User aid-based evolutionary computation for optimal parameter setting of image enhancement and segmentation
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Applications of imaging and image processing become a part of our daily life and find their crucial way in real-world areas. Accordingly, the corresponding techniques get more and more complicated. Many tasks are recognizable for a image processing chain, such as, filtering, color balancing, enhancement, segmentation, and post processing. Generally speaking, all of the image processing techniques need a control parameter setting. The better these parameters are set the better results can be achieved. Usually, these parameters are real numbers so search space is really large and brute-force searching is impossible or at least very time consuming. Therefore, the optimal setting of the parameters is an essential requirement to obtain desirable results. Obviously, we are faced with an optimization problem, which its complexity depends on the number of the parameters to be optimized and correlation among them. By reviewing the optimization methods, it can be understood that metaheuristic algorithms are the best candidates for these kind of problems. Metaheuristic algorithms are iterative approaches which can search very complex large spaces to come up with an optimal or close to optimal solution(s). They are able to solve black-box global optimization problems which are not solvable by classic mathematical methods. The first part of this thesis optimizes the control parameters for an eye-illusion, image enhancement, and image thresholding tasks by using an interactive evolutionary optimization approach. Eye illusion and image enhancement are subjective human perception-based issues, so, there is no proposed analytical fitness function for them. Their optimization is only possible through interactive methods. The second part is about setting of active contour (snake) parameters. The performance of active contours (snakes) is sensitive to its eight correlated control parameters which makes the parameter setting problem complex to solve. In this work, wehave tried to set the parameters to their optimal values by using a sample segmented image provided by an expert. As our case studies, we have used breast ultrasound, prostate ultrasound, and lung X-ray medical images. The proposed schemes are general enough to be investigated with other optimization methods and also image processing tasks. The achieved experimental results are promising for both directions, namely, interactive-based image processing and sample-based medical image segmentation.