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Entropy Based Automatic Image Segmentation

2D image entropy

Digital images are discrete representation of analogue nature. Like conventional photographic images most of them have rectangular form. Each one image is consisted of many small building blocks called pixels. Pixels are situated inside 2 dimensional image matrixes. Each one pixel represents different light value. In color images, colors are represented using RGB color model. RGB images have 3 separated matrixes where each red, green and blue light intensities values for each pixel are stored. There are several color representation models, but RGB is most convenient for digital computers.

Image entropy of image histogram show the "chaos" inside an image. Calculation of this function is a complex procedure that measures each histogram column probability corresponding total information inside all other pixels. This function is used to determine which pixels wear the most of the information content inside an image. This function is used for automatic image thresholding and for image object segmentation. After calculating image histogram entropy function we can select entropy maximum and using this maximum position for a threshold value.

Automatic Image Segmentation using 2D Multistage Entropy

An Entropy-based Objective Evaluation Method for Image Segmentation

3D Entropy based image segmentation

2D entropy function is commonly used for many image segmentation applications. 2D entropy lacks his robustness when multiple images with large variety contrasts between different regions and background need to be segmented. Here 3D entropy function comes in sense. Before this we need to define and calculate 3D image histogram (joint pixel probability function). Using this histogram we can calculate and visualize 3D entropy function. Finding its maximum we can threshold image much better than using 2D entropy. This type of thresholding will provide us information regarding two types of joint pixels: homogeneity zones (joint pixels having the same brightness) and non homogeneity (object surrounding areas and shades).

3D Entropy Based Image Segmentation