Digital Image Processing

Abstract

Many biomedical applications require the detection of infected structures in images. In order to get the originality of the image, it needs to undergo several steps of processing. This will vary from image to image depending on the type of image format, initial condition of the image and the information of interest and the composition of the image scene. While several algorithms have been proposed for semiautomatic extraction of these structures, branching points usually need specific treatment. Medical image segmentation is essential for diagnosing various problems occurs in eye. Retinal image segment is one of the critical issues because these images contain very small nerves and some artifacts present in it. This paper proposes a MGH approach to identify branching points in images. This method is used to change the representation of an image into something that is more meaningful and easier to analyze the interested object. A vector field is calculated using a novel contrast-independent tensor representation based on local phase. Our method extracting image components that are useful in the representation and description of region shape, such as boundaries, infected objects, etc. Non-curvilinear structures, including junctions and end points, are detected using directional statistics of the principal orientation as defined by the tensor. Results on synthetic and real biomedical images show the robustness of the algorithm against changes in contrast, and its ability to detect junctions in highly complex images. This proposed method is based in a model of MGH function which applies the color image to a gray scale image. This method is used to segment the image and selecting the specific image objects, thinning the object to diagnose the region.

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