Image Segmentation In Image Processing
Image segmentation is one of the most important parts in image processing. The main purpose of the image segmentation is to partition the original image into uniform regions. Image segmentation can be applied as the pre-processing stage for the other image processing techniques. There are several techniques for image segmentation in image processing. Image segmentation for bio-medical images has become basic fundamental process to perform significant quantitative analysis to identify the human organs, defected tissues etc.
In this paper, we proposed the image segmentation techniques namely Active Contour Method (ACM), Gray threshold Mechanism (GTM), Sobel Gradient Method (SGM) and Watershed Transform (WST). These four segmentation methods are
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I. INTRODUCTION
Image segmentation is the first step before going to the high level operations such as object recognition and image detail understanding. Image segmentation is commonly used to locate the objects and edges in images. Particularly in medical images[1-4], image segmentation is essential for the feature extraction, image display and image dimensions. For some other applications, it is useful to extract edges or boundaries of the objects of their interest.
Image segmentation techniques are divided into many techniques based upon the classification scheme. The most frequently used segmentation techniques are categorized into two types i.e. region based and edge based approach. The main concept of edge based approach is to identify the boundaries of the object by using the edge detection operator [2] and then extracting the boundaries using the edges information. The issue of edge detection is the existence of
Ravi Boda ,ECE Department, UCE,Osmania University Hyderabad, India, raviouce2015@gmail.com.
Sudheer Kumar Yezerla, ECE Department, UCE’Osmania University Hyderabad, India ,sudheerk260@gmail.com
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The energy of snake model is represented by its location and shape within the image local minima of required image properties. These modal does not solve total contour in image, rather it depends on other methodology such as user dependent, based on high level understanding criteria and information of image data. This interaction will give an approximate shape and starting point for desired contour. The minimized energy is a combination of weights, internal force and external force [8]. Internal force is defined based on shape of snake where
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