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基于超声图像纹理特征和临床特征的计算机辅助前列腺癌检测

Computer-aided prostate cancer detection using texture features and clinical features in ultrasound image.

作者信息

Han Seok Min, Lee Hak Jong, Choi Jin Young

机构信息

Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.

出版信息

J Digit Imaging. 2008 Oct;21 Suppl 1(Suppl 1):S121-33. doi: 10.1007/s10278-008-9106-3. Epub 2008 Mar 6.

DOI:10.1007/s10278-008-9106-3
PMID:18322751
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3043871/
Abstract

In this paper, we propose a new prostate detection method using multiresolution autocorrelation texture features and clinical features such as location and shape of tumor. With the proposed method, we can detect cancerous tissues efficiently with high specificity (about 90-95%)and high sensitivity (about 92-96%) by the measurement of the number of correctly classified pixels. Multiresolution autocorrelation can detect cancerous tissues efficiently, and clinical knowledge helps to discriminate the cancer region by location and shape of the region and increases specificity. The support vector machine is used to classify tissues based on those features. The proposed method will be helpful in formulating a more reliable diagnosis, increasing diagnosis efficiency.

摘要

在本文中，我们提出了一种新的前列腺检测方法，该方法使用多分辨率自相关纹理特征以及诸如肿瘤位置和形状等临床特征。通过所提出的方法，我们能够通过测量正确分类像素的数量，以高特异性（约90 - 95%）和高灵敏度（约92 - 96%）有效地检测癌组织。多分辨率自相关能够有效地检测癌组织，而临床知识有助于根据区域的位置和形状来区分癌区域并提高特异性。支持向量机用于基于这些特征对组织进行分类。所提出的方法将有助于制定更可靠的诊断，提高诊断效率。