Medical Image Enhancement by Using Visual Basic ¿6"
Image Processing
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Abstract:Ultrasound images, though easy to obtain, have inherent flaws due to low frequency tissue image aberrations such as poor contrast caused by the presence of the granular speckle noise. This project aims to improve the ability to differentiate between healthy and malignant conditions via the use of homomorphic filtering such as (Laplacian filter, mean filter, median filter, and sobel filter). The characteristics of the Gaussian window function are adaptively changed based on the input ultrasound image samples taken from different medical ultrasonography scans. A cost estimation function...
Abstract:Ultrasound images, though easy to obtain, have inherent flaws due to low frequency tissue image aberrations such as poor contrast caused by the presence of the granular speckle noise. This project aims to improve the ability to differentiate between healthy and malignant conditions via the use of homomorphic filtering such as (Laplacian filter, mean filter, median filter, and sobel filter). The characteristics of the Gaussian window function are adaptively changed based on the input ultrasound image samples taken from different medical ultrasonography scans. A cost estimation function helps establish the adaptability of the filter by means of calculating the mean and variance of local windows and correspondingly evaluate the most discriminative part of the image sample in process. Signal to noise ratio is adopted as an image quality measure of the enhancement operation. Experimental results show the effectiveness of the hemimorphic filtering and the robustness of the overall system as a useful diagnostic tool. This project discusses the result of using (Laplacian filter, mean filter, median filter, sobel filter) filters to get good knowledge of using these filters.
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