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Results for “Automated image segmentation”

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PubMed1997

Segmental analysis of color kinesis images: new method for quantification of the magnitude and timing of endocardial motion during left ventricular systole and diastole.

BACKGROUND: We describe a method for objective assessment of left ventricular (LV) endocardial wall motion based on Color Kinesis, a new echocardiographic technique that color-encodes pixel transitions between blood and myocardial tissue. METHODS AND RESULTS: We developed a software that analyzes Color Kinesis images and provides quantitative indices of magnitude and timing of regional endocardial motion. Images obtained in 12 normal subjects were used to evaluate the variability in each index. Esmolol, dobutamine, and atropine were used to track variations in LV function in 14 subjects. Objective evaluation of wall motion was tested in 20 patients undergoing dobutamine stress testing. Regional fractional area change, displacement, and radial shortening were displayed as histograms and time curves. Global function was assessed by calculating magnitude and timing of peak ejection or filling rates and mean time of ejection or filling. Patterns of endocardial motion were consistent between normal subjects. Fractional area change and peak ejection rate decreased with esmolol and increased with dobutamine. Time to peak ejection and mean time of contraction were prolonged with esmolol and shortened with dobutamine. Using atropine, we proved that our findings with dobutamine were not secondary to its chronotropic effects. Dobutamine induced regional wall motion abnormalities in 10 patients in 38 segments diagnosed conventionally. Segmental analysis detected abnormalities in 36 of these 38 segments and in an additional 5 of 322 segments. CONCLUSIONS: Analysis of Color Kinesis images allows fast, objective, and automated evaluation of regional wall motion sensitively enough to evaluate clinical dobutamine stress data. This method has significant potential in the diagnosis of myocardial ischemia.

Adrenergic beta-Agonists↗

PubMed1997

Automated measurement of retinal vascular tortuosity.

Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classification tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classification rate of 91% for the first problem and 95% on the second problem, which confirms that they capture much of the ophthalmologists' notion of tortuosity.

Fundus Oculi↗

PubMed2006

Stone detection in MRCP images using controlled region growing.

Stones in the biliary tract are routinely identified using MRCP (magnetic resonance cholangiopancreatography). The noisy nature of the images, as well as varying intensity, size and location of the stones, defeat most automatic detection algorithms, making computer-aided diagnosis difficult. This paper proposes a multi-stage segment-based scheme for semi-automated detection of choledocholithiasis and cholelithiasis in the MRCP images, producing good performance in tests, differentiating them from "normal" MRCP images. With the high success rate of over 90%, refinement of the scheme could be applicable in the clinical environment as a tool in aiding diagnosis, with possible applications in telemedicine.

Algorithms↗

PubMed1990

Combined morphometrical and syntactic structure analysis as tools for histomorphological insight into human lung carcinoma growth.

Histological sections of formalin-fixed, paraffin-embedded tissue comprising 60 surgical specimens of human lung carcinoma were Feulgen stained. The histomorphological images were transferred to an automated image analysing system (VISIAC) and analysed as follows. The geometrical centers of tumor cell nuclei were defined as vertices, and the minimum spanning tree (MST) was calculated based on the two-dimensional distance between the vertices. Segmentation of the images was performed semiautomatically by interactive definition of nuclei of interest and automated detection of nuclear boundaries. Several morphometric features of tumor cell nuclei were measured including size, DNA-content (extinction), and form factor, and were set in relation to parameters of the MST. The following results were obtained: DNA-content and tumor cell nucleus size ('center cell') of different microscopic tumor growth patterns are related to the number of nearest neighboring cells. No relation was found in the neighboring (surrounding) cells. The different cell types of lung carcinoma, i.e., the different microscopic tumor textures expressed the relation of center cell features to the parameters of MST. A high amount of DNA content in branching points of the MST for epidermoid carcinoma may be interpreted as carcinoma growing in epidermoid textures tend to proliferate from tumor cell nuclei related to at least one neighboring cell. The opposite was found for large cell anaplastic carcinoma (no perceptible microscopic textures of the tumors) which showed the highest DNA content in tumor cell nuclei but which was not related to any neighboring cells. This technique allows analysis of growth centers and microenvironment conditions in human lung cancer in relation to tumor texture at the light microscopy level.

Adenocarcinoma↗

PubMed1991

Morphometry of human nerve biopsies by means of automated cytometry: assessment with reference to ultrastructural analysis.

The morphometric analysis of myelinated fibres is a tool for diagnosis of neuropathies and for assessing nerve regeneration after reconstructive surgery. In order to obtain reliable information, sampling techniques should be avoided and the measurement of all the fibres within a nerve fascicle requiring the use of automated cytometry is necessary. We have developed a programme on the SAMBA cytological image analyser for the automated measurement of myelinated fibres. Different techniques of segmentation were tested and adaptive grey level thresholding gave the more reliable result. Accuracy and reproducibility were tested using a set of five human superficial peroneal nerve biopsies that were previously analysed by means of semi-automatic ultrastructural morphometry. A maximum difference of 9% in the number of fibres counted was obtained when comparing the automatic and the semi-automatic methods. In all cases the histograms of the morphometrical variables (fibre and axon diameters, myelin sheath thickness) were found to be identical to the reference histograms obtained by the semi-automatic method.

Evaluation Studies as Topic↗

PubMed1993

A computerized system for morphometric analysis of digitized images of histologically prepared arterial cross sections.

A method is described for computerized, operator-assisted, morphometric measurement of histologically prepared arterial cross sections. The system is composed of an image cytometer equipped with a one power lens and a digitizing camera interfaced with an imaging board residing in a PC computer. Algorithms were developed for automated segmentation of the areas of interest. The method is rapid, objective, accurate and requires minimum operator intervention. It gives reliable and reproducible results in the measurement of all elements of the cross section including circumference, area of the media, circumference of the internal elastic lamina, area of the lumen and percent occlusion of the lumen by the intimal thickening. While manual measurements using computerized planimetric methods give similar results, this method is an order of magnitude faster (1-2 min vs. 10-20 min per cross section) thus facilitating the study of large numbers of specimens. A blinded re-measurement of 284 sections of artery for reproducibility yielded r values of 0.86-0.97.

Adult↗

PubMed2006

Intensity inhomogeneity correction of multispectral MR images.

Intensity inhomogeneity in MR images is an undesired phenomenon, which often hampers different steps of quantitative image analysis such as segmentation or registration. In this paper, we propose a novel fully automated method for retrospective correction of intensity inhomogeneity. The basic assumption is that inhomogeneity correction could be improved by integrating spatial and intensity information from multiple MR channels, i.e., T1, T2, and PD weighted images. Intensity inhomogeneities of such multispectral images are removed simultaneously in a four-step iterative procedure. First, the probability distribution of image intensities and corresponding spatial features is calculated. In the second step, intensity correction forces that tend to minimize joint entropy of multispectral image are estimated for all image voxels. Third, independent inhomogeneity correction fields are obtained for each channel by regularization and normalization of voxel forces, and last, corresponding partial inhomogeneity corrections are performed separately for each channel. The method was quantitatively evaluated on simulated and real MR brain images and compared to three other methods.

Brain↗

PubMed2006

Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences.

Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypic emotional expressions, such as anger and happiness. Instead of representing another approach to machine analysis of prototypic facial expressions of emotion, the method presented in this paper attempts to handle a large range of human facial behavior by recognizing facial muscle actions that produce expressions. Virtually all of the existing vision systems for facial muscle action detection deal only with frontal-view face images and cannot handle temporal dynamics of facial actions. In this paper, we present a system for automatic recognition of facial action units (AUs) and their temporal models from long, profile-view face image sequences. We exploit particle filtering to track 15 facial points in an input face-profile sequence, and we introduce facial-action-dynamics recognition from continuous video input using temporal rules. The algorithm performs both automatic segmentation of an input video into facial expressions pictured and recognition of temporal segments (i.e., onset, apex, offset) of 27 AUs occurring alone or in a combination in the input face-profile video. A recognition rate of 87% is achieved.

Algorithms↗

PubMed2006

Improved discrimination of breast lesions using selective sampling of segmented MR images.

OBJECTIVE: The aim of this work is to examine if the specificity of differentiation between malignant and benign tumours can be improved by retrospectively examining lesion-extracted distributions. A semi-automated method for selecting a region-of-interest (ROI) is described. A new histogram segmentation approach for sampling pharmacokinetic breast maps of transfer uptake is defined in order to assign classification variables for the lesion. METHOD: Fifty exchange rate parameter maps were extracted from 49 subjects and retrospectively analysed. Distributions obtained from semi-automatically delineated ROIs were subdivided into ten overlapping segments. Parameters were extracted from each segment which effectively presents a new pixel intensity sampling strategy. Mann-Whitney non-parametric tests and ROC curves were generated. RESULTS: Correlation exists between mean parameter values drawn from semi-automatically or manually drawn ROIs. However, the former yield higher specificity values as applied to this subset of enhancing benign lesions. Segmenting the exchange rate parameter histogram allows the identification of which part of the distribution correlates most with tumour type. Significant improvement in specificity is obtained when using half the pixels within the ROI. CONCLUSION: Improved specificity values are obtained by a new method of selecting the differentiation parameters which relies on intensity rather than spatial segmentation. Only half the pixels available within the ROI contributed to the measured classification parameters.

Adult↗

PubMed1999

A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images.

Methods for brain tissue classification or segmentation of structural magnetic resonance imaging (MRI) data should ideally be independent of human operators for reasons of reliability and tractability. An algorithm is described for fully automated segmentation of dual echo, fast spin-echo MRI data. The method is used to assign fuzzy-membership values for each of four tissue classes (gray matter, white matter, cerebrospinal fluid and dura) to each voxel based on partition of a two dimensional feature space. Fuzzy clustering is modified for this application in two ways. First, a two component normal mixture model is initially fitted to the thresholded feature space to identify exemplary gray and white matter voxels. These exemplary data protect subsequently estimated cluster means against the tendency of unmodified fuzzy clustering to equalize the number of voxels in each class. Second, fuzzy clustering is implemented in a moving window scheme that accommodates reduced image contrast at the axial extremes of the transmitting/receiving coil. MRI data acquired from 5 normal volunteers were used to identify stable values for three arbitrary parameters of the algorithm: feature space threshold, relative weight of exemplary gray and white matter voxels, and moving window size. The modified algorithm incorporating these parameter values was then used to classify data from simulated images of the brain, validating the use of fuzzy-membership values as estimates of partial volume. Gray:white matter ratios were estimated from 20 twenty normal volunteers (mean age 32.8 years). Processing time for each three-dimensional image was approximately 30 min on a 170 MHz workstation. Mean cerebral gray and white matter volumes estimated from these automatically segmented images were very similar to comparable results previously obtained by operator dependent methods, but without their inherent unreliability.

Adult↗

PubMedDate not supplied

An image-processing system for qualitative and quantitative volumetric analysis of brain images.

In this work, we developed, implemented, and validated an image-processing system for qualitative and quantitative volumetric analysis of brain images. This system allows the visualization and quantitation of global and regional brain volumes. Global volumes were obtained via an automated adaptive Bayesian segmentation technique that labels the brain into white matter, gray matter, and cerebrospinal fluid. Absolute volumetric errors for these compartments ranged between 1 and 3% as indicated by phantom studies. Quantitation of regional brain volumes was performed through normalization and tessellation of segmented brain images into the Talairach space with a 3D elastic warping model. Retest reliability of regional volumes measured in Talairach space indicated errors of < 1.5% for the frontal, parietal, temporal, and occipital brain regions. Additional regional analysis was performed with an automated hybrid method combining a region-of-interest approach and voxel-based analysis, named Regional Analysis of Volumes Examined in Normalized Space (RAVENS). RAVENS analysis for several subcortical structures showed good agreement with operator-defined volumes. This system has sufficient accuracy for longitudinal imaging data and is currently being used in the analysis of neuroimaging data of the Baltimore Longitudinal Study of Aging.

Aged↗

PubMed2005

Automatic initialization algorithm for carotid artery segmentation in CTA images.

Analysis of CT datasets is commonly time consuming because of the required manual interaction. We present a novel and fast automatic initialization algorithm to detect the carotid arteries providing a fully automated approach of the segmentation and centerline detection. First, the volume of interest (VOI) is estimated using a shoulder landmark. The carotid arteries are subsequently detected in axial slices of the VOI by applying a circular Hough transform. To select carotid arteries related signals in the Hough space, a 3-D, direction dependent hierarchical clustering is used. To allow a successful detection for a wide range of vessel diameters, a feedback architecture was introduced. The algorithm was designed and optimized using a training set of 20 patients and subsequently evaluated using 31 test datasets. The detection algorithm, including VOI estimation, correctly detects 88% of the carotid arteries. Even though not all carotid arteries have been correctly detected, the results are very promising.

Algorithms↗

PubMed2004

Sensitivity and reproducibility of a new fast 3D segmentation technique for clinical MR-based brain volumetry in multiple sclerosis.

Fast, reliable and easy-to-use methods to quantify brain atrophy are of increasing importance in clinical studies on neuro-degenerative diseases. Here, ILAB 4, a new volumetry software that uses a fast semi-automated 3D segmentation of thin-slice T1-weighted 3D MR images based on a modified watershed transform and an automatic histogram analysis was evaluated. It provides the cerebral volumes: whole brain, white matter, gray matter and intracranial cavity. Inter- and intra-rater reliability and scan-rescan reproducibility were excellent in measuring whole brain volumes (coefficients of variation below 0.5%) of volunteers and patients. However, gray and white matter volumes were more susceptible to image quality. High accuracy of the absolute volume results (+/-5 ml) were shown by phantom and preparation measurements. Analysis times were 6 min for processing of 128 slices. The proposed technique is reliable and highly suitable for quantitative studies of brain atrophy, e.g., in multiple sclerosis.

Adult↗

PubMed1998

Automatic segmentation of gadolinium-enhanced multiple sclerosis lesions.

Automatic detection and quantitation of contrast-enhanced lesions on MRI is expected to be useful in characterizing the disease state in multiple sclerosis (MS). The enhancing structures such as cerebral vasculature and regions with no blood-brain barrier complicate automated analysis of lesion enhancement. A pulse sequence that incorporates both stationary and marching saturation bands and gradient dephasing is described for suppressing the enhancements within the cerebral vasculature. A postprocessing technique based on automatic image segmentation is implemented for eliminating enhancing structures such as choroid plexus. All MS lesions larger than 5 mm3 have been successfully identified. The automated analysis produced no false-positives or false-negative lesions above this volume in 13 patients with MS who were evaluated using the acquisition and evaluation techniques described.

Algorithms↗

PubMed2004

Computerized characterization of breast masses on three-dimensional ultrasound volumes.

We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

Algorithms↗

PubMed1997

Absence of co-localized glutamic acid decarboxylase and neuropeptides in noradrenergic axons of the rat spinal cord.

Patterns of co-localization of immunoreactivity for dopamine beta-hydroxylase (the synthetic enzyme for noradrenaline) and glutamic acid decarboxylase (the synthetic enzyme for GABA) or each one of six neuropeptides (neuropeptide Y, substance P, met-enkephalin, galanin, dynorphin A and somatostatin) were investigated with dual-colour confocal laser scanning microscopy in axons of cervical, thoracic and lumbar spinal segments of six adult rats. Four regions of the grey matter were studied (laminae I-II, V, IX and X) and, in thoracic segments, the intermediolateral cell column was also examined. The extent of co-localization was estimated by direct assessment of merged pairs of optical sections and by automated image analysis. Significant co-localization was found for neuropeptide Y in axons of the intermediolateral cell column of thoracic segments and in lamina X of cervical and thoracic segments. None of the other peptides or glutamic acid decarboxylase were found to coexist at significant levels with dopamine beta-hydroxylase and hence it is likely that this group of neuropeptides and GABA are not co-transmitters of bulbospinal noradrenergic axons in the rat.

Animals↗

PubMedDate not supplied

Weekly alcohol consumption, brain atrophy, and white matter hyperintensities in a community-based sample aged 60 to 64 years.

OBJECTIVE: The objective of this study was to determine the association between weekly alcohol consumption and brain atrophy in adults aged 60 to 64 years. METHODS: Brain magnetic resonance imaging scans from 385 adults recruited through a community survey were analyzed. Automated segmentation and manual tracing methods were used to obtain brain subvolumes and automated methods were used to obtain quantification and localization of white matter hyperintensities. Visual measures of cortical atrophy were obtained as were data on health and lifestyle factors. Alcohol consumption was assessed with the Alcohol Use Disorders Identification Test. RESULTS: In men, weekly alcohol consumption had a positive linear association with ventricular volume and gray matter and a negative linear association with white matter. In women, weekly alcohol consumption had a nonlinear relationship with cerebrospinal fluid and white matter. Alcohol consumption was not associated with white matter hyperintensities, corpus callosum size, hippocampal or amygdala volumes in analyses adjusting for confounding variables. CONCLUSION: An association between alcohol consumption and brain atrophy is evident at the population level. In women, detrimental effects of alcohol on the brain appear to occur at lower levels of consumption. It remains possible that low levels of alcohol consumption have neuroprotective benefits but is clear that high levels of consumption are detrimental.

Alcohol Drinking↗

PubMed1993

CT and SPECT image registration and fusion for spatial localization of metastatic processes using radiolabeled monoclonals.

The fusion of computed tomography (CT) and single-photon emission computerized tomography (SPECT) antibody images can enhance the information provided by either single modality by providing precise anatomical-functional correlation. Functional abnormalities seen on low resolution SPECT antibody images can be precisely located with specific anatomic structures seen in high resolution CT images. External fiducials located on each image modality aid in the automated registration, alignment and matching of CT and SPECT antibody images. The potential benefits of multimodal fusion include (A) the discrimination of more subtle activity peaks using anatomic organ segmentation, (B) temporal discrimination of recurrent disease, (C) assessment of residual activity post-surgery and (D) automated localization of significant focal activity. In addition, the correlation of function with anatomy may be used to establish the physiologic status of ambiguously identified objects in the anatomic image.

Aged↗