María J. Carreira

A snake for CT image segmentation integrating region and edge information

Abstract The 3D representation and solid modeling of knee bone structures taken from computed tomography (CT) scans are necessary processes in many medical applications. The construction of the 3D model is generally carried out by stacking the contours obtained from a 2D segmentation of each CT slice, so the quality of the 3D model strongly depends on the precision of this segmentation process. In this work we present a deformable contour method for the problem of automatically delineating the external bone (tibia and fibula) contours from a set of CT scan images. We have introduced a new region potential term and an edge focusing strategy that diminish the problems that the classical snake method presents when it is applied to the segmentation of CT images. We introduce knowledge about the location of the object of interest and knowledge about the behavior of edges in scale space, in order to enhance edge information. We also introduce a region information aimed at complementing edge information. The novelty in that is that the new region potential does not rely on prior knowledge about image statistics; the desired features are derived from the segmentation in the previous slice of the 3D sequence. Finally, we show examples of 3D reconstruction demonstrating the validity of our model. The performance of our method was visually and quantitatively validated by experts.

A Snake for Retinal Vessel Segmentation

This paper presents an innovative methodology to detect the vessel tree in retinal angiographies. The automatic analysis of retinal vessel tree facilitates the computation of the arteriovenous index, which is essential for the diagnosis of a wide range of eye diseases. We have developed a system inspired in the classical snake but incorporating domain specific knowledge, such as blood vessels topological properties. It profites mainly from the automatic localization of the optic disc and from the extraction and enhancement of the vascular tree centerlines. Encouraging results in the detection of arteriovenous structures are efficiently achieved, as shown by the systems performance evaluation on the publicy available DRIVE database.

Computer‐aided diagnoses: Automatic detection of lung nodules

This work describes a computational scheme for automatic detection of suspected lung nodules in a chest radiograph. A knowledge-based system extracts the lung masks over which we will apply the nodule detection process. First we obtain the normalized cross-correlation image. Next we detect suspicious regions by assuming a threshold. We examine the suspicious regions using a variable threshold which results in the growth of the suspicious areas and an increase in false positives. We reduce the large number of false positives by applying the facet model to the suspicious regions of the image. An algorithmic classification process gives a confidence factor that a suspicious region is a nodule. Five chest images containing 30 known nodules were used as a training set. We evaluated the system by analyzing 30 chest images with 40 confirmed nodules of varying contrast and size located in various parts of the lungs. The system detected 100% of the nodules with a mean of six false positives per image. The accuracy and specificity were 96%.

Retinal verification using a feature points-based biometric pattern

Biometrics refer to identity verification of individuals based on some physiologic or behavioural characteristics. The typical authentication process of a person consists in extracting a biometric pattern of him/her and matching it with the stored pattern for the authorised user obtaining a similarity value between patterns. In this work an efficient method for persons authentication is showed. The biometric pattern of the system is a set of feature points representing landmarks in the retinal vessel tree. The pattern extraction and matching is described. Also, a deep analysis of similarity metrics performance is presented for the biometric system. A database with samples of retina images from users on different moments of time is used, thus simulating a hard and real environment of verification. Even in this scenario, the system allows to establish a wide confidence band for the metric threshold where no errors are obtained for training and test sets.

Retinal vessel tree segmentation using a deformable contour model

This paper presents an improved version of our specific methodology to detect the vessel tree in retinal angiographies. The automatic analysis of retinal vessel tree facilitates the computation of the arteriovenous index, which is essential for the diagnosis several eye diseases. The developed system is inspired in the classical snake but incorporating domain specific knowledge, such as blood vessels topological properties. It profits from the automatic localization of the optic disc, the vessel creases extraction and, as a recent innovation, the morphological vessel segmentation, all developed in our research group. After researching and testing our system, the parameter configuration has been enhanced. Significantly better results in the detection of arteriovenous structures are obtained, keeping a high efficiency, as shown by the systems performance evaluation on the publicly available DRIVE database.

Personal verification based on extraction and characterisation of retinal feature points

This paper describes a methodology of verification of individuals based on a retinal biometric pattern. The pattern consists in feature points of the retinal vessel tree, namely bifurcations and crossovers. These landmarks are detected and characterised adding semantic information to the biometric pattern. The typical authentication process of a person once extracted the biometric pattern includes matching it with the stored pattern for the authorised user obtaining a similarity value between them. A matching algorithm and a deep analysis of similarity metrics performance is presented. The semantic information added for the feature points allows to reduce the computation load in the matching process as only points classified equally can be matched. The system is capable of establishing a safe confidence band in the similarity measure space between scores for patterns of the same individual and between different individuals.

Localization and extraction of the optic disc using the fuzzy circular hough transform

This paper presents an algorithm for automatic extraction of the optic disc in retinal images. The developed system consists of two main parts. Firstly, the localization of the region containing the optic disc is performed by means of a clustering algorithm. Then, in order to extract the optic disc, the fuzzy circular Hough transform is applied to the edges of the region. The optic disc might not be extracted since there are vessels in the inside of the optic disc. To avoid this, a crease extraction algorithm is applied to the retinal image. The vessels are extracted and the vessel edge points contained in the edge image are removed. The final system was tested by ophthalmologists. The localization of the region of interest is correct in 100% of the cases and the extraction of the optic disc is obtained in 98% of the cases.

Retinal Angiography Based Authentication

Traditional authentication (identity verification) systems, employed to gain access to a private area in a building or to data stored in a computer, are based on something the user has (an authentication card, a magnetic key) or something the user knows (a password, an identification code). But emerging technologies allow for more reliable and comfortable for the user, authentication methods, most of them based in biometric parameters. Much work could be found in literature about biometric based authentication, using parameters like iris, voice, fingerprint, face characteristics, and others. In this work a novel authentication method is presented, and first results obtained are shown. The biometric parameter employed for the authentication is the retinal vessel tree, acquired through a retinal angiography. It has already been asserted by expert clinicians that the configuration of the retinal vessels is unique for each individual and that it does not vary in his life, so it is a very well suited identification characteristic. Before the verification process can be executed, a registration step is needed to align both the reference image and the picture to be verified. A fast and reliable registration method is used to perform that step, so that the whole authentication process takes very little time.

Computer-Aided Lung Nodule Detection in Chest Radiography

Computer-aided diagnoses programs are developed for alerting the radiologist by indicating potential sites of lesions. One of the important tasks in the development of a computational system for detecting lung nodules is to diminish the number of false positives keeping on high sensitivities. In this work we describe a system for automatic lung nodule detection. The detection is carried out in several stages. First, a knowledge-based segmentation process delimits the lung boundaries. Then, a progressive thresholding of an image in which the conspicuity of nodules has been enhanced by means of filter matching and a set of growth and circularity tests fix the areas suspicious of being nodules into region previously labelled as lungs. Finally, these suspicious regions are confirmed as nodules in a new feature (curvature) space, which gives us an important help in the task of distinguishing true and false nodules from previously extracted suspicious regions. Preliminary results are very promising, achieving high sensitivities with a little ratio of false positives.

Automatic Extraction of the Retina AV Index

In this paper we describe a new method to approach the diameter of veins and arteries in the retina vascular tree, focusing not only on precision and reliability, but also on suitability for on-line assistance. The performed system may analyze the region of interest selected in the image to estimate the retinal arteriovenous index. This analysis involves two different steps: the blood vessels detection, which extracts the vascular structures present in the image, and the blood vessel measurement, which estimates the caliber of the already located vessels. The method may locate 90% of the structures, giving a reliability of 99% in detection and 95% in measurement.