288 related articles for article (PubMed ID: 16394348)
1. Use of border information in the classification of mammographic masses.
Varela C; Timp S; Karssemeijer N
Phys Med Biol; 2006 Jan; 51(2):425-41. PubMed ID: 16394348
[TBL] [Abstract][Full Text] [Related]
2. Improvement of mammographic mass characterization using spiculation meausures and morphological features.
Sahiner B; Chan HP; Petrick N; Helvie MA; Hadjiiski LM
Med Phys; 2001 Jul; 28(7):1455-65. PubMed ID: 11488579
[TBL] [Abstract][Full Text] [Related]
3. Classification of malignant and benign masses based on hybrid ART2LDA approach.
Hadjiiski L; Sahiner B; Chan HP; Petrick N; Helvie M
IEEE Trans Med Imaging; 1999 Dec; 18(12):1178-87. PubMed ID: 10695530
[TBL] [Abstract][Full Text] [Related]
4. Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis.
Sahiner B; Chan HP; Petrick N; Helvie MA; Goodsitt MM
Med Phys; 1998 Apr; 25(4):516-26. PubMed ID: 9571620
[TBL] [Abstract][Full Text] [Related]
5. Temporal change analysis for characterization of mass lesions in mammography.
Timp S; Varela C; Karssemeijer N
IEEE Trans Med Imaging; 2007 Jul; 26(7):945-53. PubMed ID: 17649908
[TBL] [Abstract][Full Text] [Related]
6. Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier.
Delogu P; Evelina Fantacci M; Kasae P; Retico A
Comput Biol Med; 2007 Oct; 37(10):1479-91. PubMed ID: 17383623
[TBL] [Abstract][Full Text] [Related]
7. Multi-scaled morphological features for the characterization of mammographic masses using statistical classification schemes.
Georgiou H; Mavroforakis M; Dimitropoulos N; Cavouras D; Theodoridis S
Artif Intell Med; 2007 Sep; 41(1):39-55. PubMed ID: 17714924
[TBL] [Abstract][Full Text] [Related]
8. Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.
Sahiner B; Petrick N; Chan HP; Hadjiiski LM; Paramagul C; Helvie MA; Gurcan MN
IEEE Trans Med Imaging; 2001 Dec; 20(12):1275-84. PubMed ID: 11811827
[TBL] [Abstract][Full Text] [Related]
9. Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system.
Catarious DM; Baydush AH; Floyd CE
Med Phys; 2004 Jun; 31(6):1512-20. PubMed ID: 15259655
[TBL] [Abstract][Full Text] [Related]
10. Gradient and texture analysis for the classification of mammographic masses.
Mudigonda NR; Rangayyan RM; Desautels JE
IEEE Trans Med Imaging; 2000 Oct; 19(10):1032-43. PubMed ID: 11131493
[TBL] [Abstract][Full Text] [Related]
11. Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.
Kooi T; van Ginneken B; Karssemeijer N; den Heeten A
Med Phys; 2017 Mar; 44(3):1017-1027. PubMed ID: 28094850
[TBL] [Abstract][Full Text] [Related]
12. Characterization of mammographic masses based on level set segmentation with new image features and patient information.
Shi J; Sahiner B; Chan HP; Ge J; Hadjiiski L; Helvie MA; Nees A; Wu YT; Wei J; Zhou C; Zhang Y; Cui J
Med Phys; 2008 Jan; 35(1):280-90. PubMed ID: 18293583
[TBL] [Abstract][Full Text] [Related]
13. Detection of breast masses in mammograms by density slicing and texture flow-field analysis.
Mudigonda NR; Rangayyan RM; Desautels JE
IEEE Trans Med Imaging; 2001 Dec; 20(12):1215-27. PubMed ID: 11811822
[TBL] [Abstract][Full Text] [Related]
14. Fissures segmentation using surface features: content-based retrieval for mammographic mass using ensemble classifier.
Liu H; Lan Y; Xu X; Song E; Hung CC
Acad Radiol; 2011 Dec; 18(12):1475-84. PubMed ID: 22055794
[TBL] [Abstract][Full Text] [Related]
15. Boundary modelling and shape analysis methods for classification of mammographic masses.
Rangayyan RM; Mudigonda NR; Desautels JE
Med Biol Eng Comput; 2000 Sep; 38(5):487-96. PubMed ID: 11094803
[TBL] [Abstract][Full Text] [Related]
16. Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices.
Chan HP; Wu YT; Sahiner B; Wei J; Helvie MA; Zhang Y; Moore RH; Kopans DB; Hadjiiski L; Way T
Med Phys; 2010 Jul; 37(7):3576-86. PubMed ID: 20831065
[TBL] [Abstract][Full Text] [Related]
17. Development of tolerant features for characterization of masses in mammograms.
Rojas-DomÃnguez A; Nandi AK
Comput Biol Med; 2009 Aug; 39(8):678-88. PubMed ID: 19524221
[TBL] [Abstract][Full Text] [Related]
18. Computer aid for decision to biopsy breast masses on mammography: validation on new cases.
Bilska-Wolak AO; Floyd CE; Lo JY; Baker JA
Acad Radiol; 2005 Jun; 12(6):671-80. PubMed ID: 15935965
[TBL] [Abstract][Full Text] [Related]
19. Development and evaluation of a case-based reasoning classifier for prediction of breast biopsy outcome with BI-RADS lexicon.
Bilska-Wolak AO; Floyd CE
Med Phys; 2002 Sep; 29(9):2090-100. PubMed ID: 12349930
[TBL] [Abstract][Full Text] [Related]
20. Effect of dominant features on neural network performance in the classification of mammographic lesions.
Huo Z; Giger ML; Metz CE
Phys Med Biol; 1999 Oct; 44(10):2579-95. PubMed ID: 10533930
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]