11-05-2013, 04:08 PM
Edge Detection by Fuzzy Intensification
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ABSTRACT:
A Gaussian membership function to model image information in spatial domain has been proposed in this paper. We enhance the contrast of the image by intensification operator, in fuzzy domain. The fuzzifier fh used for image modelling can be changed interactively for diagnosis of medical images. By minimizing the fuzzy entropy of the image information, the parameter t is calculated globally. To detect the edge of the image, a Gaussian type mask in the fuzzy domain is used.
INTRODUCTION
The separation of a scene into object and background is important step in image interpretation. This process is carried out effortlessly by the human visual system, but a machine vision algorithm designed to mimic this action requires object detection. The edge detection in a given image is first step in computer vision for the object segmentation. An edge represented image reduces the amount the data to be processed, retaining the information about the shape of the objects in an image. In gray level image, edge detection identifies the pixel located at the edge. Many methods have been proposed for edge detection. Earlier methods used gradient operator to detect edges of particular orientation. They have poor performance on blurred and noisy images.
INTENSIFICATION OPERATOR AS THE FIRST OPERATION FOR EDGE EXTRACTION
Edge detection is a local operation performed on a window. However, the detected image might not be acceptable to the human for the desired application. Therefore a re-look of edges information may be desired. This requires the system to come back at the original image. In the proposed approach, the gray value distribution of the pixel needs to be kept intact so that a re-look can be possible for the desired result and re-iteration of algorithm can be applied.
RESULT AND DISCUSSION
The proposed algorithm is implemented on MatLab. First, the intensification operator is calculated. Using different values of intensification parameter, t, and the set of intensified images are generated. The edge information for each of the intensified image is observed for its edge information quality. Since this is a subjective measure by human operator, yet standard edge detectors are used for the comparison to choose the desired quality. Further, the iterations are repeated for the other value of the fuzzifier fh.
CONCLUSION
The fuzzy edge detector proposed in this paper extracts the edge using local edge operator over a small window. However, the image is intensified globally with the intensification operator a priory. This operation can bring out the edge around the desired ray value. This automatic handling of the intensification followed by edge detection is carried out by computer. The human operator can interact in defining the desired intensification to get the edge feature of the object. Medical professional may desire to interact of the images to observe the pathological information at various gray levels. By varying the fh value and cross-over point different classes of edge can be extracted.