Grading method of inhomogeneity of contrast-enhanced ultrasound for rectal tumors based on gray level co-occurrence matrix_Journal of Biomedical Engineering

Authors：

LUO Yuan ¹ ,  ZHUANG Hua ¹ , QIN Langkuan ² , ZHAO Jieying ¹ , YIN Hao ² , LIU Dongquan ² , WU Yuting ¹ , LIU Ke ¹ , HU Hanchuan ¹

1. Department of Ultrasound Diagnosis, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;
2. College of Computer Software, Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

ZHUANG Hua, Email: annzhuang@yeah.net

Keywords：

rectal tumor; contrast-enhanced ultrasonography; heterogeneity; texture analysis; computer-aided diagnosis

DOI：

10.7507/1001-5515.201903013

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Transrectal contrast-enhanced ultrasound (CEUS) is an important examination for rectal tumors. The inhomogeneity of the CEUS images has important clinical significance. However, there is no objective method to evaluate this index. In this study, a method based on gray-level co-occurrence matrix (GLCM) is proposed to extract texture features of images and grade these images according the inhomogeneity. Specific processes include compressing the gray level of the image, calculating the texture statistics of gray level co-occurrence matrix, combining feature selection and principal component analysis (PCA) for dimensionality reduction, and training and validating quadratic discriminant analysis (QDA). After ten cross-validation, the overall accuracy rate of machine classification was 87.01%, and the accuracy of each level was as follows: Grade Ⅰ 52.94%, Grade Ⅱ 96.48% and Grade Ⅲ 92.35% respectively. The proposed method has high accuracy in judging grade Ⅱ and Ⅲ images, which can help to identify the grade of inhomogeneity of contrast-enhanced ultrasound images of rectal tumors, and may be used to assist clinical doctors in judging the grade of inhomogeneity of contrast-enhanced ultrasound of rectal tumors.

Citation： LUO Yuan, ZHUANG Hua, QIN Langkuan, ZHAO Jieying, YIN Hao, LIU Dongquan, WU Yuting, LIU Ke, HU Hanchuan. Grading method of inhomogeneity of contrast-enhanced ultrasound for rectal tumors based on gray level co-occurrence matrix. Journal of Biomedical Engineering, 2019, 36(6): 964-968. doi: 10.7507/1001-5515.201903013 Copy

1.	王锡山. 中美结直肠癌流行病学特征对比及防控策略分析. 中华结直肠疾病电子杂志, 2019, 8(1): 7-11.
2.	Onji K, Yoshida S, Tanaka S, et al. Microvascular structure and perfusion imaging of colon cancer by means of contrast-enhanced ultrasonography. Abdom Imaging, 2012, 37(2): 297-303.
3.	邓祥春, 郑波, 童朝阳, 等. 多层螺旋 CT 对粘液性与非粘液性结直肠癌的鉴别诊断价值. 中国 CT 和 MRI 杂志, 2015(8): 80-83.
4.	李奥, 林红军, 巩海燕, 等. BI-RADS4 类乳腺肿块超声造影表现不均匀性增强的病理对照分析. 临床超声医学杂志, 2015, 17(8): 509-512.
5.	Wang Y, Li L, Wang J Y, et al. Time-intensity curve parameters in rectal cancer measured using endorectal ultrasonography with sterile coupling gels filling the rectum: Correlations with tumor angiogenesis and clinicopathological features. Biomed Res Int, 2014, 2014: 587806.
6.	Lassau N, Chami L, Benatsou B, et al. Dynamic contrast-enhanced ultrasonography (DCE-US) with quantification of tumor perfusion: a new diagnostic tool to evaluate the early effects of antiangiogenic treatment. European Radiology Supplements, 2008, 17(S6): 89-98.
7.	步亚东. 图像纹理特征提取的研究. 济南: 山东师范大学, 2012.
8.	吴君, 贺超, 赵兴友, 等. 基于超声图像的纹理特征分析算法的应用. 生物医学工程研究, 2007, 26(2): 166-169.
9.	Acharya U R, Swapna G, Sree S V, et al. A review on ultrasound-based thyroid cancer tissue characterization and automated classification. Technol Cancer Res Treat, 2014, 13(4): 289-301.
10.	Acharya U R, Faust O, Molinari F, et al. Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm. Knowledge-Based Systems, 2015, 75: 66-77.
11.	Singh B K, Verma K, Thoke A S. Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Syst Appl, 2016, 66: 114-123.
12.	Abdel-Nasser M, Melendez J, Moreno A, et al. Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng Appl Artif Intell, 2017, 59: 84-92.
13.	Xian Guangming. An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst Appl, 2010, 37(10): 6737-6741.
14.	Cârţână E T, Gheonea D I, Săftoiu A. Advances in endoscopic ultrasound imaging of colorectal diseases. World J Gastroenterol, 2016, 22(5): 1756-1766.
15.	Bipat S, Glas A S, Slors F J, et al. Rectal cancer: local staging and assessment of lymph node involvement with endoluminal US, CT, and MR imaging-a meta-analysis. Radiology, 2004, 232(3): 773-783.
16.	李莉, 金磊. 基于灰度共生矩阵的乳腺肿瘤超声图像纹理特征分析. 电脑知识与技术, 2012, 8(31): 7552-7554.
17.	张慧, 迟庆云, 刘彩霞. 基于灰度共生矩阵的肝癌 B 超纹理特征决策树诊断分析. 中国医药指南, 2015, 3(25): 2-3.
18.	Rahmawaty M, Nugroho H A, Triyani Y, et al. Classification of breast ultrasound images based on texture analysis// 2016 1st International Conference on Biomedical Engineering (IBIOMED). Yogyakarta: IEEE, 2016: 84-89.
19.	Wan Tao, Bloch B N, Plecha D, et al. A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: Preliminary results in predicting OncotypeDX risk scores. Sci Rep, 2016, 6: 21394.

1. 王锡山. 中美结直肠癌流行病学特征对比及防控策略分析. 中华结直肠疾病电子杂志, 2019, 8(1): 7-11.
2. Onji K, Yoshida S, Tanaka S, et al. Microvascular structure and perfusion imaging of colon cancer by means of contrast-enhanced ultrasonography. Abdom Imaging, 2012, 37(2): 297-303.
3. 邓祥春, 郑波, 童朝阳, 等. 多层螺旋 CT 对粘液性与非粘液性结直肠癌的鉴别诊断价值. 中国 CT 和 MRI 杂志, 2015(8): 80-83.
4. 李奥, 林红军, 巩海燕, 等. BI-RADS4 类乳腺肿块超声造影表现不均匀性增强的病理对照分析. 临床超声医学杂志, 2015, 17(8): 509-512.
5. Wang Y, Li L, Wang J Y, et al. Time-intensity curve parameters in rectal cancer measured using endorectal ultrasonography with sterile coupling gels filling the rectum: Correlations with tumor angiogenesis and clinicopathological features. Biomed Res Int, 2014, 2014: 587806.
6. Lassau N, Chami L, Benatsou B, et al. Dynamic contrast-enhanced ultrasonography (DCE-US) with quantification of tumor perfusion: a new diagnostic tool to evaluate the early effects of antiangiogenic treatment. European Radiology Supplements, 2008, 17(S6): 89-98.
7. 步亚东. 图像纹理特征提取的研究. 济南: 山东师范大学, 2012.
8. 吴君, 贺超, 赵兴友, 等. 基于超声图像的纹理特征分析算法的应用. 生物医学工程研究, 2007, 26(2): 166-169.
9. Acharya U R, Swapna G, Sree S V, et al. A review on ultrasound-based thyroid cancer tissue characterization and automated classification. Technol Cancer Res Treat, 2014, 13(4): 289-301.
10. Acharya U R, Faust O, Molinari F, et al. Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm. Knowledge-Based Systems, 2015, 75: 66-77.
11. Singh B K, Verma K, Thoke A S. Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Syst Appl, 2016, 66: 114-123.
12. Abdel-Nasser M, Melendez J, Moreno A, et al. Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng Appl Artif Intell, 2017, 59: 84-92.
13. Xian Guangming. An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst Appl, 2010, 37(10): 6737-6741.
14. Cârţână E T, Gheonea D I, Săftoiu A. Advances in endoscopic ultrasound imaging of colorectal diseases. World J Gastroenterol, 2016, 22(5): 1756-1766.
15. Bipat S, Glas A S, Slors F J, et al. Rectal cancer: local staging and assessment of lymph node involvement with endoluminal US, CT, and MR imaging-a meta-analysis. Radiology, 2004, 232(3): 773-783.
16. 李莉, 金磊. 基于灰度共生矩阵的乳腺肿瘤超声图像纹理特征分析. 电脑知识与技术, 2012, 8(31): 7552-7554.
17. 张慧, 迟庆云, 刘彩霞. 基于灰度共生矩阵的肝癌 B 超纹理特征决策树诊断分析. 中国医药指南, 2015, 3(25): 2-3.
18. Rahmawaty M, Nugroho H A, Triyani Y, et al. Classification of breast ultrasound images based on texture analysis// 2016 1st International Conference on Biomedical Engineering (IBIOMED). Yogyakarta: IEEE, 2016: 84-89.
19. Wan Tao, Bloch B N, Plecha D, et al. A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: Preliminary results in predicting OncotypeDX risk scores. Sci Rep, 2016, 6: 21394.

Journal of Biomedical Engineering

Grading method of inhomogeneity of contrast-enhanced ultrasound for rectal tumors based on gray level co-occurrence matrix

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content