Implementation of PSO & HB Image Enhancement Techniques For Tumor Detection (original) (raw)
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A NOVEL APPROACH FOR IMAGE ENHANCEMENT USING PSO
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Particle Swarm Optimization based Contrast Limited enhancement for mammogram images
In the present medical scenario detection of breast cancer in its early stage is a very immense challenge. Many histogram based enhancement are present today. In this paper a Particle Swarm Optimization (PSO) for tuning the enhancement parameter of Contrast Limited Adaptive Histogram Equalization (CLAHE) based on Local Contrast Modification (LCM) is presented. The PSO method of parameter tuning adopted for LCM-CLAHE enhancement for mammogram images achieves very good quality of images compared to other exiting methods. The quality of enhanced image is tested using an efficient objective criteria based on entropy and edge information of the image. Results are compared with other enhancement techniques such as histogram equalization, unsharpmasking. The performance of this method is tested using Peak Signal to Noise Ratio. The quality of image shows that image obtained after this method can be useful for efficient detection of breast cancer in further process like segmentation, classi...
Modified Histogram Equalization for Image Contrast Enhancement Using Particle Swarm Optimization
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2011
A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this paper. This technique modifies the probability density function of an image by introducing constraints prior to the process of histogram equalization (HE). These constraints are formulated using two parameters which are optimized using swarm intelligence. This technique of contrast enhancement takes control over the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment factor is then added to the result to normalize the change in the luminance level after enhancement. This factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of highly deviated intensities have greater impact in changing the contrast of an image. This approach provides a convenient and effective way to control the enhancement process, while being adaptive to various types of images. Experimental results show that th...
IJERT-Image Enhancement using Accelerated Particle Swarm Optimization
International Journal of Engineering Research and Technology (IJERT), 2015
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IAEME Publications, 2020
Edge detection is an important task in image processing. Edge is defined as the boundary between two regions separated by two relatively distinct gray level properties. Traditional edge detection methods give rise to the exponential increment of computational time. In this paper, edge detection in gray level images is done by using Renyi entropy and particle swarm optimization (PSO) algorithm. The Renyi entropy is a one-parameter generalization of the Shannon entropy. Here Renyi entropy was calculated for the one-dimensional histogram of the images. PSO is an efficient and powerful population-based stochastic search technique for solving optimization problems, and this has been widely applicable in many scientific and engineering fields. The selection of the initial population in a population-based heuristic optimization method is most important, as it affects the search for a number of iterations and has an influence on the final solution. If the prior information about the optima is not available, then the initial population is selected randomly using a pseudorandom numbers. The main advantage of PSO algorithm is its simple in structure, easy to use, speed and robustness