( en | de | ru )

 

Disambiguation of "Loew, Murray H."

Author of the publication

copydeleteadd this publication to your clipboard
Objective analysis of ultrasound images by use of a computational observer.

, , and . IEEE Trans. Med. Imaging, 11 (4): 496-506 (1992)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Helmut Loew

Lipót Loew

Wilhelm Loew

Kathleen Murray

Bernd Murray

 

Other publications of authors with the same name

Use of infrared hyperspectral imaging (960-1680 nm) and low energy x-radiography to visualize watermarks., and . CISS, page 1-4. IEEE, (2018)Feasibility of an automated technique for detection of large misregistrations., and . Medical Imaging: Image Processing, volume 4684 of SPIE Proceedings, SPIE, (2002)Analysis of parenchymal patterns using conspicuous spatial frequency features in mammograms applied to the BI-RADS density rating scheme., and . Medical Imaging: Image Processing, volume 6144 of SPIE Proceedings, page 61440F. SPIE, (2006)Evaluation of the accuracy of an edge-based approach for multimodality brain image registration., and . Medical Imaging: Image Processing, volume 3979 of SPIE Proceedings, SPIE, (2000)Hierarchical approach for automated segmentation of the brain volume from MR images., , and . Medical Imaging: Image Processing, volume 3661 of SPIE Proceedings, SPIE, (1999)Concurrency in Feature Analysis., and . PARA, volume 1041 of Lecture Notes in Computer Science, page 313-322. Springer, (1995)Pattern recognition with new class discovery., and . CVPR, page 438-443. IEEE, (1991)Application of statistical pattern recognition and image processing in ultrasound tissue characterization., , , , and . Medical Imaging: Image Processing, volume 1233 of SPIE Proceedings, SPIE, (1990)Loss Functions Forcing Cluster Separations for Multi-class Classification Using Deep Neural Networks., , and . ACSSC, page 2106-2110. IEEE, (2019)A novel measure to evaluate generative adversarial networks based on direct analysis of generated images., and . Neural Comput. Appl., 33 (20): 13921-13936 (2021)