Potential Usefulness of Multiple-Mammographic Views in Computer-Aided Diagnosis Scheme for Identifying Histological Classification of Clustered Microcalcification

机译：多乳腺X线照片在计算机辅助诊断方案中识别簇状微钙化的组织学分类的潜在用途

获取原文

获取原文并翻译 | 示例

页面导航

摘要
著录项
相似文献
相关主题

摘要

The purpose of this study was to investigate the usefulness of multiple-view mammograms in the computerized scheme for identifying histological classifications. Our database consisted of mediolateral oblique (MLO) and craniocaudal (CC) magnification mammograms obtained from 77 patients, which included 14 invasive carcinomas, 17 noninvasive carcinomas of comedo type, 17 noninvasive carcinomas of noncomedo type, 14 mastopathies, and 15 fibroadenomas. Five features on clustered microcalcifications were determined from each of MLO and CC images by taking into account image features that experienced radiologists commonly use to identify histological classifications. Modified Bayes discriminant function (MBDF) was employed for distinguishing between histological classifications. For the input of MBDF, we used five or ten features obtained from MLO and/or CC images. With ten features, the classification accuracies for each histological classification ranged from 70.6% to 93.3%. This result was higher than that obtained with only five features either from MLO or CC images.

机译：这项研究的目的是调查多视图乳房X线照片在识别组织学分类的计算机化方案中的作用。我们的数据库包括从77例患者中获得的中外侧斜（MLO）和颅尾（CC）放大乳房X线照片，包括14例浸润性癌，17例粉刺非侵入性癌，17例非粉刺非侵入性癌，14例乳突病和15例纤维腺瘤。通过考虑经验丰富的放射科医生通常用于识别组织学分类的图像特征，从MLO和CC图像中的每一个中确定了簇状微钙化的五个特征。改进的贝叶斯判别函数（MBDF）用于区分组织学分类。对于MBDF的输入，我们使用了从MLO和/或CC图像获得的五个或十个特征。具有十个特征，每种组织学分类的分类准确性范围为70.6％至93.3％。该结果高于仅从MLO或CC图像获得五个特征的结果。

著录项

来源
《International Workshop on Digital Mammography(IWDM 2006); 20060618-21; Manchester(GB)》|2006年|P.229-236|共8页
会议地点 Manchester(GB)
作者
Ryohei Nakayama; Ryoji Watanabe; Kiyoshi Namba; Koji Yamamoto; Kan Takeda; Shigehiko Katsuragawa; Kunio Doi;
展开▼
作者单位

Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu, 514-8507, Japan;

展开▼
会议组织
原文格式 PDF
正文语种 eng
中图分类生物医学工程;
关键词

相似文献

外文文献
中文文献
专利

1. Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms. [J] . Nakayama R, Watanabe R, Namba K, Academic radiology . 2006,第10期

机译：计算机辅助诊断方案，用于通过随访放大的乳腺X线照片确定簇状微钙化的组织学分类。
2. An improved computer-aided diagnosis scheme using the nearest neighbor criterion for determining histological classification of clustered microcalcifications. [J] . Nakayama R, Watanabe R, Namba K, Methods of information in medicine . 2007,第6期

机译：一种改进的计算机辅助诊断方案，使用最近邻准则确定簇状微钙化的组织学分类。
3. Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms. [J] . Nakayama R, Uchiyama Y, Watanabe R, Medical Physics . 2004,第4期

机译：计算机辅助诊断方案，用于在放大的乳房X光照片上对簇状微钙化组织进行组织学分类。
4. Potential Usefulness of Multiple-Mammographic Views in Computer-Aided Diagnosis Scheme for Identifying Histological Classification of Clustered Microcalcification [C] . Ryohei Nakayama, Ryoji Watanabe, Kiyoshi Namba, International Workshop on Digital Mammography(IWDM 2006) . 2006

机译：用于识别聚类微钙化的组织学分类的计算机辅助诊断方案中多乳音视图的潜在用途
5. A computer-aided diagnostic scheme for classification of clustered microcalcifications in mammograms. [D] . Jiang, Yulei. 1997

机译：一种计算机辅助诊断方案，用于对乳房X线照片中的簇状微钙化进行分类。
6. Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network [O] . Akiyoshi Hizukuri, Ryohei Nakayama 2018

机译：利用卷积神经网络确定超声图像上乳腺组织学分类的计算机辅助诊断方案
7. Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network [O] . Akiyoshi Hizukuri, Ryohei Nakayama 2018

机译：用卷积神经网络确定超声图像乳房病变组织学分类的计算机辅助诊断方案

Potential Usefulness of Multiple-Mammographic Views in Computer-Aided Diagnosis Scheme for Identifying Histological Classification of Clustered Microcalcification

摘要

著录项

相似文献

相关主题

期刊订阅