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PubMed2006

Automated segmentation of molecular subunits in electron cryomicroscopy density maps.

Electron cryomicroscopy (cryoEM) is capable of imaging large macromolecular machines composed of multiple components. However, it is currently only possible to achieve moderate resolution at which it may be possible to computationally extract the individual components in the machine. In this work, we present application details of an automated method for detecting and segmenting the components of a large machine in an experimentally determined density map. This method is applicable to object with and without symmetry and takes advantage of global and local symmetry axes if present. We have applied this segmentation algorithm to several cryoEM data sets already deposited in EMDB with various complexities, symmetries and resolutions and validated the results using manually segmented density and available structures of the components in the PDB. As such, automated segmentation could become a useful tool for the analysis of the ever-increasing number of structures of macromolecular machines derived from cryoEM.

Bacteriophage P22↗

PubMed2002

3-D active appearance models: segmentation of cardiac MR and ultrasound images.

A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal image sequences. Comprehensive design of a three-dimensional (3-D) active appearance model (AAM) is reported for the first time as an involved extension of the AAM framework introduced by Cootes et al. The model's behavior is learned from manually traced segmentation examples during an automated training stage. Information about shape and image appearance of the cardiac structures is contained in a single model. This ensures a spatially and/or temporally consistent segmentation of three-dimensional cardiac images. The clinical potential of the 3-D AAM is demonstrated in short-axis cardiac MR images and four-chamber echocardiographic sequences. The method's performance was assessed by comparison with manually identified independent standards in 56 clinical MR and 64 clinical echo image sequences. The AAM method showed good agreement with the independent standard using quantitative indexes of border positioning errors, endo- and epicardial volumes, and left ventricular mass. In MR, the endocardial volumes, epicardial volumes, and left ventricular wall mass correlation coefficients between manual and AAM were R2 = 0.94, 0.97, 0.82, respectively. For echocardiographic analysis, the area correlation was R2 = 0.79. The AAM method shows high promise for successful application to MR and echocardiographic image analysis in a clinical setting.

Echocardiography, Three-Dimensional↗

PubMed1997

ECG-gated three-dimensional intravascular ultrasound: feasibility and reproducibility of the automated analysis of coronary lumen and atherosclerotic plaque dimensions in humans.

BACKGROUND: Automated systems for the quantitative analysis of three-dimensional (3D) sets of intravascular ultrasound (IVUS) images have been developed to reduce the time required to perform volumetric analyses; however, 3D image reconstruction by these nongated systems is frequently hampered by cyclic artifacts. METHODS AND RESULTS: We used an ECG-gated 3D IVUS image acquisition workstation and a dedicated pullback device in atherosclerotic coronary segments of 30 patients to evaluate (1) the feasibility of this approach of image acquisition, (2) the reproducibility of an automated contour detection algorithm in measuring lumen, external elastic membrane, and plaque+media cross-sectional areas (CSAs) and volumes and the cross-sectional and volumetric plaque+media burden, and (3) the agreement between the automated area measurements and the results of manual tracing. The gated image acquisition took 3.9+/-1.5 minutes. The length of the segments analyzed was 9.6 to 40.0 mm, with 2.3+/-1.5 side branches per segment. The minimum lumen CSA measured 6.4+/-1.7 mm2, and the maximum and average CSA plaque+media burden measured 60.5+/-10.2% and 46.5+/-9.9%, respectively. The automated contour-detection required 34.3+/-7.3 minutes per segment. The differences between these measurements and manual tracing did not exceed 1.6% (SD<6.8%). Intraobserver and interobserver differences in area measurements (n=3421; r=.97 to.99) were <1.6% (SD<7.2%); intraobserver and interobserver differences in volumetric measurements (n=30; r=.99) were <0.4% (SD<3.2%). CONCLUSIONS: ECG-gated acquisition of 3D IVUS image sets is feasible and permits the application of automated contour detection to provide reproducible measurements of the lumen and atherosclerotic plaque CSA and volume in a relatively short analysis time.

Adult↗

PubMed1997

Automated location of dysplastic fields in colorectal histology using image texture analysis.

Automation in histopathology is an attractive concept and recent advances in the application of computerized expert systems and machine vision have made automated image analysis of histological images possible. Systems capable of complete automation not only require the ability to segment tissue features and grade histological abnormalities, but, must also be capable of locating diagnostically useful areas from within complex histological scenes. This is the first stage of the diagnostic process. The object of this study was to develop criteria for the automatic identification of focal areas of colorectal dysplasia from a background of histologically normal tissue. Fields of view representing normal colorectal mucosa (n = 120) and dysplastic mucosa (n = 120) were digitally captured and subjected to image texture analysis. Two features were selected as being the most important in the discrimination of normal and adenomatous colorectal mucosa. The first was a feature of the co-occurrence matrix and the second was the number of low optical density pixels in the image. A linear classification rule defined using these two features was capable of correctly classifying 86 per cent of a series of training images into their correct groups. In addition, large histological scenes were digitally captured, split into their component images, analysed according to texture, and classified as normal or abnormal using the previously defined classification rule. Maps of the histological scenes were constructed and in most cases, dysplastic colorectal mucosa was correctly identified on the basis of image texture: 83 per cent of test images were correctly classified. This study demonstrates that abnormalities in low-power tissue morphology can be identified using quantitative image analysis. The identification of diagnostically useful fields advances the potential of automated systems in histopathology: these regions could than be scrutinized at high power using knowledge-guided image segmentation for disease grading. Systems of this kind have the potential to provide objectivity, unbiased sampling, and valuable diagnostic decision support.

Adenomatous Polyps↗

PubMed1996

Automated measurement of infarct size with scanned images of triphenyltetrazolium chloride-stained rat brains.

BACKGROUND AND PURPOSE: The extent of brain infarction after local cerebral ischemia is frequently assessed with the mitochondrial activity indicator 2,3,5-triphenyltetrazolium chloride (TTC). We describe an automated procedure for analysis of infarct size in TTC-stained rat brains. METHODS: Rats were subjected to middle cerebral artery occlusion and killed after 24 to 36 hours, and their brains were processed for TTC staining. Digital images of coronal sections from these brains (n > 50) were acquired with a desktop color scanner. The resulting images were divided into red, blue, and green component images. Total brain and infarct areas were automatically determined on the basis of total pixel intensity and area after segmentation of the red and green images, respectively. Automated measurements were compared with those made with a video camera-based image acquisition system that required manual tracing of lesion boundaries. RESULTS: The spatial resolution of scanned brain images (approximately equal to 200 microns) was comparable to that of the camera-based system and provided sufficient detail to recognize infarct boundaries and neuroanatomical features. Scanner-based acquisition and analysis were faster than with the camera-based method. The green component image accurately distinguished infarcted from normal brain, and the red component image represented total brain dimensions. Infarct measurements obtained by the automated method correlated closely with those from conventional apparatus (R2 = .89, P < .001). Intraobserver reliability with the automated method (R2 = 1.00) was higher than with the conventional method (R2 = .77). CONCLUSIONS: Infarct size after middle cerebral artery occlusion in the rat can be rapidly and reproducibly assessed with inexpensive scanning equipment and automated image analysis of TTC-stained brains.

Animals↗

PubMed1999

Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data.

A new fast automated algorithm has been developed to segment the brain from T1-weighted volume MR images. The algorithm uses automated thresholding and morphological operations. It is fully three-dimensional and therefore independent of scan orientation. The validity and the performance of the algorithm were evaluated by comparing the automatically calculated brain volume with semi-automated measurements in 10 subjects, by calculating the brain volume from repeated scans in another 10 subjects, and by visual inspection. The mean and standard deviation of the difference between semi-automated and automated measurements were 0.56% and 2.8% of the mean brain volume, respectively, which is within inter-observer variability of the semi-automated method. The mean and standard deviation of the difference between the total volumes calculated from repeated scans were 0.40% and 1.2% of the mean brain volume, respectively. Good results were also obtained from a scan of abnormal brains.

Adult↗

PubMed1993

Size of cortical bone and relationship to bone mineral density assessed by quantitative computed tomography image segmentation.

RATIONALE AND OBJECTIVES: The accuracy of the measurement of the size of cortical bone on computed tomography (CT) images of human vertebrae was evaluated using an automated contour detection and segmentation procedure. METHODS: Forty human lumbar vertebrae were scanned using 8-mm slices and an automated detection for definition of trabecular and cortical region of interest. The vertebrae were embedded in a polyester resin and 8-mm-thick midvertebral specimens were excised using a diamond circular saw. Contact radiographs of these specimens were performed and, after photograph magnification, the cortical area was measured using computerized planimetry. RESULTS: Cortical area measured on CT images was highly correlated with the area measured by planimetry on the specimens (r = .91; P < .001) with, however, a systematic over-estimation. A significant relationship was found between density and width of the cortex (r = .56; P < .001). CONCLUSIONS: Computed tomography is able to assess the size of cortical bone in human vertebrae, but a threshold detection algorithm, as used in the current study, is not adequate to obtain the precise anatomic dimensions.

Aged↗

PubMed2001

Automatic segmentation of medical images for renal calculi and analysis.

Development of an automated system to identify renal calculi based on its physical characteristics is proposed. Calculi are due to abnormal collection of certain chemicals like oxalate, phosphate and uric arid. Renal calculi may be present in kidney, ureter or in urinary bladder. An algorithm is proposed to detect calculus using its shadow. The system also extracts the properties of calculi such as size, shape and location, which are vital for reliable diagnosis. This method has been implemented in the MATLAB/IDL platform and a considerable success rate is obtained.

Algorithms↗

PubMed2004

Multivariate statistical model for 3D image segmentation with application to medical images.

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Algorithms↗

PubMedDate not supplied

A quantitative assay for intercellular aggregation.

In an earlier communication (Munn et al., J Immunol. Methods 166: 11-25, 1993), we presented the initial development of a quantitative assay for monitoring the rates of cellular aggregation based on digital image processing and video microscopy. This study describes some important enhancements and modifications to the procedure. A new index is introduced to characterize the three-dimensional morphology of the aggregates. This index is based on temporal changes in the projected area of the cells and cell aggregates during the course of the experiment. By drawing an analogy with the kinetic theory of gases, we have also introduced a procedure to normalize for variations in cell seeding density among different experiments. In addition, the image analysis technique has been improved by introducing a background subtraction algorithm to remove illumination defects and an adaptive segmentation procedure. These improvements allowed us to completely automate the image analysis procedure, thus minimizing user intervention and improving the reproducibility of the measurements. The enhanced visual assay is evaluated using some recent results from our studies on homotypic lymphocyte aggregation.

Cell Aggregation↗

PubMed2005

[Medical image automatic adjusting window and segmentation].

Image guided surgical navigation system is the most advanced surgical apparatus, which develops most rapidly and has great application prospects in neurosurgery, orthopaedics, E.N.T. department etc. In current surgical navigation systems, windowing, segmenting and registration of medical images all depend on manual operation, and automation of image processing is urgently needed. This paper proposes the algorithm which realizes very well automatic windowing and segmentation of medical images: first, we analyze a lot of MRI and CT images and propose corresponding windowing algorithm according to their common features of intensity distribution. Experiments show that the effects of windowing of most MRI and CT images are optimized. Second, we propose the seed growing algorithm based on intensity connectivity,which can segment tumor and its boundary exactly by simply clicking the mouse, and control dynamically the results in real time. If computer memory permits, the algorithm can segment 3D images directly. Tests show that this function is able to shorten the time of surgical planning, lower the complexity, and improve the efficiency in navigation surgery.

Algorithms↗

PubMed2006

A new computer-based decision-support system for the interpretation of bone scans.

OBJECTIVE: To develop a completely automated method, based on image processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases. METHODS: A total of 200 patients, all of whom had the diagnosis of breast or prostate cancer and had undergone bone scintigraphy, were studied retrospectively. Whole-body images, anterior and posterior, were obtained after injection of 99mTc-methylene diphosphonate. The study material was randomly divided into a training group and a test group, with 100 patients in each group. The training group was used in the process of developing the image analysis techniques and to train the artificial neural networks. The test group was used to evaluate the automated method. The image processing techniques included algorithms for segmentation of the head, chest, spine, pelvis and bladder, automatic thresholding and detection of hot spots. Fourteen features from each examination were used as input to artificial neural networks trained to classify the images. The interpretations by an experienced physician were used as the 'gold standard'. RESULTS: The automated method correctly identified 28 of the 31 patients with metastases in the test group, i.e., a sensitivity of 90%. A false positive classification of metastases was made in 18 of the 69 patients not classified as having metastases by the experienced physician, resulting in a specificity of 74%. CONCLUSION: A completely automated method can be used to detect metastases in bone scans. Future developments in this field may lead to clinically valuable decision-support tools.

Adult↗

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↗

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↗