College of Engineering, Huaqiao University, Quanzhou 362021, China.
Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou 363000, China.
Comput Math Methods Med. 2020 Oct 6;2020:7359375. doi: 10.1155/2020/7359375. eCollection 2020.
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.
前列腺癌是男性最常见的癌症之一。前列腺癌的早期检测是成功治疗的关键。超声成像是早期发现前列腺癌最适宜的方法之一。虽然超声图像可以显示癌症病变，但主观解释并不准确。因此，本文提出了一种经直肠超声图像分析方法，旨在通过图像处理对前列腺组织进行特征描述，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，采用局部二值化和高斯马尔可夫随机场提取纹理特征，并进行线性组合。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 张经直肠超声图像数据集，准确率为 70.93%，灵敏度为 70.00%，特异性为 71.74%。实验结果表明，该方法在一定程度上可以区分癌组织和非癌组织。
