较多困难。 针对此问题,该文提出基于人类视觉特性和灰度共生矩阵的肺部医学影像质量评价方法。 首先求得肺部图像的灰度共生矩阵,然后求得灰度共生矩阵的能量、对比度、自相关、均匀性特征,作为医学影像的特征向量,输入到支持向量回归算法预测影像质量。 收集了肺部 CT 和 MRI 影像若干,由有经验的胸部放射学医生对影像质量进行评分,得到主观评分值,然后用文中方法对肺部医学影像质量进行评分。 分别用斯皮尔曼等级次序相关系数、皮尔逊线性相关系数来衡量客观评价方法测试结果与主观评价之间的一致性。 实验结果表明,该方法取得了优良而稳定的结果,较好地符合主观质量评分和人类视觉特性。
Abstract:
The medical image quality assessment ( IQA) method has important significance. However, it is difficult for medical IQAbecause of its diversity and complexity. There fore, we propose the pulmonary medical image quality assessment method based on human visual characteristics and gray level co-occurrence matrix ( GLCMS) innovatively. GLCM of pulmonary image is obtained firstly,then the energy,contrast,correlation and homogeneity of the GLCMS are extracted as the feature vector of the medical images. Finally these features are taken as the inputs to the support vector regression ( SVR) algorithm to predict the image quality. The Pulmonary CT and MRI images of patients are collected,the images quality are evaluated by experienced doctor,then the subjective scores of image quality are obtained. The objective score of image quality is evaluated by the method,and the Spearman rank order correlation coefficient and Pearson linear correlation coefficient are used to measure the consistency between the objective assessment and subjective assessment. The experiment demonstrates that the proposed method is outstanding, stable and achieves better human visual characteristics and subjective perceived consistency.

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更新日期/Last Update: 2021-09-10