Lung Lesion Segmentation Using Gaussian Filter and Discrete Wavelet Transform

Lung cancer is the growth of a tumour, referred to as a nodule that arises from cells covering the airways of the respiratory arrangement. Effective detection of lung cancer at premature stages enables any cure options, and reduce risk of insidious surgery and increased continued existence rate. Recently, image processing techniques are extensively used in different medical areas for lung tumour image improvement in early detection and cure stages. This is due to the importance of the time factor of discovering the abnormality issues in target images. The developed system is mainly an algorithm combining different image processing techniques such as filtering, erosion, discrete wavelets transform, and thresholding. However, the main aim of this work is to investigate the effectiveness of different filters along with different types of discrete wavelets toward an accurate segmentation of a lung tumour in a CT image. The experimental results of the developed system show that the use of Gaussian filter with the Haar wavelets is the best for such segmentation task.