Local frequency and envelope estimation by Teager-Kaiser energy operators in white-light scanning interferometry.

In this work, a new method for surface extraction in white light scanning interferometry (WLSI) is introduced. The proposed extraction scheme is based on the Teager-Kaiser energy operator and its extended versions. This non-linear class of operators is helpful to extract the local instantaneous envelope and frequency of any narrow band AM-FM signal. Namely, the combination of the envelope and frequency information, allows effective surface extraction by an iterative re-estimation of the phase in association with a new correlation technique, based on a recent TK cross-energy operator. Through the experiments, it is shown that the proposed method produces substantially effective results in term of surface extraction compared to the peak fringe scanning technique, the five step phase shifting algorithm and the continuous wavelet transform based method. In addition, the results obtained show the robustness of the proposed method to noise and to the fluctuations of the carrier frequency.

Fuzzy clustering-based segmented attenuation correction in whole-body PET imaging.

Segmented attenuation correction is now a widely accepted technique to reduce noise propagation from transmission scanning in positron emission tomography (PET). In this paper, we present a new method for segmenting transmission images in whole-body scanning. This reduces the noise in the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the fuzzy C-means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are, therefore, segmented into populations of uniform attenuation based on knowledge of the human anatomy. The clustering procedure starts with an overspecified number of clusters followed by a merging process to group clusters with similar properties (redundant clusters) and removal of some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and allows the classification of both pre- or post-injection transmission images obtained using either coincident 68Ge or single-photon 137Cs sources into main tissue components in terms of attenuation coefficients. A high-quality transmission image of the scanner bed is obtained from a high statistics scan and added to the transmission image. The segmented transmission images are then forward projected to generate attenuation correction factors to be used for the reconstruction of the corresponding emission scan. The technique has been tested on a chest phantom simulating the lungs, heart cavity and the spine, the Rando-Alderson phantom, and whole-body clinical PET studies showing a remarkable improvement in image quality, a clear reduction of noise propagation from transmission into emission data allowing for reduction of transmission scan duration. There was very good correlation (R2 = 0.96) between maximum standardized uptake values (SUVs) in lung nodules measured on images reconstructed with measured and segmented attenuation correction with a statistically significant decrease in SUV (17.03% +/- 8.4%, P < 0.01) on the latter images, whereas no proof of statistically significant differences on the average SUVs was observed. Finally, the potential of the FCM algorithm as a segmentation method and its limitations as well as other prospective applications of the technique are discussed.

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.

Regional differences and metabolic changes in normal aging of the human brain: proton MR spectroscopic imaging study.

BACKGROUND AND PURPOSE: Aging is recognized to originate from a diversity of mechanisms that blur the limits between normal and pathologic processes. The purpose of this study was to determine the early effect of normal aging on the regional distribution of brain metabolite concentrations, including N-acetylaspartate (NAA), a major neuronal marker, choline (Cho), and creatine (Cr). METHODS: Thirty-two healthy participants, ages 21 to 61 years, were examined by proton MR spectroscopic (1H MRS) imaging. 1H MRS imaging acquisitions were performed in two brain locations: the centrum semiovale and the temporal lobe. Thirty voxels were selected in four cerebral regions, cortical, semioval, temporal, and hippocampal, and 1H MR spectra were processed to determine the metabolite ratios. RESULTS: With advancing age of the participants, the ratios of %NAA, NAA:Cho, and NAA:Cr were significantly decreased, whereas the ratios of %Cho and %Cr were significantly increased in the cortical, semioval, and temporal regions. On the basis of the significant metabolic difference determined by cluster analysis, two groups of 16 participants with ages ranging from 21 to 39 years (younger group) and 40 to 61 years (older group) were compared. Repeated measures analysis of variance tests, with multiple comparison procedures between the two age groups and among the four brain region groups, showed significant decreases of the %NAA, NAA:Cho, and NAA:Cr ratios in the semioval and temporal regions of the older group compared with the younger group. When compared with other cerebral regions, %NAA and %Cho ratios were significantly decreased in the hippocampal and cortical regions, respectively. CONCLUSION: These metabolic changes suggest that brain aging is characterized by a reduction in neuronal viability or function associated with an accelerated membrane degradation and/or an increase in glial cell numbers.

Temporal covariance analysis of first-pass contrast-enhanced myocardial magnetic resonance images.

In this paper a temporal covariance method designed to analyze a Magnetic resonance (MR) image sequence of myocardial perfusion is presented. This method is used to map the first-pass transit of a contrast agent (Gd-chelates) through the heart. A map of bolus transit delay is constructed pixel by pixel corresponding to a myocardial reference using a temporal covariance measure. The resulting covariance map is a parametric image representing regions with different temporal dynamics. The proposed method is evaluated in 14 patients with coronary artery disease and eight healthy volunteers. Under rest and stress, covariance method is able to reveal a perfusion defect in stenosed coronary-artery-related myocardium. Furthermore, the method presents the advantage of its easy implementation and real-time parametric map construction.

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.

Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering.

A method is presented for fully automated detection of Multiple Sclerosis (MS) lesions in multispectral magnetic resonance (MR) imaging. Based on the Fuzzy C-Means (FCM) algorithm, the method starts with a segmentation of an MR image to extract an external CSF/lesions mask, preceded by a local image contrast enhancement procedure. This binary mask is then superimposed on the corresponding data set yielding an image containing only CSF structures and lesions. The FCM is then reapplied to this masked image to obtain a mask of lesions and some undesired substructures which are removed using anatomical knowledge. Any lesion size found to be less than an input bound is eliminated from consideration. Results are presented for test runs of the method on 10 patients. Finally, the potential of the method as well as its limitations are discussed.

Analysis of dynamic nuclear cardiac images by covariance function.

A new method using the covariance function as a measure of functional similarity is presented for dynamic analysis of a sequence of scintigraphic cardiac images taken throughout the cardiac cycle. The similarity between the temporal response of pixels in a reference region of the scintigraphic image series and the temporal response of the remaining pixels in the image sequence is calculated. The resulting covariance image is a functional image representing regions with different temporal dynamics. A box-plot representation of this image permits better interpretation for clinical decision making. This analysis allows visualization of the ventricular emptying pattern, which may be useful in studying motion or conduction abnormalities. Compared to Fourier analysis, our method does not make assumption that the data are periodic and that the transition between the first and the last frame of the study is smooth. The proposed method has been performed in one normal patient and twenty patients with abnormal ventricular contraction patterns, and there is no computational difficulty in its implementation. A comparison with the Fourier analysis is performed.

A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI.

Quantitative assessment of Magnetic Resonance Imaging (MRI) lesion load of patients with multiple sclerosis (MS) is the most objective approach for a better understanding of the history of the pathology, either natural or modified by therapies. To achieve an accurate and reproducible quantification of MS lesions in conventional brain MRI, an automatic segmentation algorithm based on a multiresolution approach using pyramidal data structures is proposed. The systematic pyramidal decomposition in the frequency domain provides a robust and flexible low level tool for MR image analysis. Context-dependent rules regarding MRI findings in MS are used as high level considerations for automatic lesion detection.

Automated detection of the left ventricular region in magnetic resonance images by Fuzzy c-Means model.

A new method for automated detection of the Left Ventricular (LV) region in Magnetic Resonance Imaging is presented. This method is based on the Fuzzy c-Means (FCM) clustering algorithm. The FCM is applied to each static frame of the cardiac cycle to detect the LV region. Delineation of this region is essential in the quantitative analysis of the cardiac function. The effectiveness of the method is demonstrated by application to sequences of cardiac images.

Automated detection of the left ventricular region in gated nuclear cardiac imaging.

An approach to automated outlining the left ventricular contour and its bounded area in gated isotopic ventriculography is proposed. Its purpose is to determine the ejection fraction (EF), an important parameter for measuring cardiac function. The method uses a modified version of the fuzzy C-means (MFCM) algorithm and a labeling technique. The MFCM algorithm is applied to the end diastolic (ED) frame and then the (FCM) is applied to the remaining images in a "box" of interest. The MFCM generates a number of fuzzy clusters. Each cluster is a substructure of the heart (left ventricle,...). A cluster validity index to estimate the optimum clusters number present in image data point is used. This index takes account of the homogeneity in each cluster and is connected to the geometrical property of data set. The labeling is only performed to achieve the detection process in the ED frame. Since the left ventricle (LV) cluster has the greatest area of the cardiac images sequence in ED phase, a framing operation is performed to obtain, automatically, the "box" enclosing the LV cluster. THe EF assessed in 50 patients by the proposed method and a semi-automatic one, routinely used, are presented. A good correlation between the two methods EF values is obtained (R = 0.93). The LV contour found has been judged very satisfactory by a team of trained clinicians.

Delineation and quantitation of brain lesions by fuzzy clustering in positron emission tomography.

In this study, we investigate the application of the fuzzy clustering to the anatomical localization and quantitation of brain lesions in Positron Emission Tomography (PET) images. The method is based on the Fuzzy C-Means (FCM) algorithm. The algorithm segments the PET image data points into a given number of clusters. Each cluster is an homogeneous region of the brain (e.g. tumor). A feature vector is assigned to a cluster which has the highest membership degree. Having the label affected by the FCM algorithm to a cluster, one may easily compute the corresponding spatial localization, area and perimeter. Studies concerning the evolution of a tumor after different treatments in two patients are presented.

Left ventricle automated detection method in gated isotopic ventriculography using fuzzy clustering.

A method that uses the fuzzy ISODATA clustering algorithm and Fourier analysis is proposed for automated detection of heart left ventricle contours. This operation is used for quantitative analysis of cardiac function. The computation begins by finding the phase image. The fuzzy ISODATA algorithm is first applied to this image to generate a number of clusters that correspond to the organ substructures (ventricles, atria). Second, the ventricles cluster is isolated and the intensities of its points are replaced by the corresponding ones from the original (end diastolic) frame. Finally, a reduced image representing the ventricular region is obtained and an additional clustering is performed to find the left ventricular boundary automatically. This algorithm is tested by application of 105 sets of 16 images each. These results are compared with the measurements obtained with two semi-automatic methods used, respectively, on the Philips and the Sopha Medical gamma cameras.