Novel methods for automatic segmentation of abnormal lung parenchyma utilizing watershed and wavelet transforms
thesisposted on 24.05.2021, 11:07 by Rushin Shojaii
CT scan of the thorax is widely used to diagnose and evaluate numerous lung diseases. These scans yield a large amount of image data. The expanding volume of thoracic CT studies along with the increase of image data, elucidates the need of computer-aided diagnosis (CAD) schemes to assist the radiologists. Since several lung diseases are diagnosed based on the patterns of lung tissue in medical images, texture segmentation is an essential part of the [sic] most CAD systems. The processing step of most CAD systems is lung segmentation. In the first part of this thesis a novel approach for lung segmentation is proposed. The proposed method is based on watershed transform, which is fast and accurate. Lung region is precisely marked with internal and external markers. The markers are combined with the gradient image of the original data, then watershed transform is applied on the combined data to find the lung borders. A "Rolling ball" filter is used to fill the cavities and make the contour smooth while preserving the original borders. In the second part of this research work a novel composite method is proposed to segment the abnormality in lung tissue. The proposed approach is based on wavelet transform and intensity similarities. Our focus is on the honeycomb texture in lung tissue, which occurs with several interstitial lung diseases. After segmenting lung regions, Wavelet Transform is applied to decompose the image. The transformed lung region is thresholded to extract high resolution areas. Then the regions with low pixel intensities are kept and grown to segment the honeycomb regions. The proposed method has been tested on 91 pediatric chest CT images containing healthy and unhealthy lung images. Statistical analysis has been done and the results show the sensitivity of 100% along with the average Specificity of 95.14 %. A comparison with AMFM (82.5 % sensitivity and 99.9 % specificity) and ANN methods (100 % sensitivity and 88.1 % specificity) reveals the superiority of the proposed approach. The test results of both the lung segmentation and abnormal lung tissue segmentation techniques validate the robustness of the proposed methods.