298 related articles for article (PubMed ID: 24513228)
1. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm.
Pereira DC; Ramos RP; do Nascimento MZ
Comput Methods Programs Biomed; 2014 Apr; 114(1):88-101. PubMed ID: 24513228
[TBL] [Abstract][Full Text] [Related]
2. Geometry-Based Pectoral Muscle Segmentation From MLO Mammogram Views.
Taghanaki SA; Liu Y; Miles B; Hamarneh G
IEEE Trans Biomed Eng; 2017 Nov; 64(11):2662-2671. PubMed ID: 28129144
[TBL] [Abstract][Full Text] [Related]
3. Correspondence in texture features between two mammographic views.
Gupta S; Markey MK
Med Phys; 2005 Jun; 32(6):1598-606. PubMed ID: 16013719
[TBL] [Abstract][Full Text] [Related]
4. Fusion of k-Gabor features from medio-lateral-oblique and craniocaudal view mammograms for improved breast cancer diagnosis.
Sasikala S; Ezhilarasi M
J Cancer Res Ther; 2018; 14(5):1036-1041. PubMed ID: 30197344
[TBL] [Abstract][Full Text] [Related]
5. Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis.
Huo Z; Giger ML; Vyborny CJ
IEEE Trans Med Imaging; 2001 Dec; 20(12):1285-92. PubMed ID: 11811828
[TBL] [Abstract][Full Text] [Related]
6. Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.
Vikhe PS; Thool VR
J Med Syst; 2016 Apr; 40(4):82. PubMed ID: 26811073
[TBL] [Abstract][Full Text] [Related]
7. Globally supported radial basis function based collocation method for evolution of level set in mass segmentation using mammograms.
Kashyap KL; Bajpai MK; Khanna P
Comput Biol Med; 2017 Aug; 87():22-37. PubMed ID: 28549292
[TBL] [Abstract][Full Text] [Related]
8. Influence of using manual or automatic breast density information in a mass detection CAD system.
Oliver A; Lladó X; Freixenet J; Martí R; Pérez E; Pont J; Zwiggelaar R
Acad Radiol; 2010 Jul; 17(7):877-83. PubMed ID: 20540910
[TBL] [Abstract][Full Text] [Related]
9. Location of mammograms ROI's and reduction of false-positive.
Salazar-Licea LA; Pedraza-Ortega JC; Pastrana-Palma A; Aceves-Fernandez MA
Comput Methods Programs Biomed; 2017 May; 143():97-111. PubMed ID: 28391823
[TBL] [Abstract][Full Text] [Related]
10. Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.
Vikhe PS; Thool VR
J Med Syst; 2017 Oct; 41(12):190. PubMed ID: 29071592
[TBL] [Abstract][Full Text] [Related]
11. Evaluating geodesic active contours in microcalcifications segmentation on mammograms.
Duarte MA; Alvarenga AV; Azevedo CM; Calas MJ; Infantosi AF; Pereira WC
Comput Methods Programs Biomed; 2015 Dec; 122(3):304-15. PubMed ID: 26363676
[TBL] [Abstract][Full Text] [Related]
12. A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms.
Yin K; Yan S; Song C; Zheng B
Int J Comput Assist Radiol Surg; 2019 Feb; 14(2):237-248. PubMed ID: 30288698
[TBL] [Abstract][Full Text] [Related]
13. A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer.
Hazarika M; Mahanta LB
Asian Pac J Cancer Prev; 2018 Aug; 19(8):2141-2148. PubMed ID: 30139217
[TBL] [Abstract][Full Text] [Related]
14. External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.
Benndorf M; Burnside ES; Herda C; Langer M; Kotter E
Med Phys; 2015 Aug; 42(8):4987-96. PubMed ID: 26233224
[TBL] [Abstract][Full Text] [Related]
15. Predicting breast cancer risk using mammographic density measurements from both mammogram sides and views.
Stone J; Ding J; Warren RM; Duffy SW
Breast Cancer Res Treat; 2010 Nov; 124(2):551-4. PubMed ID: 20544272
[TBL] [Abstract][Full Text] [Related]
16. Automated and effective content-based image retrieval for digital mammography.
Singh VP; Srivastava S; Srivastava R
J Xray Sci Technol; 2018; 26(1):29-49. PubMed ID: 29480232
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network.
P S; R T
Asian Pac J Cancer Prev; 2018 Sep; 19(9):2665-2671. PubMed ID: 30256567
[TBL] [Abstract][Full Text] [Related]
19. Hybrid segmentation of mass in mammograms using template matching and dynamic programming.
Song E; Xu S; Xu X; Zeng J; Lan Y; Zhang S; Hung CC
Acad Radiol; 2010 Nov; 17(11):1414-24. PubMed ID: 20817575
[TBL] [Abstract][Full Text] [Related]
20. Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes.
Rodríguez-Ruiz A; Feng SSJ; van Zelst J; Vreemann S; Mann JR; D'Orsi CJ; Sechopoulos I
Med Phys; 2017 Jun; 44(6):2161-2172. PubMed ID: 28244109
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
