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Results for “Automated image segmentation”

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PubMed2004

Automated segmentation of human brain MR images using a multi-agent approach.

Image interpretation consists in finding a correspondence between radiometric information and symbolic labeling with respect to specific spatial constraints. It is intrinsically a distributed process in terms of goals to be reached, zones in the image to be processed and methods to be applied. To cope the the difficulty inherent in this process, several information processing steps are required to gradually extract information. In this paper we advocate the use of situated cooperative agents as a framework for managing such steps. Dedicated agent behaviors are dynamically adapted depending on their position in the image, of their topographic relationships and of the radiometric information available. The information collected by the agents is gathered, shared via qualitative maps, or used as soon as available by acquaintances. Incremental refinement of interpretation is obtained through a coarse to fine strategy. Our work is essentially focused on radiometry-based tissue interpretation where knowledge is introduced or extracted at several levels to estimate models for tissue-intensity distribution and to cope with noise, intensity non-uniformity and partial volume effect. Several experiments on phantom and real images were performed. A complete volume can be segmented in less than 5 min with about 0.84% accuracy of the segmented reference. Comparison with other techniques demonstrates the potential interest of our approach for magnetic resonance imaging (MRI) brain scan interpretation.

Artificial Intelligence↗

PubMed2005

Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images.

A crucial task in inflammation research and inflammatory drug validation is leukocyte velocity data collection from microscopic video imagery. Since manual methods are bias-prone and extremely time consuming, automated tracking methods are required to compute cell velocities. However, an automated tracking method is of little practical use unless it is accompanied by a mechanism to validate the tracker output. In this paper, we propose a validation technique that accepts or rejects the output of automated tracking methods. The proposed method first generates a spatiotemporal image from the cell locations given by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte. For segmenting the spatiotemporal images, we employ an edge-direction sensitive nonlinear filter followed by an active contour based technique. The proposed nonlinear filter, the maximum absolute average directional derivative (MAADD), first computes the magnitude of the mean directional derivative over an oriented line segment and then chooses the maximum of all such values within a range of orientations of the line segment. The proposed active contour segmentation is obtained via growing contours controlled by a two-dimensional force field, which is constructed by imposing a Dirichlet boundary condition on the gradient vector flow (GVF) field equations. The performance of the proposed validation method is reported here for the outputs of three different tracking techniques: the method was successful in 97% of the trials using manual tracking, in 94% using correlation tracking and in 93% using active contour tracking.

Algorithms↗

PubMed1990

The Athena semi-automated karyotyping system.

In this article we describe Athena, a system that provides for semi-automated karyotyping of metaphase spreads. The system is based upon the Macintosh II computer. It uses software that is written entirely in C and consists of approximately 200 Kbytes of executable code. Athena provides automated segmentation of metaphase images into individual chromosomes, automated measurements on each banded chromosome, and automated classification into the standard Paris-convention karyotype. Furthermore, the system provides the ability to construct one or more chromosome data bases to represent the types of metaphase spreads and staining techniques that may be used in a given laboratory. Because we believe that it is impossible to construct a system that can achieve perfect segmentation, perfect separation of touching and overlapping chromosomes, perfect localization of the centromeres, and perfect classification, the system offers the possibility for interaction at each of the above stages using the well-accepted Macintosh user interface.

Centromere↗

PubMed2007

Fully automated segmentation and morphometrical analysis of muscle fiber images.

BACKGROUND: Measurement of muscle fiber size and determination of size distribution is important in the assessment of neuromuscular disease. Fiber size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibers in routinely hematoxylin-eosin stained cryostat sections. METHODS: The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as color and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass. The performance of the computed method was compared to results obtained by manual measurement by experts. RESULTS: The correct classification rate of the computed method was high (98%). Segmentation and measurement results obtained manually or automatically did not reveal any significant differences. CONCLUSIONS: The presented region based active contour approach has been proven to accurately segment and measure muscle fibers. Complete automation minimizes user interaction, thus, batch processing, as well as objective and reproducible muscle fiber morphometry are provided.

Algorithms↗

PubMed1999

On standardizing the MR image intensity scale.

The lack of a standard image intensity scale in MRI causes many difficulties in image display and analysis. A two-step postprocessing method is proposed for standardizing the intensity scale in such a way that for the same MR protocol and body region, similar intensities will have similar tissue meaning. In the first step, the parameters of the standardizing transformation are "learned" from a set of images. In the second step, for each MR study these parameters are used to map their histogram into the standardized histogram. The method was tested quantitatively on 90 whole-brain studies of multiple sclerosis patients for several protocols and qualitatively for several other protocols and body regions. Measurements using mean squared difference showed that the standardized image intensities have statistically significantly (P < 0.01) more consistent range and meaning than the originals. Fixed gray level windows can be established for the standardized images and used for display without the need of per case adjustment. Preliminary results also indicate that the method facilitates improving the degree of automation of image segmentation. Magn Reson Med 42:1072-1081, 1999.

Algorithms↗

PubMed1992

Initial results for automated computational modeling of patient-specific electromagnetic hyperthermia.

Developments in finite-difference time-domain (FD-TD) computational modeling of Maxwell's equations, super-computer technology, and computed tomography (CT) imagery open the possibility of accurate numerical simulation of electromagnetic (EM) wave interactions with specific, complex, biological tissue structures. One application of this technology is in the area of treatment planning for EM hyperthermia. In this paper, we report the first highly automated CT image segmentation and interpolation scheme applied to model patient-specific EM hyperthermia. This novel system is based on sophisticated tools from the artificial intelligence, computer vision, and computer graphics disciplines. It permits CT-based patient-specific hyperthermia models to be constructed without tedious manual contouring on digitizing pads or CRT screens. The system permits in principle near real-time assistance in hyperthermia treatment planning. We apply this system to interpret actual patient CT data, reconstructing a 3-D model of the human thigh from a collection of 29 serial CT images at 10 mm intervals. Then, using FD-TD, we obtain 2-D and 3-D models of EM hyperthermia of this thigh due to a waveguide applicator. We find that different results are obtained from the 2-D and 3-D models, and conclude that full 3-D tissue models are required for future clinical usage.

Computer Simulation↗

PubMed2003

Automated 3-D PDM construction from segmented images using deformable models.

In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of three-dimensional PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using computed tomography data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.

Algorithms↗

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↗

PubMed2005

[Study of automated segmentation of the cerebellum and brainstem on brain MR images].

MR imaging is an important method for diagnosing abnormalities of the brain. This paper presents an automated method to segment the cerebellum and brainstem for brain MR images. MR images were obtained from 10 normal subjects (male 4, female 6; 22-75 years old, average 31.0 years) and 15 patients with brain atrophy (male 3, female 12; 62-85 years of age, average 76.0 years). The automated method consisted of the following four steps: (1) segmentation of the brain on original images, (2) detection of an upper plane of the cerebellum using the Hough transform, (3) correction of the plane using three-dimensional (3D) information, and (4) segmentation of the cerebellum and brainstem using the plane. The results indicated that the regions obtained by the automated method were visually similar to those obtained by a manual method. The average rates of coincidence between the automated method and manual method were 83.0+/-9.0% in normal subjects and 86.4+/-3.6% in patients.

Adult↗

PubMed2004

Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation.

PURPOSE: To assess the effectiveness of two automated magnetic resonance imaging (MRI) segmentation methods in determining the gross tumor volume (GTV) of brain tumors for use in radiation therapy treatment planning. METHODS AND MATERIALS: Two automated MRI tumor segmentation methods (supervised k-nearest neighbors [kNN] and automatic knowledge-guided [KG]) were evaluated for their potential as "cyber colleagues." This required an initial determination of the accuracy and variability of radiation oncologists engaged in the manual definition of the GTV in MRI registered with computed tomography images for 11 glioma patients. Three sets of contours were defined for each of these patients by three radiation oncologists. These outlines were compared directly to establish inter- and intraoperator variability among the radiation oncologists. A novel, probabilistic measurement of accuracy was introduced to compare the level of agreement among the automated MRI segmentations. The accuracy was determined by comparing the volumes obtained by the automated segmentation methods with the weighted average volumes prepared by the radiation oncologists. RESULTS: Intra- and inter-operator variability in outlining was found to be an average of 20% +/- 15% and 28% +/- 12%, respectively. Lowest intraoperator variability was found for the physician who spent the most time producing the contours. The average accuracy of the kNN segmentation method was 56% +/- 6% for all 11 cases, whereas that of the KG method was 52% +/- 7% for 7 of the 11 cases when compared with the physician contours. For the areas of the contours where the oncologists were in substantial agreement (i.e., the center of the tumor volume), the accuracy of kNN and KG was 75% and 72%, respectively. The automated segmentation methods were found to be least accurate in outlining at the edges of the tumor volume. CONCLUSIONS: The kNN method was able to segment all cases, whereas the KG method was limited to enhancing tumors and gliomas with clear enhancing edges and no cystic formation. Both methods undersegment the tumor volume when compared with the radiation oncologists and performed within the variability of the contouring performed by experienced radiation oncologists based on the same data.

Aged↗

PubMed2000

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.

Algorithms↗

PubMed1995

A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images.

A completely automated algorithm is presented which is capable of identifying both the right- and left-lung fields on digitized chest radiographic images. The algorithm is tested on a sample of 802 chest images against lung fields drawn by a human observer. The average accuracies are found to be 0.957 +/- 0.003 and 0.960 +/- 0.003 for right- and left-lung regions, respectively. To put them into perspective, the results are compared to several other simple segmentation techniques. These include a comparison of two sets of lung fields drawn by the human observer at different times which yielded accuracies of 0.967 +/- 0.005 and 0.967 +/- 0.004 for right- and left-lung regions, respectively.

Algorithms↗

PubMed1995

Image segmentation of cribriform gland tissue.

OBJECTIVE: To develop procedures for the segmentation of cribriform prostatic glands. STUDY DESIGN: A knowledge-guided procedure following a model-based reasoning process was developed in the context of a set of interacting expert systems for machine vision in histometry. RESULTS: With 78 entities in the knowledge file, fully automated, correct segmentation of approximately 70-80% of cribriform glands was attained--i.e., outlining of histologic components agreed with visual assessment. Measurement of gland size, shape, lumen area, number of lumina per gland, epithelial layer thickness, degree of cribriformity and determination of completeness of lining of a gland by a basal cell layer could be taken from the correctly segmented images. CONCLUSION: The automated procedure allows a histometric characterization of premalignant and malignant prostatic lesions. Extension of system capabilities to the utilization of spectral information is expected to allow an increase in the correct segmentation rate.

Humans↗

PubMed2001

Objective threshold selection procedure (OTS) for segmentation of scanning laser confocal microscope images.

The determination of volumes and interface areas from confocal laser scanning microscopy (CLSM) images requires the identification of component objects by segmentation. An automated method for the determination of segmentation thresholds for CLSM imaging of biofilms was developed. The procedure, named objective threshold selection (OTS), is a three-dimensional development of the approach introduced by the popular robust automatic threshold selection (RATS) method. OTS is based on the statistical properties of local gray-values and gradients in the image. By characterizing the dependence between a volumetric feature and the intensity threshold used for image segmentation, the former can be determined with an arbitrary confidence level, with no need for user intervention. The identification of an objective segmentation procedure renders the possibility for the full automation of volume and interfacial area measurement. Images from two distinct biofilm systems, acquired using different experimental techniques and instrumental setups were segmented by OTS to determine biofilm volume and interfacial area. The reliability of measurements for each case was analyzed to identify optimal procedure for image acquisition. The automated OTS method was shown to reproduce values obtained manually by an experienced operator.

Automation↗

PubMed2006

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

Algorithms↗

PubMed2006

Evaluation of crystalline objects in crystallizing protein droplets based on line-segment information in greyscale images.

Several automated crystallization systems have recently been developed for high-throughput X-ray structure analysis. However, the evaluation process for the growth state of crystallizing protein droplets has not yet been completely automated. This paper proposes a new evaluation method for crystalline objects in automated crystallization experiments. The main objective is to determine whether a droplet contains crystals suitable for diffraction experiments and analysis. The evaluation method developed here involves extracting line-segment features from an image of the droplet and discriminating the state of crystallization using classifiers based on line features. In order to verify the efficacy of the proposed method, it was used to classify images obtained by an automated crystallization system.

Algorithms↗

PubMed2004

Automated detection and volume measurement of plexiform neurofibromas in neurofibromatosis 1 using magnetic resonance imaging.

An automated technique for segmentation and volumetric measurement of plexiform neurofibromas (PN) in neurofibromatosis 1 using short T1-inversion recovery magnetic resonance images is presented. The algorithm described implements heuristics derived from human-based recognition of lesions. This technique combines region-based with boundary-based segmentation. Two observers, who performed semi-automated volume calculations and manual tracings to estimate tumor volume, validated this method on 9 PNs of different size and location. This automated method was reproducible (coefficient of variation 0.6-5.6%), yielded similar results to manual tumor tracings (R = 0.999) and will likely improve the ability to measure PNs in ongoing clinical trials.

Algorithms↗

PubMed2005

Semi-automated basal ganglia segmentation using large deformation diffeomorphic metric mapping.

This paper investigates the techniques required to produce accurate and reliable segmentations via grayscale image matching. Finding a large deformation, dense, non-rigid transformation from a template image to a target image allows us to map a template segmentation to the target image space, and therefore compute the target image segmentation and labeling. We outline a semi-automated procedure involving landmark and image intensity-based matching via the large deformation diffeomorphic mapping metric (LDDMM) algorithm. Our method is applied specifically to the segmentation of the caudate nucleus in pre- and post-symptomatic Huntington's Disease (HD) patients. Our accuracy is compared against gold-standard manual segmentations and various automated segmentation tools through the use of several error metrics.

Algorithms↗