Thesis Abstract

<p> For accurate and high rate of lung cancer detection using image processing, an effective preprocessing technique is required. A quality preprocessing technique is necessary to ensure effective removal of noise that interferes with the features of the image and hence improves lung cancer detection rate and accuracy. In this research, an Improved Gaussian Filter (IGF) technique was developed for effective lung image preprocessing. For image segmentation, Otsu thresholding method was used. The binarization was used for classification and Matlab as the simulation software. The filtering performance of the developed method was compared with the filtering performance of optimized Gaussian Filter (GF), the result showed that PSNR values obtained using improved Gaussian filter has an average gain of 1.2557dB over the PSNR values obtained using the optimized Gaussian Filter (GF). The detection rate and accuracy of the output from the Improved Gaussian filter was compared to the detection rate and accuracy of the output of the Gaussian filter and the result showed an improvement in average lung cancer detection rate and accuracy of 17.5% and 2.68% respectively when Improved Gaussian filter was used. <br></p>

Thesis Overview