184 related articles for article (PubMed ID: 16399033)
1. A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images.
Chen W; Giger ML; Bick U
Acad Radiol; 2006 Jan; 13(1):63-72. PubMed ID: 16399033
[TBL] [Abstract][Full Text] [Related]
2. Automated breast segmentation of fat and water MR images using dynamic programming.
Rosado-Toro JA; Barr T; Galons JP; Marron MT; Stopeck A; Thomson C; Thompson P; Carroll D; Wolf E; Altbach MI; Rodríguez JJ
Acad Radiol; 2015 Feb; 22(2):139-48. PubMed ID: 25572926
[TBL] [Abstract][Full Text] [Related]
3. A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI.
Lin M; Chan S; Chen JH; Chang D; Nie K; Chen ST; Lin CJ; Shih TC; Nalcioglu O; Su MY
Med Phys; 2011 Jan; 38(1):5-14. PubMed ID: 21361169
[TBL] [Abstract][Full Text] [Related]
4. Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images.
Zheng Y; Baloch S; Englander S; Schnall MD; Shen D
Med Image Comput Comput Assist Interv; 2007; 10(Pt 2):393-401. PubMed ID: 18044593
[TBL] [Abstract][Full Text] [Related]
5. Increasing the contrast of the brain MR FLAIR images using fuzzy membership functions and structural similarity indices in order to segment MS lesions.
Bijar A; Khayati R; Peñalver Benavent A
PLoS One; 2013; 8(6):e65469. PubMed ID: 23799015
[TBL] [Abstract][Full Text] [Related]
6. U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging.
Douglas L; Bhattacharjee R; Fuhrman J; Drukker K; Hu Q; Edwards A; Sheth D; Giger M
J Med Imaging (Bellingham); 2023 Nov; 10(6):064502. PubMed ID: 37990686
[TBL] [Abstract][Full Text] [Related]
7. Micro-segmentation of retinal image lesions in diabetic retinopathy using energy-based fuzzy C-Means clustering (EFM-FCM).
Naz H; Nijhawan R; Ahuja NJ; Saba T; Alamri FS; Rehman A
Microsc Res Tech; 2024 Jan; 87(1):78-94. PubMed ID: 37681440
[TBL] [Abstract][Full Text] [Related]
8. A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization.
Ertas G; Doran SJ; Leach MO
Med Biol Eng Comput; 2017 Jan; 55(1):57-68. PubMed ID: 27106750
[TBL] [Abstract][Full Text] [Related]
9. Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm.
Iswisi AFA; Karan O; Rahebi J
Biomed Res Int; 2021; 2021():3248834. PubMed ID: 34988224
[TBL] [Abstract][Full Text] [Related]
10. Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?
Doran SJ; Hipwell JH; Denholm R; Eiben B; Busana M; Hawkes DJ; Leach MO; Silva IDS
Med Phys; 2017 Sep; 44(9):4573-4592. PubMed ID: 28477346
[TBL] [Abstract][Full Text] [Related]
11. Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm.
Mohammadi S; Ghaderi S; Ghaderi K; Mohammadi M; Pourasl MH
Int J Surg Case Rep; 2023 Oct; 111():108818. PubMed ID: 37716060
[TBL] [Abstract][Full Text] [Related]
12. Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.
Rundo L; Beer L; Ursprung S; Martin-Gonzalez P; Markowetz F; Brenton JD; Crispin-Ortuzar M; Sala E; Woitek R
Comput Biol Med; 2020 May; 120():103751. PubMed ID: 32421652
[TBL] [Abstract][Full Text] [Related]
13. Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG).
Al-Faris AQ; Ngah UK; Isa NA; Shuaib IL
J Digit Imaging; 2014 Feb; 27(1):133-44. PubMed ID: 24100762
[TBL] [Abstract][Full Text] [Related]
14. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET.
Hatt M; Cheze le Rest C; Turzo A; Roux C; Visvikis D
IEEE Trans Med Imaging; 2009 Jun; 28(6):881-93. PubMed ID: 19150782
[TBL] [Abstract][Full Text] [Related]
15. Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs.
Pandey D; Yin X; Wang H; Su MY; Chen JH; Wu J; Zhang Y
Heliyon; 2018 Dec; 4(12):e01042. PubMed ID: 30582055
[TBL] [Abstract][Full Text] [Related]
16. Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.
Douglas L; Fuhrman J; Hu Q; Edwards A; Sheth D; Abe H; Giger M
J Med Imaging (Bellingham); 2024 May; 11(3):034501. PubMed ID: 38737493
[TBL] [Abstract][Full Text] [Related]
17. STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis.
Zheng Y; Englander S; Baloch S; Zacharaki EI; Fan Y; Schnall MD; Shen D
Med Phys; 2009 Jul; 36(7):3192-204. PubMed ID: 19673218
[TBL] [Abstract][Full Text] [Related]
18. An Intuitionistic Fuzzy Clustering Approach for Detection of Abnormal Regions in Mammogram Images.
Chaira T
J Digit Imaging; 2021 Apr; 34(2):428-439. PubMed ID: 33755865
[TBL] [Abstract][Full Text] [Related]
19. Unsupervised Segmentation of 5D Hyperpolarized Carbon-13 MRI Data Using a Fuzzy Markov Random Field Model.
Daniels CJ; Gallagher FA
IEEE Trans Med Imaging; 2018 Apr; 37(4):840-850. PubMed ID: 28880161
[TBL] [Abstract][Full Text] [Related]
20. Tumor Region Location and Classification Based on Fuzzy Logic and Region Merging Image Segmentation Algorithm.
Zhao T; Dai H
J Healthc Eng; 2021; 2021():1141619. PubMed ID: 34721822
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]