ABSTRACT
A modified algorithm using fuzzy Gibbs random field model and fuzzy c-means (FCM) clustering is proposed for segmentation of Magnetic resonance(MR) brain images. Spatial constraints using the definitions of homogeneity of cliques and fuzzy Gibbs clique potential are introduced in this algorithm. A new modified objective function , which is established by introducing the spatial constraints into the traditional intensity based FCM algorithm, leads to the establishment of new iterative formulas for membership matrix and centroids. This algorithm can improve the performance of corresponding traditional one by modifying the original intensity based segmentation model. Experiments on synthetic images and MR phantoms show the validation of the proposed algorithm, which is usually a better alternative for segmenting medical MR images corrupted by noise.
Subject(s)
Humans , Algorithms , Brain , Cluster Analysis , Fuzzy Logic , Image Interpretation, Computer-Assisted , Methods , Magnetic Resonance Imaging , Methods , Pattern Recognition, Automated , MethodsABSTRACT
A modified Snake algorithm for medical image segmentation based on improved Greedy method and electrostatic field model is presented in this paper. Based on Greedy method, this algorithm features a new adjacent point selection strategy and the corresponding criteria, which can be used for searching the potential snake points. A new external image force based on electrostatic field model and the simplified force field computation based on preprocessing image are also introduced. Comparative experiments indicate the validity of the method.
Subject(s)
Humans , Algorithms , Brain , Physiology , Electromagnetic Fields , Image Processing, Computer-Assisted , Methods , Magnetic Resonance Imaging , Methods , Models, Theoretical , Static ElectricityABSTRACT
Image segmentation is still a difficult problem since its effect would vary with the subjects processed. An approach of subtracting background from the entire image of retinal blood vessels presented in this paper. The background subtraction is based on the real image itself taken photographically and is not dependent on the prior knowledge of system for recording image, the approach achieves the grayscale enhancement of retinal blood vessels in preprocessing and provides a quality image for the next process of binarization. This experiment in the preprocessing of subtraction shows good enhancement effect.