Title :
Unsupervised Algorithm for Radiographic Image Segmentation Based on the Gaussian Mixture Model
Author :
Mekhalfa, Faiza ; Nacereddine, Nafaâ ; Goumeïdane, Aïcha Baya
Author_Institution :
Welding & NDT Res. Center/Image & Signal Process. Lab., Cheraga
Abstract :
In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.
Keywords :
expectation-maximisation algorithm; fuzzy set theory; image segmentation; neural nets; probability; radiography; unsupervised learning; Gaussian mixture model; expectation maximization; fuzzy c-means algorithm; image segmentation; probabilistic neural networks; radiographic images; segmentation accuracy; unsupervised algorithm; Clustering algorithms; Code standards; Geometry; Image segmentation; Neural networks; Partitioning algorithms; Radiography; Signal processing algorithms; Testing; Welding; Gaussian mixture model; Weld defect; expectation maximization algorithm; fuzzy C-means algorithm; image segmentation; radiographic images;
Conference_Titel :
EUROCON, 2007. The International Conference on "Computer as a Tool"
Conference_Location :
Warsaw
Print_ISBN :
978-1-4244-0813-9
Electronic_ISBN :
978-1-4244-0813-9
DOI :
10.1109/EURCON.2007.4400401
