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Results for “Automated image segmentation”

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PubMed2002

Image processing and 3D reconstruction of serial section micrographs from Musca Domestica's biological cells responsible for visual processing.

The ability to visualize and understand three-dimensional objects from two-dimensional cross-section or slice images is difficult, even if the observer has a general concept of the object of interest. The focus of this research is to apply image-processing methods to two-dimensional cross-section electron transmission micrographs of the biological cells of the Musca Domestica's, or household fly's visual system in an effort to better understand the cells responsible for processing visual information. The application of knowledge gained from biological systems is know as biomimetics. The first task will be to construct a useful three-dimensional data set from two-dimensional micrographs provided by the U.S. Air Force Academy in Colorado Springs, Colorado. The data set will be constructed by aligning these images in an edge-to-edge fashion to form a layer. Once each layer is reconstructed, the layers will be stacked and registered to form the third dimension of the data set. This task is complicated by the fact that translation, rotation and scaling mismatches exist in the images. The second task will be to segment and label the biological cells of interest. Computerized segmentation has not yet proved successful, resulting in a manual or "brain-powered" approaches being used at many institutions. By using and modifying current computer image-processing techniques, advances leading to a semi-automated segmentation process may result. Finally, the segmented data must be formatted for use with existing software to render and view the cell(s) of interest. A "marching cubes" surface-rendering algorithm is often implemented in current visualization software, along with routines to view, rotate and scale the resulting surfaces in real time. The result of viewing and manipulating the biological data set will be an increased understanding of the processes of the fly's visual system. Other researchers will use the knowledge gained from the three-dimensional renderings of the cells to further develop an analog vision system based on the fly's compound eye. Much of this research is funded by the Navy Air Warfare Center in an effort to design an analog visual system with real-time target identification and tracking capabilities.

Animals↗

PubMed2003

Automated lung segmentation in X-ray computed tomography: development and evaluation of a heuristic threshold-based scheme.

RATIONALE AND OBJECTIVES: To develop and evaluate a reliable, fully-automated lung segmentation scheme for application in X-ray computed tomography. MATERIALS AND METHODS: The automated scheme was heuristically developed using a slice-based, pixel-value threshold and two sets of classification rules. Features used in the rules include size, circularity, and location. The segmentation scheme operates slice-by-slice and performs three key operations: (1) image preprocessing to remove background pixels, (2) computation and application of a pixel-value threshold to identify lung tissue, and (3) refinement of the initial segmented regions to prune incorrectly detected airways and separate fused right and left lungs. RESULTS: The performance of the automated segmentation scheme was evaluated using 101 computed tomography cases (91 thick slice, 10 thin slice scans). The 91 thick cases were pre- and post-surgery from 50 patients and were not independent. The automated scheme successfully segmented 94.0% of the 2,969 thick slice images and 97.6% of the 1,161 thin slice images. The mean difference of the total lung volumes calculated by the automated scheme and functional residual capacity plus 60% inspiratory capacity was -24.7 +/- 508.1 mL. The mean differences of the total lung volumes calculated by the automated scheme and an established, commonly used semi-automated scheme were 95.2 +/- 52.5 mL and -27.7 +/- 66.9 mL for the thick and thin slice cases, respectively. CONCLUSION: This simple, fully-automated lung segmentation scheme provides an objective tool to facilitate lung segmentation from computed tomography scans.

Image Processing, Computer-Assisted↗

PubMed2005

Simplified method of hepatic fibrosis quantification: design of a new morphometric analysis application.

INTRODUCTION: Semiquantitative evaluation of liver specimens is considered the standard method for measuring fibrosis; however, these systems lack the precision of a quantitative technique. METHODS: We developed an image analysis application (FibroXact) that automates and simplifies color segmentation. Trichrome slides were scanned and the program was used to evaluate each pixel based upon hue, lightness, and saturation values. Percent fibrosis was automatically calculated after thresholding. RESULTS: Linear regression demonstrated a strong relationship between the FibroXact result and the semiquantitative result (P<0.001). CONCLUSION: We have developed an image analysis application to quantify liver fibrosis. Correlation between our results and a standard semiquantitative system was demonstrated.

Azo Compounds↗

PubMed1996

Automated 3-D montage synthesis from laser-scanning confocal images: application to quantitative tissue-level cytological analysis.

This paper presents a landmark based method for efficient, robust, and automated computational synthesis of high-resolution, two-dimensional (2-D) or three-dimensional (3-D) wide-area images of a specimen from a series of overlapping partial views. The synthesized image is the set union of the areas or volumes covered by the partial views, and is called the "montage." This technique is used not only to produce gray-level montages, but also to montage the results of automated image analysis, such as 3-D cell segmentation and counting, so as to generate large representations that are equivalent to processing the large wide-area image at high resolution. The method is based on computing a concise set of feature-tagged landmarks in each partial view, and establishing correspondences between the landmarks using a combinatorial point matching algorithm. This algorithm yields a spatial transformation linking the partial views that can be used to create the montage. Such processing can be a first step towards high-resolution large-scale quantitative tissue studies. A detailed example using 3-D laser-scanning confocal microscope images of acriflavine-stained hippocampal sections of rat brain is presented to illustrate the method.

Animals↗

PubMed2003

Preferred collagen fiber orientation in the human mid-shaft femur.

Collagen fiber orientation is one aspect of the microstructure of bone that influences its mechanical properties. While the spatial distribution of preferentially oriented collagen is hypothesized to reflect the effects of loading during the process of aging, its variability in a modern human sample is essentially unknown. In a large sample (n = 67) of autopsied adults, the variability of collagen fiber orientation in the mid-shaft femur was examined in relation to age and sex. Montaged images of entire 100 microm thick cross-sections were obtained using circularly polarized light microscopy (CPLM) under standardized illuminating conditions. An automated image-analyzing routine divided images into 48 segments according to anatomical position. Average gray values (varying with orientation) were quantified for each segment, and one-way ANOVA with Tukey HSD post hoc tests were applied to assess differences between segments. Collagen fiber orientation appeared to be nonrandomly distributed across the mid-shaft femur sample; however, no single "human" pattern was identified. Individual variation, unexplainable by age, sex, or body size, exceeded population-level trends. Differences between age and sex groups suggest there is a strong correspondence between collagen fiber orientation and tissue-type distributions. The minimal consistencies demonstrated here may reflect mechanical forces induced at the femoral mid-shaft. However, the myriad of other factors that may influence collagen fiber orientation patterning, including growth trajectories, metabolic and nutritional status, and disease states, must be explored further. Only then, in conjunction with studies of other structural and material properties of bone, will we be able to elucidate the linkages between microstructure and functional adaptation in the human mid-shaft femur.

Adult↗

PubMed1998

Usefulness of color kinesis imaging for evaluation of regional right ventricular wall motion in patients with surgically repaired tetralogy of Fallot.

We evaluated regional right ventricular wall motion during systole in patients with surgically repaired tetralogy of Fallot (TOF) using color kinesis imaging. Color kinesis images were obtained in a subcostal sagittal view from 19 patients with repaired TOF (TOF group), aged 3 to 5 years, and 20 age-matched normal subjects (control group). For regional wall motion analysis, the endocardial motion distance and the fractional area change (FAC) were calculated for 6 segments obtained from color kinesis images. The endocardial inward excursion distances and the FACs in the upper, middle, and lower posterior segments of the TOF group were significantly greater than those of the control group (p <0.01 for each segment). The upper anterior segment showed significantly reduced inward excursion distance and FAC in the TOF group than in the control group (p <0.01, both). The dyskinetic outward excursion distances of the middle and lower posterior segments were significantly lower in the TOF group than in the control group (p <0.01, both). In the upper and middle anterior segments, the endocardial outward excursion distances were significantly higher in the TOF group than in the control group (p <0.01 and 0.05). Correlation between right ventriculographic and color kinesis measurements was excellent (y = 1.14x - 1.30, r = 0.87 for the endocardial inward excursion, and y = 1.03x +/- 0.56, r = 0.91 for the FAC). In conclusion, color kinesis is a useful method for assessing regional right ventricular wall motion. Segmental analysis of color kinesis images provides accurate, automated, and quantitative diagnosis of regional right ventricular wall motion abnormalities in patients with surgically repaired TOF.

Child, Preschool↗

PubMedDate not supplied

Effects of image orientation on the comparability of pediatric brain volumes using three-dimensional MR data.

PURPOSE: The purpose of this study was to examine the comparability of morphometric measurements made on pediatric data sets collected at five scanner locations, each using variations on a 3D spoiled gradient-recalled echo (SPGR) pulse sequence. METHOD: Archived MR data from 60 typically developing children were collected and separated into seven groups based on the pulse sequence used. A highly automated image-processing procedure was used to segment the brain data into white tissue, gray tissue, and CSF compartments and into various neuroanatomic regions of interest. RESULTS: Volumetric comparisons between groups revealed differences in areas of the temporal and occipital lobes. These differences were observed when comparing data sets with different image orientations and appeared to be due to partial volume averaging (PVA) and susceptibility-induced geometric distortions. CONCLUSION: Our results indicate that slice selection and image resolution should be controlled in volumetric studies using aggregated data from multiple centers to minimize the effects of PVA and susceptibility-induced geometric distortions.

Adolescent↗

PubMed1995

Knowledge-guided segmentation and morphometric analysis of colorectal dysplasia.

The histopathologic grading of colorectal adenomatous dysplasia is a subjective process. In this study, automated image analysis using knowledge-guided software was used to quantitatively assess colorectal glandular characteristics. Cases of histologically normal mucosa and of low, moderate and high grade dysplasia were examined using the technique. A total of 19 morphometric and densitometric features were measured on each gland. These included gland shape, epithelial area, nuclear stratification and nuclear optical density. Discriminant analysis of the data revealed those morphometric features which provided the best discrimination between the various histologic groups. The area of epithelium occupied by nuclei was the strongest discriminating variable, and this, in combination with a discriminant function derived from the remaining variables, was used to plot cases in bivariate sample space. The plots revealed that the data for normal glands were consistently well separated from dysplastic gland data. Data sets belonging to the various grades of dysplasia showed varying degrees of separation, depending which two histologic groups the discriminant function was based on. This study showed that automated image analysis of complex histologic scenes is possible using knowledge-guided segmentation and that it can provide useful data for the objective classification of colorectal dysplasia.

Adenoma↗

PubMed2007

Automated interpretation of PET/CT images in patients with lung cancer.

PURPOSE: To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung cancer. METHODS: A total of 87 patients who underwent PET/CT examinations due to suspected lung cancer comprised the training group. The test group consisted of PET/CT images from 49 patients suspected with lung cancer. The consensus interpretations by two experienced physicians were used as the 'gold standard' image interpretation. The training group was used in the development of the automated method. The image processing techniques included algorithms for segmentation of the lungs based on the CT images and detection of lesions in the PET images. Lung boundaries from the CT images were used for localization of lesions in the PET images in the feature extraction process. Eight features from each examination were used as inputs to artificial neural networks trained to classify the images. Thereafter, the performance of the network was evaluated in the test set. RESULTS: The performance of the automated method measured as the area under the receiver operating characteristic curve, was 0.97 in the test group, with an accuracy of 92%. The sensitivity was 86% at a specificity of 100%. CONCLUSIONS: A completely automated method using artificial neural networks can be used to detect lung cancer with such a high accuracy that the application as a clinical decision support tool appears to have significant potential.

Adult↗

PubMed2005

Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling.

This paper presents a new method for deformable model-based segmentation of lumen and thrombus in abdominal aortic aneurysms from computed tomography (CT) angiography (CTA) scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialize the automated thrombus segmentation method. For the lumen, the image-derived deformation term is based on a simple grey level model (two thresholds). For the more complex problem of thrombus segmentation, a grey level modeling approach with a nonparametric pattern classification technique is used, namely k-nearest neighbors. The intensity profile sampled along the surface normal is used as classification feature. Manual segmentations are used for training the classifier: samples are collected inside, outside, and at the given boundary positions. The deformation is steered by the most likely class corresponding to the intensity profile at each vertex on the surface. A parameter optimization study is conducted, followed by experiments to assess the overall segmentation quality and the robustness of results against variation in user input. Results obtained in a study of 17 patients show that the agreement with respect to manual segmentations is comparable to previous values reported in the literature, with considerable less user interaction.

Algorithms↗

PubMed2003

Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error.

The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skull-stripping. Although automated method showed better efficient results, it should require additional intervention. In contrast to that, semi-automated method showed better accurate results, but it was time consuming and prone to operator bias. Therefore, it might be practical that the semi-automated method was used as the post-processing of the automated one.

Algorithms↗

PubMed2000

Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging.

The well-known variability in the distribution of high frequency electromagnetic fields in the human body causes problems in the analysis of structural information in high field magnetic resonance images. We describe a method of compensating for the purely intensity-based effects. In our simple and rapid correction algorithm, we first use statistical means to determine the background image noise level and the edges of the image features. We next populate all "noise" pixels with the mean signal intensity of the image features. These data are then smoothed by convolution with a gaussian filter using Fourier methods. Finally, the original data that are above the noise level are normalized to the smoothed images, thereby eliminating the lowest spatial frequencies in the final, corrected data. Processing of a 124 slice, 256 x 256 volume dataset requires under 70 sec on a laptop personal computer. Overall, the method is less prone to artifacts from edges or from sensitivity to absolute head position than are other correction techniques. Following intensity correction, the images demonstrated obvious qualitative improvement and, when subjected to automated segmentation tools, the accuracy of segmentation improved, in one example, from 35.3% to 84.7% correct, as compared to a manually-constructed gold standard.

Algorithms↗

PubMed2004

Automatic segmentation of different-sized white matter lesions by voxel probability estimation.

A new method for fully automated segmentation of white matter lesions (WMLs) on cranial MR imaging is presented. The algorithm uses five types of regular MRI-scans. It is based on a K-Nearest Neighbor (KNN) classification technique, which builds a feature space from voxel intensity features and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps binary segmentations can be produced. ROC-curves show that the segmentations achieve a high sensitivity and specificity. Three similarity measures, the similarity index (SI), the overlap fraction (OF) and the extra fraction (EF), are calculated for evaluation of the results and determination of the optimal threshold on the probability map. Investigation of the relation between the total lesion volume and the similarity measures shows that the method performs well for lesions larger than 2 cc. The maximum SI per patient is correlated to the total WML volume. No significant relation between the lesion volume and the optimal threshold has been observed. The probabilistic equivalents of the SI, OF en EF (PSI, POF and PEF) allow direct evaluation of the probability maps, which provides a strong tool for comparison of different classification results. A significant correlation between the lesion volume and the PSI and the PEF has been noticed. This method for automated WML segmentation is applicable to lesions of different sizes and shapes, and reaches an accuracy that is comparable to existing methods for multiple sclerosis lesion segmentation. Furthermore, it is suitable for detection of WMLs in large and longitudinal population studies.

Aged↗

PubMed2002

Measuring size and shape of the hippocampus in MR images using a deformable shape model.

A method for segmentation and quantification of the shape and size of the hippocampus is proposed, based on an automated image analysis algorithm. The algorithm uses a deformable shape model to locate the hippocampus in magnetic resonance images and to determine a geometric representation of its boundary. The deformable model combines three types of information. First, it employs information about the geometric properties of the hippocampal boundary, from a local and relatively finer scale to a more global and relatively coarser scale. Second, the model includes a statistical characterization of normal shape variation across individuals, serving as prior knowledge to the algorithm. Third, the algorithm utilizes a number of manually defined boundary points, which can help guide the model deformation to the appropriate boundaries, wherever these boundaries are weak or not clearly defined in MR images. Excellent agreement is demonstrated between the algorithm and manual segmentations by well-trained raters, with a correlation coefficient equal to 0.97 and algorithm/rater differences statistically equivalent to interrater differences for manual definitions.

Algorithms↗

PubMed2004

Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections.

We present a region-based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over-segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two- as well as three-dimensional images.

Automation↗

PubMedDate not supplied

B-spline methods for interactive segmentation and modeling of lumen and vessel surfaces in three-dimensional intravascular ultrasound.

Intravascular ultrasound (IVUS) provides direct depiction of coronary anatomy, including the degree and extent of coronary plaque, useful in quantitative research or clinical studies. These studies require fast and accurate analysis of coronary morphometry in volumetric IVUS images. Semi-automated interaction techniques are important for this task due to limitations of automated processing. We present B-spline-based surface fitting and manipulation methods that provide a foundation for such interaction techniques. They can be integrated easily with previously developed segmentation algorithms to provide a semi-automated segmentation system for identification of luminal and vessel borders in volumetric IVUS images.

Algorithms↗

PubMed2000

Evaluation of octree regional spatial normalization method for regional anatomical matching.

The goal of regional spatial normalization is to remove anatomical differences between individual three-dimensional (3D) brain images by warping them to match features of a standard brain atlas. Processing to fit features at the limiting resolution of a 3D MR image volume is computationally intensive, limiting the broad use of full-resolution regional spatial normalization. In Kochunov et al. (1999: Neuro-Image 10:724-737), we proposed a regional spatial normalization algorithm called octree spatial normalization (OSN) that reduces processing time to minutes while targeting the accuracy of previous methods. In the current study, modifications of the OSN algorithm for use in human brain images are described and tested. An automated brain tissue segmentation procedure was adopted to create anatomical templates to drive feature matching in white matter, gray matter, and cerebral-spinal fluid. Three similarity measurement functions (fast-cross correlation (CC), sum-square error, and centroid) were evaluated in a group of six subjects. A combination of fast-CC and centroid was found to provide the best feature matching and speed. Multiple iterations and multiple applications of the OSN algorithm were evaluated to improve fit quality. Two applications of the OSN algorithm with two iterations per application were found to significantly reduce volumetric mismatch (up to six times for lateral ventricle) while keeping processing time under 30 min. The refined version of OSN was tested with anatomical landmarks from several major sulci in a group of nine subjects. Anatomical variability was appreciably reduced for every sulcus investigated, and mean sulcal tracings accurately followed sulcal tracings in the target brain.

Algorithms↗

PubMed2004

Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours.

Thalamus is an important neuro-anatomic structure in the brain. In this paper, an automated method is presented to segment thalamus from magnetic resonance images (MRI). The method is based on a discrete dynamic contour model that consists of vertices and edges connecting adjacent vertices. The model starts from an initial contour and deforms by external and internal forces. Internal forces are calculated from local geometry of the model and external forces are estimated from desired image features such as edges. However, thalamus has low contrast and discontinues edges on MRI, making external force estimation a challenge. The problem is solved using a new algorithm based on fuzzy C-means (FCM) unsupervised clustering, Prewitt edge-finding filter, and morphological operators. In addition, manual definition of the initial contour for the model makes the final segmentation operator-dependent. To eliminate this dependency, new methods are developed for generating the initial contour automatically. The proposed approaches are evaluated and validated by comparing automatic and radiologist's segmentation results and illustrating their agreement.

Algorithms↗