Automated methods of detecting lung disease typically involve the following: 1) Subdividing the lung into small
regions of interest (ROIs). 2) Calculating the features of these small ROIs. 3) Applying a machine learnt classifier
to determine the class of each ROI. When the number of features that need to be calculated is large, as in the
case of filter bank methods or in methods calculating a large range of textural properties, the classification
can run quite slowly. This is even more noticeable when a number of disease patterns are considered. In this
paper, we investigate the possibility of using a cascade of classifiers to concentrate the processing power on
promising regions. In particular, we focused on the detection of the honeycombing disease pattern. We used
knowledge of the appearance and the distribution of honeycombing to selectively classify ROIs. This avoids the
need to explicitly classify all ROIs in the lung; making the detection process more effcient. We evaluated the
performance of the system over 42 HRCT slices from 8 different patients and show that the system performs
the task of detecting honeycombing with a high degree of accuracy (accuracy = 86.2%, sensitivity = 90.0%,
specificity = 82.2%).
KEYWORDS: Computer aided diagnosis and therapy, Databases, Lung, Medical imaging, Telemedicine, Java, Computer aided design, Data communications, Knowledge acquisition, Picture Archiving and Communication System
As part of the Learning Medical Imaging Knowledge project, we are developing a knowledge-based, machine learning and knowledge acquisition framework for systematic feature extraction and recognition of a range of lung diseases from High Resolution Computed Tomography (HRCT) images. This framework allows radiologists to remotely diagnose and share expert knowledge about lung HRCT interpretation, which is then used to develop a Computer Aided Diagnosis (CAD) system for lung disease. In this paper, we describe the knowledge acquisition system LMIK, which is Internet-based and platform-independent. The LMIK utilises the Internet to provide users with secure access to patient and research data and facilitates communication among highly qualified radiologists and researchers. It is currently used by five radiologists and over 20 researchers and has proved to be an invaluable research tool. Research is underway to develop computer algorithms for automatic diagnosis of lung diseases. In future, these algorithms will be integrated into LMIK to equip it with CAD capabilities to improve diagnostic accuracy of radiologists and extend availability of expert clinical knowledge to wider communities.