The interpretation of high-resolution computed tomography (HRCT) images of the chest showing disorders of the
lung tissue associated with interstitial lung diseases (ILDs) is time-consuming and requires experience. Whereas
automatic detection and quantification of the lung tissue patterns showed promising results in several studies, its
aid for the clinicians is limited to the challenge of image interpretation, letting the radiologists with the problem
of the final histological diagnosis. Complementary to lung tissue categorization, providing visually similar cases
using content-based image retrieval (CBIR) is in line with the clinical workflow of the radiologists.
In a preliminary study, a Euclidean distance based on volume percentages of five lung tissue types was used
as inter-case distance for CBIR. The latter showed the feasibility of retrieving similar histological diagnoses
of ILD based on visual content, although no localization information was used for CBIR. However, to retrieve
and show similar images with pathology appearing at a particular lung position was not possible. In this work,
a 3D localization system based on lung anatomy is used to localize low-level features used for CBIR. When
compared to our previous study, the introduction of localization features allows improving early precision for
some histological diagnoses, especially when the region of appearance of lung tissue disorders is important.
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%).
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 used for
transferring the information from the atlas to a query image. Results show that the method is able to approximate the
segments on sparse HRCT data with slice gap up to 25 millimeters.
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.
A project is underway to develop automated methods of fusing cerebral magnetic resonance angiography (MRA) and x-ray angiography (XRA) for creating accurate visualizations used in planning treatment of vascular disease. We have developed a vascular phantom suitable for testing segmentation and fusion algorithms with either derived images (psuedo-MRA/psuedo-XRA) or actual MRA or XRA image sequences. The initial unilateral arterial phantom design, based on normal human anatomy, contains 48 tapering vascular segments with lumen diameters from 2.5 millimeter to 0.25 millimeter. The initial phantom used rapid prototyping technology (stereolithography) with a 0.9 millimeter vessel wall fabricated in an ultraviolet-cured plastic. The model fabrication resulted in a hollow vessel model comprising the internal carotid artery, the ophthalmic artery, and the proximal segments of the anterior, middle, and posterior cerebral arteries. The complete model was fabricated but the model's lumen could not be cleared for vessels with less than 1 millimeter diameter. Measurements of selected vascular outer diameters as judged against the CAD specification showed an accuracy of 0.14 mm and precision (standard deviation) of 0.15 mm. The plastic vascular model produced provides a fixed geometric framework for the evaluation of imaging protocols and the development of algorithms for both segmentation and fusion.
Computer assisted 3D visualization of the human cerebro-vascular system can help to locate blood vessels during diagnosis and to approach them during treatment. Our aim is to reconstruct the human cerebro-vascular system from the partial information collected from a variety of medical imaging instruments and to generate a 3D graphical representation. This paper describes a tool developed for 3D visualization of cerebro-vascular structures. It also describes a symbolic approach to modeling vascular anatomy. The tool, called Ispline, is used to display the graphical information stored in a symbolic model of the vasculature. The vascular model was developed to assist image processing and image fusion. The model consists of a structural symbolic representation using frames and a geometrical representation of vessel shapes and vessel topology. Ispline has proved to be useful for visualizing both the synthetically constructed vessels of the symbolic model and the vessels extracted from a patient's MR angiograms.
Arterio-venous malformations (AVMs) are a congenital disorder that affects a small percentage of the population. They are treated by blocking or reducing the blood supply followed by surgery. This paper looks in a preliminary way at visualizing the cerebral vasculature and ultimately the AVMs. These visualizations provide support for the surgeons and radiologists. Our concern is to substantiate the point that there are deficiencies in the data correctable with reference to digital subtraction angiograms and we conjecture that knowledge based processing of this data may lead to improved results. The paper explores the basis of the difficulty and it compares the performance of several algorithms. Simple geometric objects are studied and the dependence of error on several parameters is shown. A comparison is drawn between the richness of the data available from x-ray angiograms (XRAs) and magnetic resonance angiograms (MRAs). Inferences are drawn on approaches that may be appropriate for the evolution of a description of the vasculature. Comment is also made on the way in which different representations may be compared.