Search

A method for the detection of juxta-pleural lung nodules with radius ≤ 5mm in chest computed tomography images is proposed. The lung volume is segmented using region-growing and refined with morphological operations and active contours to include juxta-pleural nodules. Nodule candidates are searched slice-wise inside the lung volume segmentation. Solid nodules are detected by selecting an appropriate threshold inside a representative sliding window. Sub-solid and non-solid nodules are enhanced with a multiscale Laplacian-of-Gaussian filtering prior to their detection. Obvious non-nodule candidates, namely small blood vessels, are discarded using fixed rules. Then, a support vector machine with radial basis function is trained with the remaining candidates to further reduce the number of false positives (FPs). The final system sensitivity is 57.4% with 4 FPs/scan.The performance is similar or better than state-of-the-art methods, especially when considering the high number and small radius of the studied juxta-pleural nodules.

Retinal vessel caliber changes are associated with several major diseases, such as diabetes and hypertension. These caliber changes can be evaluated using eye fundus images. However, the clinical assessment is tiresome and prone to errors, motivating the development of automatic methods. An automatic method based on vessel crosssection intensity profile model fitting for the estimation of vessel caliber in retinal images is herein proposed. First, vessels are segmented from the image, vessel centerlines are detected and individual segments are extracted and smoothed. Intensity profiles are extracted perpendicularly to the vessel, and the profile lengths are determined. Then, model fitting is applied to the smoothed profiles. A novel parametric model (DoG-L7) is used, consisting on a Difference-of-Gaussians multiplied by a line which is able to describe profile asymmetry. Finally, the parameters of the best-fit model are used for determining the vessel width through regression using ensembles of bagged regression trees with random sampling of the predictors (random forests). The method is evaluated on the REVIEW public dataset. A precision close to the observers is achieved, outperforming other state-of-the-art methods. The method is robust and reliable for width estimation in images with pathologies and artifacts, with performance independent of the range of diameters.

This paper describes a segmentation method for automating the region of interest (ROI) delineation in chromatographic images, thus allowing the definition of the image area that contains the fundamental information for further processing while excluding the frame of the chromatographic plate that does not contain relevant data for disease identification. This is the first component of a screening tool for Fabry disease, which will be based on the automatic analysis of the chromatographic patterns extracted from the image ROI. Image segmentation is performed in two phases, where each individual pixel is finally considered as frame or ROI. In the first phase, an unsupervised learning method is used for classifying image pixels into three classes: frame, ROI or unknown. In the second phase, distance features are used for deciding which class the unknown pixels belong to. The segmentation result is post-processed using a sequence of morphological operators in order to obtain the final ROI rectangular area. The proposed methodology was successfully evaluated in a dataset of 41 chromatographic images.

The calibration steps of a 3-D data acquisition system based on the ratio of two intensity images are described. This is a triangulation-based structural light technique in which a multiple of projected light planes are identified by the ratio of two measured intensities. The calibration scheme can be divided in two main steps, which are described in detail: camera calibration and light plane calibration. Camera calibration consists of the determination of some geometric camera parameters that allow the determination of the line of sight of each pixel. Light plane calibration consists of the determination of the correspondence between the measured ratios and the position of the light planes. Some preliminary operations which ease the calculation of depth are also described. System accuracy is evaluated and results on test scenes are presented.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.