A fully automatic algorithm for segmentation of the breasts in DCE-MR images

Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. C...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Ročník 2010; s. 3146 - 3149
Hlavní autoři: Giannini, V, Vignati, A, Morra, L, Persano, D, Brizzi, D, Carbonaro, L, Bert, A, Sardanelli, F, Regge, D
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2010
Témata:
Algorithms
Artificial Intelligence
Breast
Breast - anatomy & histology
Coils
Contrast Media
Female
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image segmentation
Lesions
Magnetic Resonance Imaging - methods
Manuals
Muscles
Pattern Recognition, Automated - methods
Pixel
Reproducibility of Results
Sensitivity and Specificity
ISBN:1424441234, 9781424441235
ISSN:1094-687X, 1557-170X, 2375-7477
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79±0.09, recall=0.95± 0.02, precision=0.82 ± 0.1).
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISBN:1424441234
9781424441235
ISSN:1094-687X
1557-170X
2375-7477
DOI:10.1109/IEMBS.2010.5627191