61 related articles for article (PubMed ID: 11721807)
1. Digital mammography: wavelet transform and Kalman-filtering neural network in mass segmentation and detection.
Qian W; Sun X; Song D; Clark RA
Acad Radiol; 2001 Nov; 8(11):1074-82. PubMed ID: 11721807
[TBL] [Abstract][Full Text] [Related]
2. Digital mammography: comparison of adaptive and nonadaptive CAD methods for mass detection.
Qian W; Li L; Clarke L; Clark RA; Thomas J
Acad Radiol; 1999 Aug; 6(8):471-80. PubMed ID: 10480043
[TBL] [Abstract][Full Text] [Related]
3. Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system.
Meinel LA; Stolpen AH; Berbaum KS; Fajardo LL; Reinhardt JM
J Magn Reson Imaging; 2007 Jan; 25(1):89-95. PubMed ID: 17154399
[TBL] [Abstract][Full Text] [Related]
4. Comparison of diagnostic accuracy of breast masses using digitized images versus screen-film mammography.
Liang Z; Du X; Liu J; Yao X; Yang Y; Li K
Acta Radiol; 2008 Jul; 49(6):618-22. PubMed ID: 18568552
[TBL] [Abstract][Full Text] [Related]
5. Multimodality computerized diagnosis of breast lesions using mammography and sonography.
Drukker K; Horsch K; Giger ML
Acad Radiol; 2005 Aug; 12(8):970-9. PubMed ID: 16087091
[TBL] [Abstract][Full Text] [Related]
6. Classification of EEG signals using neural network and logistic regression.
Subasi A; Erçelebi E
Comput Methods Programs Biomed; 2005 May; 78(2):87-99. PubMed ID: 15848265
[TBL] [Abstract][Full Text] [Related]
7. Detecting movement-related EEG change by wavelet decomposition-based neural networks trained with single thumb movement.
Chen CW; Lin CC; Ju MS
Clin Neurophysiol; 2007 Apr; 118(4):802-14. PubMed ID: 17317306
[TBL] [Abstract][Full Text] [Related]
8. Digital mammography: hybrid four-channel wavelet transform for microcalcification segmentation.
Qian W; Clarke LP; Song D; Clark RA
Acad Radiol; 1998 May; 5(5):354-64. PubMed ID: 9597103
[TBL] [Abstract][Full Text] [Related]
9. Ipsilateral-mammogram computer-aided detection of breast cancer.
Sun X; Qian W; Song D
Comput Med Imaging Graph; 2004 Apr; 28(3):151-8. PubMed ID: 15081498
[TBL] [Abstract][Full Text] [Related]
10. Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography.
Mazurowski MA; Habas PA; Zurada JM; Tourassi GD
Phys Med Biol; 2008 Feb; 53(4):895-908. PubMed ID: 18263947
[TBL] [Abstract][Full Text] [Related]
11. Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms.
Zhang W; Yoshida H; Nishikawa RM; Doi K
Med Phys; 1998 Jun; 25(6):949-56. PubMed ID: 9650185
[TBL] [Abstract][Full Text] [Related]
12. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms.
Yoshida H; Doi K; Nishikawa RM; Giger ML; Schmidt RA
Acad Radiol; 1996 Aug; 3(8):621-7. PubMed ID: 8796725
[TBL] [Abstract][Full Text] [Related]
13. Wavelet transform filtering and nonlinear anisotropic diffusion assessed for signal reconstruction performance on multidimensional biomedical data.
Frangakis AS; Stoschek A; Hegerl R
IEEE Trans Biomed Eng; 2001 Feb; 48(2):213-22. PubMed ID: 11296877
[TBL] [Abstract][Full Text] [Related]
14. Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications.
Zheng B; Qian W; Clarke LP
IEEE Trans Med Imaging; 1996; 15(5):589-97. PubMed ID: 18215940
[TBL] [Abstract][Full Text] [Related]
15. Performance gain in computer-assisted detection schemes by averaging scores generated from artificial neural networks with adaptive filtering.
Zheng B; Chang YH; Good WF; Gur D
Med Phys; 2001 Nov; 28(11):2302-8. PubMed ID: 11764037
[TBL] [Abstract][Full Text] [Related]
16. A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.
Cruz-Aceves I; Cervantes-Sanchez F; Avila-Garcia MS
J Healthc Eng; 2018; 2018():5812059. PubMed ID: 29849999
[TBL] [Abstract][Full Text] [Related]
17. Compression of digital chest radiographs with a mixture of principal components neural network: evaluation of performance.
Dony RD; Coblentz CL; Nabmias C; Haykin S
Radiographics; 1996 Nov; 16(6):1481-8. PubMed ID: 8946548
[TBL] [Abstract][Full Text] [Related]
18. Three-Class Mammogram Classification Based on Descriptive CNN Features.
Jadoon MM; Zhang Q; Haq IU; Butt S; Jadoon A
Biomed Res Int; 2017; 2017():3640901. PubMed ID: 28191461
[TBL] [Abstract][Full Text] [Related]
19. Design and implementation of full-frame, bit-allocation image-compression hardware module. Work in progress.
Ho BK; Chao J; Zhu P; Huang HK
Radiology; 1991 May; 179(2):563-7. PubMed ID: 2014312
[TBL] [Abstract][Full Text] [Related]
20. Comparing the performance of image enhancement methods to detect microcalcification clusters in digital mammography.
Moradmand H; Setayeshi S; Karimian AR; Sirous M; Akbari ME
Iran J Cancer Prev; 2012; 5(2):61-8. PubMed ID: 25628822
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]