https://scholars.lib.ntu.edu.tw/handle/123456789/477763
標題: | Computerized Breast Mass Detection Using Multi-Scale Hessian-Based Analysis for Dynamic Contrast-Enhanced MRI | 作者: | Huang Y.-H. YEUN-CHUNG CHANG CHIUN-SHENG HUANG Chen J.-H. RUEY-FENG CHANG |
關鍵字: | Breast; Detection; Hessian; Magnetic resonance imaging; Morphologic | 公開日期: | 2014 | 出版社: | Springer New York LLC | 卷: | 27 | 期: | 5 | 起(迄)頁: | 649-660 | 來源出版物: | Journal of Digital Imaging | 摘要: | This study aimed to investigate a computer-aided system for detecting breast masses using dynamic contrast-enhanced magnetic resonance imaging for clinical use. Detection performance of the system was analyzed on 61 biopsy-confirmed lesions (21 benign and 40 malignant lesions) in 34 women. The breast region was determined using the demons deformable algorithm. After the suspicious tissues were identified by kinetic feature (area under the curve) and the fuzzy c-means clustering method, all breast masses were detected based on the rotation-invariant and multi-scale blob characteristics. Subsequently, the masses were further distinguished from other detected non-tumor regions (false positives). Free-response operating characteristics (FROC) curve and detection rate were used to evaluate the detection performance. Using the combined features, including blob, enhancement, morphologic, and texture features with 10-fold cross validation, the mass detection rate was 100 % (61/61) with 15.15 false positives per case and 91.80 % (56/61) with 4.56 false positives per case. In conclusion, the proposed computer-aided detection system can help radiologists reduce inter-observer variability and the cost associated with detection of suspicious lesions from a large number of images. Our results illustrated that breast masses can be efficiently detected and that enhancement and morphologic characteristics were useful for reducing non-tumor regions. © 2014, Society for Imaging Informatics in Medicine. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925839964&doi=10.1007%2fs10278-014-9681-4&partnerID=40&md5=57d4bd6261f27c6ac27cb37f12df966f https://scholars.lib.ntu.edu.tw/handle/123456789/477763 |
ISSN: | 08971889 | DOI: | 10.1007/s10278-014-9681-4 | SDG/關鍵字: | Error detection; Image segmentation; Magnetic resonance imaging; Mammography; Matrix algebra; Medical imaging; Textures; Tumors; Breast; Computer aided detection systems; Dynamic contrast enhanced magnetic resonance imaging; Dynamic contrast enhanced MRI; Fuzzy c-means clustering method; Hessian; Morphologic; Morphologic characteristics; Feature extraction; contrast medium; gadodiamide; gadolinium pentetate; adult; aged; automated pattern recognition; breast; Breast Neoplasms; computer assisted diagnosis; diagnostic use; female; human; image enhancement; middle aged; nuclear magnetic resonance imaging; observer variation; pathology; procedures; reproducibility; retrospective study; three dimensional imaging; Adult; Aged; Breast; Breast Neoplasms; Contrast Media; Female; Gadolinium DTPA; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Middle Aged; Observer Variation; Pattern Recognition, Automated; Reproducibility of Results; Retrospective Studies |
顯示於: | 醫學系 |
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