Paper Info
Title
Segmentation of Mammography by Applying GrowCut for Mass Detection.
Abstract
Accurately segmenting tumors in digital mammography images is a hard task. However, quality of segmentation is important to avoid misdiagnosis. In this work, the GrowCut technique, which is based on cellular automaton, was used to segment tumor regions of digitized mammograms available in the Mini-Mias database. A set of images was submitted to GrowCut technique and segmented images were compared with ground truth in terms of metrics of area, perimeter, Feret's distance, form factor, and solidity. For segmenting tumors, low user interaction is required. Results showed that GrowCut segmentation images obtained similar properties and shape of the ground-truth images, with an average estimated error close to zero, for all metrics analyzed.
Year
DOI
Venue
2013
10.3233/978-1-61499-289-9-87
Studies in Health Technology and Informatics
Keywords
Field
DocType
Segmentation,Mammogram,GrowCut,Cancer
Digital mammography,Mammography,Data mining,Pattern recognition,GrowCut algorithm,FERET,Segmentation,Perimeter,Ground truth,Artificial intelligence,Medicine,Radiographic Image Enhancement
Conference
Volume
ISSN
Citations 
192
0926-9630
4
PageRank 
References 
Authors
0.44
0
3
Name
Order
Citations
PageRank
F. R. Cordeiro1467.14
Wellington P. dos Santos23611.00
Abel Guilhermino Silva-Filho36212.94