algorithms

Abstract. We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures. 1

Abstract. We present an automated approach for the segmentation of airways in CT datasets. The approach utilizes the Gradient Vector Flow and consists of two main processing steps. Initially, airway-like structures are identified and their centerlines are extracted. These centerlines are used in a second step to initialize the actual segmentation of the corresponding airways. An evaluation on 20 clinical datasets shows that our method achieves a good average airway branch count (63.0%) without any major leakage. 1

Ideally, an image should be reported and interpreted in the same way (e.g., the same perceived likelihood of malignancy) or similarly by any two radiologists; however, as much research has demonstrated, this is not often the case. Various efforts have made an attempt at tackling the problem of reducing the variability in radiologists ’ interpretations of images. The Lung Image Database Consortium (LIDC) has provided a database of lung nodule images and associated radiologist ratings in an effort to provide images to aid in the analysis of computer-aided tools. Likewise, the Radiological Society of North America has developed a radiological lexicon called RadLex. As such, the goal of this paper is to investigate the feasibility of associating LIDC characteristics and terminology with RadLex terminology. If matches between LIDC characteristics and RadLex terms are found, probabilistic models based on image features may be used as decision-based rules to predict if an image or lung nodule could be characterized or classified as an associated RadLex term. The results of this study were matches for 25 (74%) out of 34 LIDC terms in RadLex. This suggests that LIDC characteristics and associated rating terminology may be better conceptualized or reduced to produce even more matches with RadLex. Ultimately, the goal is to identify and establish a more standardized rating system and terminology to reduce the subjective variability between radiologist annotations. A standardized rating system can then be utilized by future researchers to develop automatic annotation models and tools for computer-aided decision systems. KEY WORDS: Chest CT, digital imaging, image data, image interpretation, imaging informatics, lung, radiographic image interpretation, computer-assisted, reporting, RadLex, semantic, LIDC

Abstract. The algorithm described in this paper takes (a) two temporallyseparated CT scans, I1 and I2, and (b) a series of locations in I1, and it produces, for each location, an affine transformation mapping the locations and their immediate neighborhood from I1 to I2. It does this without deformable registration by using a combination of feature extraction, indexing, refinement and decision processes. Together these essentially “recognize ” the neighborhoods. We show on lung CT scans that this works at near interactive speeds, and is at least as accurate as the Diffeomorphic Demons algorithm [1]. The algorithm may be used both for diagnosis and treatment monitoring. 1

Abstract. In this paper we propose a fully self-assessed adaptive region growing airway segmentation algorithm. We rely on a standardized and self-assessed region-based approach to deal with varying imaging conditions. Initialization of the algorithm requires prior knowledge of trachea location. This can be provided either by manual seeding or by automatic trachea detection in upper airway tree image slices. The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing region. Extensive validation is provided for a set of 20 chest CT scans. Our method exhibits very low leakage into the lung parenchyma, so even though the smaller airways are not obtained from the region growing, our fully automatic technique can provide robust and accurate initialization for other methods.

Abstract. We present an automated approach for airway tree reconstruction from CT images. Our approach performs an initial identification of tubular structures, followed by a reconstruction of the airway tree. During the reconstruction step, tubular objects that are part of the airway tree are identified and linked together based on prior knowledge about the structure of human airway trees. A major advantage of our approach is that it handles local disturbances robustly, as demonstrated by our experiments. 1

Abstract not found

Automatic segmentation of the airway tree from thoracic CT scans using a multi-threshold approach

Abstract. Automated lung segmentation in multidetector computed tomography data is a first processing step in computer-aided quantitative assessment of lung disease. Robust segmentation of diseased lungs is a non-trivial problem which is unsolved up to now. Consequently, lung segmentation results need to be manually verified, which is time-consuming and costly. We propose a novel algorithm for detecting gross abnormal lung segmentations based on a fast 3D shape retrieval approach. First, the segmentation result to verify is used to query a 3D lung shape database containing normal lung shapes. Second, the 3D shape dissimilarity between query and retrieved shape is utilized to assess the abnormality of the segmentation. Our method represents a first step toward the development of a quality assessment system for lung segmentations. Key words: Segmentation abnormality detection, shape retrieval, shape context, lung segmentation 1