Automatic lung segmentation using graph cut optimization.
Medical Imaging revolutionized the practice of diagnostic medicine by providing a means of visualizing the internal organs and structure of the body. Computer technologies have played an increasing role in the acquisition and handling, storage and transmission of these images. Due to further advances in computer technology, research efforts have turned towards adopting computers as assistants in detecting and diagnosing diseases, resulting in the incorporation of Computer-aided Detection (CAD) systems in medical practice. Computed Tomography (CT) images have been shown to improve accuracy of diagnosis in pulmonary imaging. Segmentation is an important preprocessing necessary for high performance of the CAD. Lung segmentation is used to isolate the lungs for further analysis and has the advantage of reducing the search space and computation time involved in disease detection. This dissertation presents an automatic lung segmentation method using Graph Cut optimization. Graph Cut produces globally optimal solutions by modeling the image data and spatial relationship among the pixels. Several objects in the thoracic CT image have similar pixel values to the lungs, and the global solutions of Graph Cut produce segmentation results where the lungs, and all other objects similar in intensity value to the lungs, are included. A distance prior encoding the euclidean distance of pixels from the set of pixels belonging to the object of interest is proposed to constrain the solution space of the Graph Cut algorithm. A segmentation method using the distance-constrained Graph Cut energy is also proposed to isolate the lungs in the image. The results indicate the suitability of the distance prior as a constraint for Graph Cut and shows the effectiveness of the proposed segmentation method in accurately segmenting the lungs from a CT image.
Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2015.
Theses - Computer Science.