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EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

          This method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel’s feature vector. Feature vectors are composed of the pixel’s intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales.

          The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations.

PROPOSED CONCEPT:

          We propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularize, provided by using L2 Lebesgue measure of the -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature’s boundaries (i.e. H1 Hausdorff measure).

EXISTING  TECHNIQUE :

          2-D GABOR WAVELET AND BAYESIAN CLASSIFIER

PROPOSED ALGORITHM:

          INFINITE PERIMETER ACTIVE CONTOUR WITH HYBRID REGION INFORMATION (IPACHI) MODEL

TECHNIQUE DEFINITION:

          Bayesian classifier with class conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. GMMs represent a halfway between purely nonparametric and parametric models, providing a fast classification.

ALGORITHM DEFINITION:

          IPAC model takes the advantage of using  different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity  information will guarantee a correct feature’s segmentation.

DRAWBACKS:

          It only takes into account information local to each pixel through image filters, ignoring useful information from shapes and structures present in the image.

          This method did not perform well for very large variations in lighting throughout an image, but this occurred for only one image out of the 40 tested from both databases.

          It is possible to use only the skeleton of the segmentations for the extraction of shape.

ADVANTAGES:

          Different types of region information, such as the combination of intensity information and local phase based enhancement map.

          Analysis, such as measurements of diameters and tortuosity of the vessels, classification of veins and arteries, calculation of the arteriovenous ratio.

          Automated or semi-automated segmentation methods would have improvements in efficiency and accuracy.

          Fast, readily available, highest spatial resolution.