Biomedical subjects

B P Lelieveldt

Publications and source records attributed to B P Lelieveldt.

6 recordsLinked to original sources

PubMed2001

Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images.

A fully automated approach to segmentation of the left and right cardiac ventricles from magnetic resonance (MR) images is reported. A novel multistage hybrid appearance model methodology is presented in which a hybrid active shape model/active appearance model (AAM) stage helps avoid local minima of the matching function. This yields an overall more favorable matching result. An automated initialization method is introduced making the approach fully automated. Our method was trained in a set of 102 MR images and tested in a separate set of 60 images. In all testing cases, the matching resulted in a visually plausible and accurate mapping of the model to the image data. Average signed border positioning errors did not exceed 0.3 mm in any of the three determined contours-left-ventricular (LV) epicardium, LV and right-ventricular (RV) endocardium. The area measurements derived from the three contours correlated well with the independent standard (r = 0.96, 0.96, 0.90), with slopes and intercepts of the regression lines close to one and zero, respectively. Testing the reproducibility of the method demonstrated an unbiased performance with small range of error as assessed via Bland-Altman statistic. In direct border positioning error comparison, the multistage method significantly outperformed the conventional AAM (p < 0.001). The developed method promises to facilitate fully automated quantitative analysis of LV and RV morphology and function in clinical setting.

Computer Simulation↗

PubMed2001

Neuro-fuzzy systems for computer-aided myocardial viability assessment.

This paper describes a multimodality framework for computer-aided myocardial viability assessment based on neuro-fuzzy techniques. The proposed approach distinguishes two main levels: the modality-independent inference level and the modality-dependent application level. This two-level distinction releases the hard constraint of multimodality image registration. An abstract description template is used to describe the different myocardial functions (contractile function, perfusion, metabolism). Parameters extracted from different image modalities are combined to derive a diagnostic image. The neuro-fuzzy techniques make our system transparent, adaptive and easily extendable. Its effectiveness and robustness are demonstrated in a positron emission tomography/magnetic resonance imaging data fusion application.

Artificial Intelligence↗

PubMed2001

Automated observer-independent acquisition of cardiac short-axis MR images: a pilot study.

The authors compared an automated observer-independent acquisition planning method for short-axis multisection multiphase cardiac magnetic resonance imaging studies with conventional manual image planning. Systematic and random differences and reproducibility of left ventricular function measurements and image geometry were evaluated in five healthy adult volunteers and 20 patient studies. Results with the automated planning method were as accurate and reproducible as those with the manual planning method.

Adult↗

PubMed2000

Quantification of global and regional ventricular function in cardiac magnetic resonance imaging.

One of the strong assets of cardiac magnetic resonance (CMR) is its ability to assess myocardial anatomy, structure, function, flow, and perfusion within a single examination. Quantification of global and regional function from magnetic resonance imaging (MRI) studies was shown to be accurate and reproducible in experimental and clinical research studies. With the advent of high-performance MRI scanners and newly developed pulse sequences, image acquisition times have been reduced considerably in recent years. However, the clinical use of CMR remains limited for various reasons. Among these limitations is that the amount of images obtained in a typical cardiac examination is so large that visual and especially quantitative image analysis is tedious and time consuming. There is an urgent need for optimized dedicated software tools featuring highly automated contour detection and optimized display capabilities to present the quantitative results to the physician in an orderly fashion, thus facilitating clinical decision making. This article focuses on the state of the art in CMR postprocessing techniques for quantitative assessment of global and regional function.

Female↗

PubMed1999

Anatomical model matching with fuzzy implicit surfaces for segmentation of thoracic volume scans.

Many segmentation methods for thoracic volume data require manual input in the form of a seed point, initial contour, volume of interest etc. The aim of the work presented here is to further automate this segmentation initialization step. In this paper an anatomical modeling and matching method is proposed to coarsely segment thoracic volume data into anatomically labeled regions. An anatomical model of the thorax is constructed in two steps: 1) individual organs are modeled with blended fuzzy implicit surfaces and 2) the single organ models are grouped into a tree structure with a solid modeling technique named constructive solid geometry (CSG). The combination of CSG with fuzzy implicit surfaces allows a hierarchical scene description by means of a boundary model, which characterizes the scene volume as a boundary potential function. From this boundary potential, an energy function is defined which is minimal when the model is registered to the tissue-air transitions in thoracic magnetic resonance imaging (MRI) data. This allows automatic registration in three steps: feature detection, initial positioning and energy minimization. The model matching has been validated in phantom simulations and on 15 clinical thoracic volume scans from different subjects. In 13 of these sets the matching method accurately partitioned the image volumes into a set of volumes of interest for the heart, lungs, cardiac ventricles, and thorax outlines. The method is applicable to segmentation of various types of thoracic MR-images, provided that a large part of the thorax is contained in the image volume.

Heart↗

PubMedDate not supplied

Myocardium extraction in positron emission tomography based on soft computing.

This paper presents an efficient and accurate approach to myocardium extraction in Positron Emission Tomography (PET) images based on a careful application of soft computing techniques. PET images present a noisy background, making the automatic myocardium extraction and uptake quantification a difficult task. In this work a Self Organized Radial Basis Function Network (SRBFN) is designed to focus on the myocardium in an iterative process until the total extraction of the myocardium from the noisy background is achieved. Fuzzy sets and fuzziness measures are used to compute the error of the network. The method was tested on a set of nine images of different patients and its effectiveness is illustrated in two patients showing tracer uptake defects.

Fluorodeoxyglucose F18↗