Bronchopulmonary Segments Approximation using Anatomical Atlas

Bronchopulmonary segments are valuable as they give more accurate localization than lung lobes. Traditionally, determining the segments requires segmentation and identification of segmental bronchi, which, in turn, require volumetric imaging data. In this paper, we present a method for approximating the bronchopulmonary segments for sparse data by effectively using an anatomical atlas. The atlas is constructed from a volumetric data and contains accurate information about bronchopulmonary segments. A new ray-tracing based image registration is developed for transferring the information from the atlas to a query image. Results show that the method is able to approximate the segments using sparse HRCT data with slice gap up to 25 millimeters

Computerised analysis of lung anatomy in high-resolution CT

The amount of imaging information produced by today’s High-Resolution CT (HRCT) scanners is beyond the ability of a radiologist to process in normal clinical practice. A computer-aideddiagnosis (CAD) system is then required to scan the large number of images and draw the radiologist’s attention to fewer but diagnostically useful images.In this dissertation, one of the most important components of the CAD system is investigated: a computerised system that understands the anatomy of the lung. Like the human radiologist,a CAD system must have a deep understanding of lung anatomy before it learns to detect diseases. Such an automated system must solve three classical problems in medical image analysis -segmentation, classification and registration.Segmentation: We propose a framework that simplifies the task of developing various segmentation algorithms. The framework consists of a simple, yet flexible, workflow and common imageprocessing library that unifies and enables rapid development of such algorithms. We present segmentation algorithms for 10 different anatomical structures developed using the framework.New techniques are also developed to improve the accuracy of the segmentation over the existing methods.Classification: We extend an interactive machine learning approach called Ripple-Down Rules (RDR) to address the classification of lung surface in HRCT, using shape and positional analysis.We develop a prototype of a specialised tool that allows a human expert to interactively train the system. The performance of the fully trained RDR classifier is then compared to the traditionalC4.5 decision-tree classifier.Registration: We propose a novel method for surface-based registration, which is used to compare lungs from two different subjects and to map information from one lung to another. Themethod has several properties that suit the nature of HRCT data and it is able to overcome some limitations posed by the existing surface registration techniques. One of the most importantaspects is the ability to register a partial 3D surface with a full 3D surface.As a result of the contributions in these three areas, we built a computerised lung atlas using a number of segmentation and classification techniques. The atlas contains information aboutanatomical landmarks and regions, which can be mapped to another scan using the proposed registration. The anatomical information acquired by the proposed system can be used by othercomputerised systems that detect lung disease patterns and, ultimately, advance the research for CAD