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Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection
Computational and Mathematical Methods in Medicine ( IF 2.809 ) Pub Date : 2020-10-06 , DOI: 10.1155/2020/7359375
Xiaofu Huang 1 , Ming Chen 2 , Peizhong Liu 1, 3 , Yongzhao Du 1, 3
Affiliation  

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.

中文翻译:

基于纹理特征的经直肠超声图像分类对前列腺癌的诊断

前列腺癌是男性中最常见的癌症之一。早期发现前列腺癌是成功治疗的关键。超声成像是早期检测前列腺癌的最合适方法之一。尽管超声图像可以显示癌症病变,但主观解释并不准确。因此,本文提出一种经直肠超声图像分析方法,旨在通过图像处理来表征前列腺组织,以评估恶性肿瘤的可能性。首先,通过光密度转换对输入图像进行预处理。然后,使用局部二值化和高斯马尔可夫随机场提取纹理特征,并执行线性组合。最后,将融合的纹理特征提供给SVM分类器进行分类。该方法已应用于从医院获得的342例经直肠超声图像的数据集,准确性为70.93%,灵敏度为70.00%,特异性为71.74%。实验结果表明,可以在一定程度上区分癌组织和非癌组织。
更新日期:2020-10-11
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