Computer-aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures
Computer-aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures
复制标题
使用信息论相似性测量在断层合成重建体积中计算机辅助检测乳房肿块
DOI：
10.1117/12.772978
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
J. Lo
中科院分区：
文献类型：
--
作者：
Swatee Singh;G. Tourassi;A. Chawla;R. Saunders;E. Samei;J. Lo
The purpose of this project is to study two Computer Aided Detection (CADe) systems for breast masses for digital tomosynthesis using reconstructed slices. This study used eighty human subject cases collected as part of on-going clinical trials at Duke University. Raw projections images were used to identify suspicious regions in the algorithm's high sensitivity, low specificity stage using a Difference of Gaussian filter. The filtered images were thresholded to yield initial CADe hits that were then shifted and added to yield a 3D distribution of suspicious regions. The initial system performance was 95% sensitivity at 10 false positives per breast volume. Two CADe systems were developed. In system A, the central slice located at the centroid depth was used to extract a 256 X 256 Regions of Interest (ROI) database centered at the lesion coordinates. For system B, 5 slices centered at the lesion coordinates were summed before the extraction of 256 × 256 ROIs. To avoid issues associated with feature extraction, selection, and merging, information theory principles were used to reduce false positives for both the systems resulting in a classifier performance of 0.81 and 0.865 Area Under Curve (AUC) with leave-one-case-out sampling. This resulted in an overall system performance of 87% sensitivity with 6.1 FPs/ volume and 85% sensitivity with 3.8 FPs/ volume for systems A and B respectively. This system therefore has the potential to detect breast masses in tomosynthesis data sets.
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影响因子：
3.8
作者：
Yi-Ta Wu;Jun Wei;Lubomir M. Hadjiiski;B. Sahiner;Chuan Zhou;Jun Ge;Jiazheng Shi;Yiheng Zhang;H. Chan
通讯作者：
Yi-Ta Wu;Jun Wei;Lubomir M. Hadjiiski;B. Sahiner;Chuan Zhou;Jun Ge;Jiazheng Shi;Yiheng Zhang;H. Chan
影响因子：
3.8
作者：
Chan, HP;Sahiner, B;Adler, DD
通讯作者：
Adler, DD
影响因子：
3.8
作者：
Ge, Jun;Sahiner, Berkman;Zhou, Chuan
通讯作者：
Zhou, Chuan
使用遗传算法在数字化乳房 X 光照片中进行计算机化质量检测的特征选择。
影响因子：
4.8
作者：
Zheng,B;Chang,YH;Wang,XH;Good,WF;Gur,D
通讯作者：
Gur,D
影响因子：
3.8
作者：
Paquerault, S;Petrick, N;Helvie, MA
通讯作者：
Helvie, MA