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Results for “Automated image segmentation”

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At least 289 records · Page 16Linked to original sources

PubMed2006

Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction.

This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity.

Algorithms↗

PubMed1999

Automatic vessel segmentation using active contours in cine phase contrast flow measurements.

The segmentation of images obtained by cine magnetic resonance (MR) phase contrast velocity mapping using manual or semi-automated methods is a time consuming and observer-dependent process that still hampers the use of flow quantification in a clinical setting. A fully automatic segmentation method based on active contour model algorithms for defining vessel boundaries has been developed. For segmentation, the phase image, in addition to the magnitude image, is used to address image distortions frequently seen in the magnitude image of disturbed flow fields. A modified definition for the active contour model is introduced to reduce the influence of missing or spurious edge information of the vessel wall. The method was evaluated on flow phantom data and on in vivo images acquired in the ascending aorta of humans. Phantom experiments resulted in an error of 0.8% in assessing the luminal area of a flow phantom equipped with an artificial heart valve. Blinded evaluation of the volume flow rates from automatic vs. manual segmentation of gradient echo (FFE) phase contrast images obtained in vivo resulted in a mean difference of -0.9 +/- 3%. The mean difference from automatic vs. manual segmentation of images acquired with a hybrid phase contrast sequence (TFEPI) within a single breath-hold was -0.9 +/- 6%.

Adult↗

PubMed2000

Magnetic resonance imaging deformation-based segmentation of the hippocampus in patients with mesial temporal sclerosis and temporal lobe epilepsy.

We compared manual and automated segmentations of the hippocampus in patients with mesial temporal sclerosis. This comparison showed good precision of the deformation-based automated segmentations.

Brain Mapping↗

PubMed2002

Automated 3-D echocardiography analysis compared with manual delineations and SPECT MUGA.

A major barrier for using 3-D echocardiography for quantitative analysis of heart function in routine clinical practice is the absence of accurate and robust segmentation and tracking methods necessary to make the analysis automatic. In this paper, we present an automated three-dimensional (3-D) echocardiographic acquisition and image-processing methodology for assessment of left ventricular (LV) function. We combine global image information provided by a novel multiscale fuzzy-clustering segmentation algorithm, with local boundaries obtained with phase-based acoustic feature detection. We then use the segmentation results to fit and track the LV endocardial surface using a 3-D continuous transformation. To our knowledge, this is the first report of a completely automated method. The protocol is evaluated in a small clinical case study (nine patients). We compare ejection fractions (EFs) computed with the new approach to those obtained using the standard clinical technique, single-photon emission computed tomography multigated acquisition. Errors on six datasets were found to be within six percentage points. A further two, with poor image quality, improved upon EFs from manually delineated contours, and the last failed due to artifacts in the data. Volume-time curves were derived and the results compared to those from manual segmentation. Improvement over an earlier published version of the method is noted.

Algorithms↗

PubMed2006

Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images.

Typical packages used for coregistration in functional image analyses include automated image registration (AIR) and statistical parametric mapping (SPM). However, both methods have limited-dimension deformation models. A fully deformable model, which combines the piecewise linear registration for coarse alignment with demons algorithm for voxel-level refinement, allows a higher degree of spatial deformation. This leads to a more accurate colocalization of the functional signal from different subjects and therefore can produce a more reliable group average signal. We quantitatively compared the performance of the three different registration approaches through a series of experiments and we found that the fully deformable model consistently produces a more accurate structural segmentation and a more reliable functional signal colocalization than does AIR or SPM.

Adult↗

PubMed1999

Artificial neural networks and spatial temporal contour linking for automated endocardial contour detection on echocardiograms: a novel approach to determine left ventricular contractile function.

This study investigated the use of artificial neural networks (ANN) for image segmentation and spatial temporal contour linking for the detection of endocardial contours on echocardiographic images. Using a backpropagation network, the system was trained with 279 sample regions obtained from eight training images to segment images into either tissue or blood pool region. The ANN system was then applied to parasternal short axis images of 38 patients. Spatial temporal contour linking was performed on the segmented images to extract endocardial boarders. Left ventricular areas (end-systolic and end-diastolic) determined with the automated system were calculated and compared to results obtained by manual contour tracing performed by two independent investigators. In addition, ejection fractions (EF) were derived using the area-length method and compared with radionuclide ventriculography. Image quality was classified as good in 12 (32%), moderate in 13 (34%) and poor in 13 (34%) patients. The ANN system provided estimates of end-diastolic and end-systolic areas in 36 (89%) of echocardiograms, which correlated well with those obtained by manual tracing (R = 0.99, SEE = 1.44). A good agreement was also found for the comparison of EF between the ANN system and Tc-radionuclide ventriculography (RNV, R = 0.93, SEE = 6.36). The ANN system also performed well in the subset of patients with poor image quality. Endocardial contour detection using artificial neural networks and spatial temporal contour linking allows accurate calculations of ventricular areas from transthoracic echocardiograms and performs well even in images with poor quality. This system could greatly enhance the feasibility, accuracy and reproducibility of calculating cardiac areas to derive left ventricular volumes and ejection fractions.

Adult↗

PubMed2006

Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer's disease.

We describe a new algorithm for the automated segmentation of the hippocampus (Hc) and the amygdala (Am) in clinical Magnetic Resonance Imaging (MRI) scans. Based on homotopically deforming regions, our iterative approach allows the simultaneous extraction of both structures, by means of dual competitive growth. One of the most original features of our approach is the deformation constraint based on prior knowledge of anatomical features that are automatically retrieved from the MRI data. The only manual intervention consists of the definition of a bounding box and positioning of two seeds; total execution time for the two structures is between 5 and 7 min including initialisation. The method is evaluated on 16 young healthy subjects and 8 patients with Alzheimer's disease (AD) for whom the atrophy ranged from limited to severe. Three aspects of the performances are characterised for validating the method: accuracy (automated vs. manual segmentations), reproducibility of the automated segmentation and reproducibility of the manual segmentation. For 16 young healthy subjects, accuracy is characterised by mean relative volume error/overlap/maximal boundary distance of 7%/84%/4.5 mm for Hc and 12%/81%/3.9 mm for Am; for 8 Alzheimer's disease patients, it is 9%/84%/6.5 mm for Hc and 15%/76%/4.5 mm for Am. We conclude that the performance of this new approach in data from healthy and diseased subjects in terms of segmentation quality, reproducibility and time efficiency compares favourably with that of previously published manual and automated segmentation methods. The proposed approach provides a new framework for further developments in quantitative analyses of the pathological hippocampus and amygdala in MRI scans.

Adult↗

PubMed2006

Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.

Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirements make this approach impractical. To achieve automation, an individual segmentation can be propagated to another individual using an anatomical correspondence estimate relating the atlas image to the target image. The accuracy of the resulting target labeling has been limited but can potentially be improved by combining multiple segmentations using decision fusion. We studied segmentation propagation and decision fusion on 30 normal brain MR images, which had been manually segmented into 67 structures. Correspondence estimates were established by nonrigid registration using free-form deformations. Both direct label propagation and an indirect approach were tested. Individual propagations showed an average similarity index (SI) of 0.754+/-0.016 against manual segmentations. Decision fusion using 29 input segmentations increased SI to 0.836+/-0.009. For indirect propagation of a single source via 27 intermediate images, SI was 0.779+/-0.013. We also studied the effect of the decision fusion procedure using a numerical simulation with synthetic input data. The results helped to formulate a model that predicts the quality improvement of fused brain segmentations based on the number of individual propagated segmentations combined. We demonstrate a practicable procedure that exceeds the accuracy of previous automatic methods and can compete with manual delineations.

Adult↗

PubMed2001

Automated separation of gray and white matter from MR images of the human brain.

A simple automatic procedure for segmentation of gray and white matter in high resolution 1.5T T1-weighted MR human brain images was developed and validated. The algorithm is based on histogram shape analysis of MR images that were corrected for scanner nonuniformity. Calibration and validation was done on a set of 80 MR images of human brains. The automatic method's values for the gray and white matter volumes were compared with the values from thresholds set twice by the best three of six raters. The automatic procedure was shown to perform as good as the best rater, where the average result of the best three raters was taken as reference. The method was also compared with two other histogram-based threshold methods, which yielded comparable results. The conclusion of the study thus is that automated threshold based methods can separate gray and white matter from MR brain images as reliably as human raters using a thresholding procedure.

Adult↗

PubMed1997

Conjugate imagery in the automated reproduction of three dimensional coordinates from two dimensional coordinate data.

The three-dimensional video analysis of human motion commonly utilises automated image processing and digitisation processes to produce real-time unidentified two-dimensional coordinate data of segmental markers. In what can be a time-consuming process the two-dimensional data are then identified and tracked to produce three-dimensional coordinates. This paper presents an approach to the automated reproduction of three-dimensional coordinates from two-dimensional coordinates data. Conjugate imaging techniques were utilised in the development of four criterion measures for determining the validity of conjugate (corresponding) image points. An algorithm based on the criterion measures was then developed for the automated reproduction of three dimensional coordinates from camera image coordinate data. The algorithm was tested with a 55 point marker system viewed in four video cameras (digitisation error approx. 0.2%, lab point separation > or = 6 cm). The success of the algorithm was dependent on the closeness of markers, the accuracy of the photogrammetric system, and the number of markers visible in two camera images. The present research has developed techniques based on conjugate imagery for the automated reproduction of three-dimensional coordinates from two-dimensional data, and provided a bases for further development of automated three-dimensional tracking.

Algorithms↗

PubMedDate not supplied

Three-dimensional reconstruction of entire vertebral bodies.

This paper describes a technique for 3-D reconstruction of large cancellous bone regions. The output is a 3-D array describing the original cancellous bone region, and the output can be used for any kind of measurement of the bone architecture. The technique was developed as a tool for researchers conducting experimental and clinical studies related to cancellous bone architecture and, ultimately, to cancellous bone quality. A set of new and unbiased methods for quantification of cancellous bone has been a stimulus for the development of the technique, as the quantification methods rely on 3-D information. The technique is based on automated serial sectioning, and all steps from specimen preparation to image segmentation are described in detail. Examples of 3-D reconstructed vertebral bodies are given. By use of the described technique, between 300 and 600 sections can be made and registered per hour, which means that an average vertebral body can be reconstructed in about 2 h. Compared to previous implementations of the general principle of serial sectioning, this is a significant improvement both in resolution and in time efficiency.

Aged↗

PubMedDate not supplied

Measurement and classification 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. Finally, we discuss how the accuracy of these measures can be influence by the method used to extract the blood vessel segments.

Diagnosis, Computer-Assisted↗

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.

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↗

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↗