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Results for “Automated image segmentation”

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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↗

PubMed1998

A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms.

We compared the performance of three computer based classification methods when applied to the problem of detecting microaneurysms on digitised angiographic images of the retina. An automated image processing system segmented 'candidate' objects (microaneurysms or spurious objects), and produced a list of features on each candidate for use by the classifiers. We compared an empirically derived rule based system with two automated methods, linear discriminant analysis and a learning vector quantiser artificial neural network, to classify the objects as microaneurysms or otherwise. ROC analysis shows that the rule based system gave a higher performance than the other methods (p = 0.92) although a much greater development time is required.

Aneurysm↗

PubMed2004

Automated segmentation of lumbar vertebrae in digital videofluoroscopic images.

Low back pain is a significant problem in the industrialized world. Diagnosis of the underlying causes can be extremely difficult. Since mechanical factors often play an important role, it can be helpful to study the motion of the spine. Digital videofluoroscopy has been developed for this study and it can provide image sequences with many frames, but which often suffer due to noise, exacerbated by the very low radiation dosage. Thus, determining vertebra position within the image sequence presents a considerable challenge. There have been many studies on vertebral image extraction, but problems of repeatability, occlusion and out-of-plane motion persist. In this paper, we show how the Hough transform (HT) can be used to solve these problems. Here, Fourier descriptors were used to describe the vertebral body shape. This description was incorporated within our HT algorithm from which we can obtain affine transform parameters, i.e., scale, rotation and center position. The method has been applied to images of a calibration model and to images from two sequences of moving human lumbar spines. The results show promise and potential for object extraction from poor quality images and that models of spinal movement can indeed be derived for clinical application.

Algorithms↗

PubMed2005

Automated segmentation of pigmented skin lesions in multispectral imaging.

The aim of this study was to develop an algorithm for the automatic segmentation of multispectral images of pigmented skin lesions. The study involved 1700 patients with 1856 cutaneous pigmented lesions, which were analysed in vivo by a novel spectrophotometric system, before excision. The system is able to acquire a set of 15 different multispectral images at equally spaced wavelengths between 483 and 951 nm. An original segmentation algorithm was developed and applied to the whole set of lesions and was able to automatically contour them all. The obtained lesion boundaries were shown to two expert clinicians, who, independently, rejected 54 of them. The 97.1% contour accuracy indicates that the developed algorithm could be a helpful and effective instrument for the automatic segmentation of skin pigmented lesions.

Adolescent↗

PubMed2001

Measuring extraocular muscle volume using dynamic contours.

The effect of medical treatment on extraocular muscle enlargement in thyroid associated ophthalmopathy (TAO) may be monitored by measuring the change in volume of the extraocular muscles on serial orbital MRI examinations. In theory, 3D image sets offer the opportunity to minimise errors due to poor repositioning and partial volume effects. This study describes an automated technique for estimating extraocular muscle volumes from 3D datasets. Operator input is minimal and the technique is robust. Verification of the technique on both simulated and real datasets is described. For simulated image sets, both automated segmentation and manual outlining produced estimates of volume which were on average 4% less than "true" volume. For real patient data, extraocular muscle volumes measured by the automated technique were 1.6% (SD 13%) less than volumes measured by manual outlining. Coefficient of variation for repeat outlining of the same image dataset for the automated technique was 1.0%, compared with 4% for manual outlining. The manual technique took an experienced operator approximately 20 min to perform, compared to 7 min for the automated technique. The automated method is therefore rapid, reproducible and at least as accurate as other available methods.

Algorithms↗

PubMed2005

Relationship between multiple sclerosis intention tremor severity and lesion load in the brainstem.

Intention tremor due to multiple sclerosis is clinically similar to cerebellar tremor. This study investigated, in 14 multiple sclerosis patients, the relationship between intention tremor severity and the lesion load in different infratentorial regions. Tremor amplitude was quantified during step-tracking tasks. The lesion load was measured on magnetic resonance images using an automated segmentation method. Intention tremor amplitude was significantly related to lesion load in the brainstem but not in the cerebellum. Specifically, tremor amplitude correlated with the lesion load in the contralateral pons, and patients with more severe tremor in both arms had a greater lesion load bilaterally in the pons. These results support the view that multiple sclerosis intention tremor is related to dysfunction of cerebellar inflow and/or outflow pathways.

Brain Stem↗

PubMed1993

Standardization and quality control of quantitative microscopy in pathology.

Standardization and quality control of quantitative microscopy techniques are distinct but related concerns. The first deals with the great variety of quantitative methods, measured features, and even response variables used in investigation of biological or clinical processes. The latter deals with reproducibility of results from those investigations across time and test performance sites. Though distinct, efforts for standardization and quality control are inherently interactive. Consensus on standard methods, instrumentation, and data analysis is hard to achieve in fields developing as rapidly as quantitative microscopy. Consensus is possible, however, on the issues that affect test performance and interpretation. For example, issues of specimen type, fixation, processing, and staining affect image cytometry just as they do flow cytometry. Raw data acquisition issues include area sampling rules and fidelity of optical and sensor systems (light wavelength, glare/stray light, lens aberrations, numerical aperture, depth of focus, scan precision, pixel spacing and depth, sensor linearity, and stability). Intermediate data issues are primarily related to image foreground/background segmentation techniques--automated versus manual, object-specific versus field-based. Data reduction and interpretation procedures also provide many roads for divergence from uniformity. Each of these issues must be considered in terms of its effect on comparability and utility of quantitative microscopy results. Quality control for quantitative microscopy is as important as standardization for its use in research programs and with clinical specimens. The sine qua non of quality control is comparison of experimental results against a known "correct" value to estimate accuracy, and against other experimental results to estimate precision.(ABSTRACT TRUNCATED AT 250 WORDS)

Colonic Neoplasms↗

PubMed2000

Medical image segmentation using analysis of isolable-contour maps.

A common challenge for automated segmentation techniques is differentiation between images of close objects that have similar intensities, whose boundaries are often blurred due to partial-volume effects. We propose a novel approach to segmentation of two-dimensional images, which addresses this challenge. Our method, which we call intrinsic shape for segmentation (ISeg), analyzes isolabel-contour maps to identify coherent regions that correspond to major objects. ISeg generates an isolabel-contour map for an image by multilevel thresholding with a fine partition of the intensity range. ISeg detects object boundaries by comparing the shape of neighboring isolabel contours from the map. ISeg requires only little effort from users; it does not require construction of shape models of target objects. In a formal validation with computed-tomography angiography data, we showed that ISeg was more robust than conventional thresholding, and that ISeg's results were comparable to results of manual tracing.

Algorithms↗

PubMed2006

Plaque development, vessel curvature, and wall shear stress in coronary arteries assessed by X-ray angiography and intravascular ultrasound.

The relationships among vascular geometry, hemodynamics, and plaque development in the coronary arteries are complex and not yet well understood. This paper reports a methodology for the quantitative analysis of in vivo coronary morphology and hemodynamics, with particular emphasis placed on the critical issues of image segmentation and the automated classification of disease severity. We were motivated by the observation that plaque more often developed at the inner curvature of a vessel, presumably due to the relatively lower wall shear stress at these locations. The presented studies are based on our validated methodology for the three-dimensional fusion of intravascular ultrasound (IVUS) and X-ray angiography, introducing a novel approach for IVUS segmentation that incorporates a robust, knowledge-based cost function and a fully optimal, three-dimensional segmentation algorithm. Our first study shows that circumferential plaque distribution depends on local vessel curvature in the majority of vessels. The second study analyzes the correlation between plaque distribution and wall shear stress in a set of 48 in vivo vessel segments. The results were conclusive for both studies, with a stronger correlation of circumferential plaque thickness with local curvature than with wall shear stress. The inverse relationship between local wall shear stress and plaque thickness was significantly more pronounced (p<0.025) in vessel cross sections exhibiting compensatory enlargement (positive remodeling) without luminal narrowing than when the full spectrum of disease severity was considered. The inverse relationship was no longer observed in vessels where less than 35% of vessel cross sections remained without luminal narrowing. The findings of this study confirm, in vivo, the hypothesis that relatively lower wall shear stress is associated with early plaque development.

Computer Simulation↗

PubMed1995

Live cell image segmentation.

A major requirement of an automated, real-time, computer vision-based cell tracking system is an efficient method for segmenting cell images. The usual segmentation algorithms proposed in the literature exhibit weak performance on live unstained cell images, which can be characterized as being of low contrast, intensity-variant, and unevenly illuminated. We propose a two-stage segmentation strategy which involves: 1) extracting an approximate region containing the cell and part of the background near the cell, and 2) segmenting the cell from the background within this region. The approach effectively reduces the influence of peripheral background intensities and texture on the extraction of a cell region. The experimental results show that this approach for segmenting cell images is both fast and robust.

Algorithms↗

PubMed2005

Three-dimensional reconstruction of tumor microvasculature: simultaneous visualization of multiple components in paraffin-embedded tissue.

Three-dimensional (3D) visualization of microscopic structures may provide useful information about the exact 3D configuration, and offers a useful tool to examine the spatial relationship between different components in tissues. A promising field for 3D investigation is the microvascular architecture in normal and pathological tissue, especially because pathological angiogenesis plays a key role in tumor growth and metastasis formation. This paper describes an improved method for 3D reconstruction of microvessels and other microscopic structures in transmitted light microscopy. Serial tissue sections were stained for the endothelial marker CD34 to highlight microvessels and corresponding images were selected and aligned. Alignment of stored images was further improved by automated non-rigid image registration, and automated segmentation of microvessels was performed. Using this technique, 3D reconstructions were produced of the vasculature of the normal brain. Also, to illustrate the complexity of tumor vasculature, 3D reconstructions of two brain tumors were performed: a hemangioblastoma and a glioblastoma multiforme. The possibility of multiple component visualization was shown in a 3D reconstruction of endothelium and pericytes of normal cerebellar cortex and a hemangioblastoma using alternate staining for CD34 and alpha-smooth muscle actin in serial sections, and of a GBM using immunohistochemical double staining. In conclusion, the described 3D reconstruction procedure provides a promising tool for simultaneous visualization of microscopic structures.

Brain Neoplasms↗

PubMed2006

Semi-automated CT segmentation using optic flow and Fourier interpolation techniques.

In radiotherapy treatment planning, tumor volumes and anatomical structures are manually contoured for dose calculation, which takes time for clinicians. This study examines the use of semi-automated segmentation of CT images. A few high curvature points are manually drawn on a CT slice. Then Fourier interpolation is used to complete the contour. Consequently, optical flow, a deformable image registration method, is used to map the original contour to other slices. This technique has been applied successfully to contour anatomical structures and tumors. The maximum difference between the mapped contours and manually drawn contours was 6 pixels, which is similar in magnitude to difference one would see in manually drawn contours by different clinicians. The technique fails when the region to contour is topologically different between two slices. A solution is recommended to manually delineate contours on a sparse subset of slices and then map in both directions to fill the remaining slices.

Algorithms↗

PubMed1999

Age-associated changes in rhesus CNS composition identified by MRI.

Multispectral automated segmentation of MR images of the brains of 10 young (5-8 years), 10 middle-aged (12-17 years), and 11 old (21-27 years) female rhesus monkeys revealed age-associated changes in brain volume and composition. Total brain parenchymal volume (expressed as fraction of intracranial volume-%ICV) decreased at a linear rate of 0.3+/-0.04% ICV/year. Up to age approximately 15 years, this loss was almost entirely due to gray matter loss, with a compensatory increase in cerebrospinal fluid (CSF), and possibly some white matter. Brain tissue composition, expressed as the gray matter/white matter volume ratio confirmed that gray matter loss exceeded white matter loss, but the rate of decline in the gray/white ratio began to slow after approximately 15 years. Comparison of these age-associated changes in rhesus brain with those in humans suggest that the brain aging in rhesus is a good model of human brain aging, but occurs approximately 3-fold faster.

Aging↗

PubMed1995

Automated screening for cytomegalovirus infected cells using image analysis. Comparison of two immunoenzymatic staining methods with respect to colour segmentation.

The detection of human cytomegalovirus (HCMV) infected poly-morphonuclear leukocytes (PMNLs) for early finding of the pp65 antigen using automated image analysis has been improved. The routinely used immunoenzyme peroxidase (PO) labelling has been replaced by alkaline phosphatase (AP) using new fuchsin as substrate. The number of automatically detected false positive objects due to incomplete inactivation of endogenous peroxidase and strong variations in counterstain intensity could be reduced by 81% using this AP staining method. The number of detected truly positive cells with both staining methods was not significantly different. Furthermore, a new image analysis system providing processing of colour images was evaluated. Since plain differences in absorption wavelength are required for colour segmentation, the red immuno-staining was combined with a green counterstain using methyl green. Screening of AP- instead of PO-stained slides in combination with colour segmentation resulted in a further reduction of the number of falsely detected alarms from 61% to 11%. Consequently, the sensitivity of the automated detection was improved. For AP staining detection of cells in frequencies of approximately one to one million was demonstrated. Screening for CMV-positive, alkaline phosphatase labelled cells using an image analysis system with colour segmentation resulted in a reduced false alarm rate, a better visual interpretation of the images and subsequently an increase in the sensitivity of the automated screening process.

Cytomegalovirus↗

PubMed2006

Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images.

This paper describes a fully automated segmentation and recognition scheme, which is designed to recognize lung anatomical structures in the human chest by segmenting the different chest internal organ and tissue regions sequentially from high-resolution chest CT images. A sequential region-splitting process is used to segment lungs, airway of bronchus, lung lobes and fissures based on the anatomical structures and statistical intensity distributions in CT images. The performance of our scheme is evaluated by segmenting lung structures from high-resolution multi-slice chest CT images from 44 patients; the validity of our method was proved by preliminary experimental results.

Humans↗

PubMed2006

Technical aspects and evaluation methodology for the application of two automated brain MRI tumor segmentation methods in radiation therapy planning.

The purpose of this study was to design the steps necessary to create a tumor volume outline from the results of two automated multispectral magnetic resonance imaging segmentation methods and integrate these contours into radiation therapy treatment planning. Algorithms were developed to create a closed, smooth contour that encompassed the tumor pixels resulting from two automated segmentation methods: k-nearest neighbors and knowledge guided. These included an automatic three-dimensional (3D) expansion of the results to compensate for their undersegmentation and match the extended contouring technique used in practice by radiation oncologists. Each resulting radiation treatment plan generated from the automated segmentation and from the outlining by two radiation oncologists for 11 brain tumor patients was compared against the volume and treatment plan from an expert radiation oncologist who served as the control. As part of this analysis, a quantitative and qualitative evaluation mechanism was developed to aid in this comparison. It was found that the expert physician reference volume was irradiated within the same level of conformity when using the plans generated from the contours of the segmentation methods. In addition, any uncertainty in the identification of the actual gross tumor volume by the segmentation methods, as identified by previous research into this area, had small effects when used to generate 3D radiation therapy treatment planning due to the averaging process in the generation of margins used in defining a planning target volume.

Algorithms↗

PubMed1998

Breast cancer risk and measured mammographic density.

It has been well established that there is a positive correlation between the dense appearance of breast stroma and parenchyma on a mammogram and the risk of breast cancer. Subjective assessment by radiologists indicated relative risks on the order of 4 to 6 for the group of women whose mammograms showed a density of over 75% or more of the projected area compared to those with an absence of density. In order to obtain a more quantitative, continuous and reproducible means of estimating breast density, which is sensitive to small changes, we have developed quantitative methods for the analysis of mammographic density, which can be applied to digitized mammograms. These techniques have been validated in a nested case-control study on 708 women aged 40-59 years (on entry) who participated in a national mammographic screening study. An interactive image segmentation method and two completely automated techniques based on image texture and grey scale histogram measures have been developed and evaluated. While our methods all show statistically significant risk factors for dense breasts, the interactive method currently provides the highest risk values (relative risk 4.0, 95% confidence interval (CI) = 2.12-7.56) compared to a measure based on the shape of the image histogram (relative risk 3.35, 95% CI = 1.57-7.12) or the fractal dimension of the mammogram (relative risk 2.54, 95% CI = 1.14-5.68). All methods were highly consistent between images of the left and right breast and between the two standard views (cranio-caudal and medio-lateral oblique) of each breast, so that studies can be done by sampling only one of the four views per examination. There is a large number of factors in addition to breast density which affect the appearance of the mammogram. In particular, the assessment of density is made difficult where the breast is not uniformly compressed, e.g. at the periphery. We have designed and are currently evaluating an image processing algorithm that effectively corrects for this problem and have considered methods for controlling some of the variables of image acquisition in prospective studies. Measurements of breast density may be helpful in assigning risk groups to women. Such measurements might guide the frequency of mammographic screening, aid the study of breast cancer aetiology, and be useful in monitoring possible risk-modifying interventions. Using our techniques, we have been able to show that reduction of the proportion of fat in the diet can result in reductions of breast density, although the direct connection to risk has not yet been made. The relationship between breast density and hormone-related and genetic factors is also of great interest. It is often not possible or ethical to obtain mammograms on some groups of women for whom information on density would be very useful. This includes younger women as well as groups in which it would be desirable to obtain such information at frequent intervals. For this reason, we are exploring the use of imaging approaches such as ultrasound and magnetic resonance imaging, which do not require ionizing radiation, to make measurements analogous to those now being performed by using X-ray mammograms.

Adult↗