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At least 253 records · Page 14Linked to original sources
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↗
PubMed2006

Rapid and automatic calculation of the midsagittal plane in magnetic resonance diffusion and perfusion images.

RATIONALE AND OBJECTIVES: A near real-time and fully automatic method for calculation of the midsagittal plane (MSP) for magnetic resonance (MR) diffusion and perfusion images is introduced. MATERIALS AND METHODS: The method is based on the Kullback-Leibler's (KL) measure quantifying the difference between two intensity distributions. The MSP is a sagittal plane with the highest KL measure. The method was validated quantitatively for 61 diffusion-weighted imaging (DWI), cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), peak height (PKHT), and time to peak (TPP) data sets of 11 stroke patients based on the ground truth provided by two raters. RESULTS: Average angular errors are less than 1 degrees for DWI and less than 2 degrees for CBF and CBV. Average distance errors measured in the worst case (on the brain's bounding box) are less than 2.5 mm for DWI and less than 5 mm for CBF and CBV. This algorithmic accuracy is at the level of interrater variability. Results obtained for the other perfusions maps (MTT, PKHT, TTP) were inferior; therefore, processing of CBF or CBV is preferred for accurate and robust calculation of the MSP from perfusion maps. Calculation of the MSP takes about half a second on a standard computer. CONCLUSIONS: The proposed method is near real-time and fully automatic, and neither user interaction nor parameter setting is needed. It does not require preprocessing of data. The method potentially is useful in rapid and automated processing of MR stroke diffusion and perfusion images.

Algorithms↗
PubMed2002

Computer vision and digital imaging technology in melanoma detection.

With today's treatment options, melanoma cure rates can be improved only if the diagnosis is made early enough to allow for curative surgery. Since clinical signs of malignancy in a pigmented lesion are often ambiguous and even dermatology experts may misdiagnose melanoma, diagnostic tools and procedures have been developed to assist the clinician in the diagnostic workup. Epiluminescence microscopy or dermatoscopy is widely used to inspect the melanin reticulum at the epidermo-dermal junction zone for signs indicative of early tumor growth. With the help of computer technology, digital dermatoscopy systems have entered the diagnostic arena capable of accurately assessing skin surface features modeled along the ABCD criteria already used for clinical assessment of pigmented skin lesions. Today's technically refined computer-based systems provide sophisticated functionalities for automated feature extraction and lesion assessment for quantitative analysis, and may also be used for education and training purposes.

Dermis↗
PubMed2003

Computer-aided classification of breast masses in ultrasonic B-scans using a multiparameter approach.

Classification of breast masses in ultrasonic B-scan images is undertaken using a multiparameter approach. The parameters are generated on the basis of a non-Rayleigh statistic model of the backscattered envelope from the breast tissue. They can be computed automatically with minimal clinical intervention once the location of the mass is known. A new discriminant is developed that combines these parameters linearly. It is seen that this new discriminant performs classification of masses into benign or malignant better than the classification by any one of the individual parameters. The data set studied consisted of 99 cases (70 patients with benign masses and 29 patients with malignant masses). The areas under the receiver operating characteristic (ROC) curves (Az) and statistical attributes of the areas were studied to establish the enhancement in performance. The Az value after combining all the parameters was found to be 0.8701. Upon combining this parameter with the level of suspicion (LOS) scores of a radiologist, the performance is further enhanced with an area under the (empirical) ROC of 0.94 having an operating point at a sensitivity of 0.965 and specificity of 0.87. It is suggested that this automated approach may hold promise as a means of classifying breast masses.

Algorithms↗
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↗
PubMed2006

Kernel Fisher discriminant for shape-based classification in epilepsy.

In this paper, we present the application of kernel Fisher discriminant in the statistical analysis of shape deformations that indicate the hemispheric location of an epileptic focus. The scans of two classes of patients with epilepsy, those with a right and those with a left anterior medial temporal lobe focus (RATL and LATL), as validated by clinical consensus and subsequent surgery, were compared to a set of age and sex matched healthy volunteers using both volume and shape based features. Shape-based features are derived from the displacement field characterizing the non-rigid deformation between the left and right hippocampi of a control or a patient as the case may be. Using the shape-based features, the results show a significant improvement in distinguishing between the controls and the rest (RATL and LATL) vis-a-vis volume-based features. Using a novel feature, namely, the normalized histogram of the 3D displacement field, we also achieved significant improvement over the volume-based feature in classifying the patients as belonging to either of the two classes LATL or RATL, respectively. It should be noted that automated identification of hemispherical foci of epilepsy has not been previously reported.

Algorithms↗
PubMed2005

PCP: a program for supervised classification of gene expression profiles.

UNLABELLED: PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance. Importantly, the implementation integrates gene selection and class prediction stages, which is vital for computing reliable performance estimates in small-sample scenarios. Additionally, the program includes automated and efficient model selection (optimization of parameters) for support vector machine (SVM) classifier. The distribution includes Linux and Windows/Cygwin binaries. The program can easily be ported to other platforms. AVAILABILITY: Free download at http://pcp.sourceforge.net

Algorithms↗
PubMed2005

New automated fluoroscopic systems for pediatric applications.

Pediatric patients are at higher risk to the adverse effects from exposure to ionizing radiation than adults. The smaller sizes of the anatomy and the reduced X-ray attenuation of the tissues provide special challenges. The goal of this effort is to investigate strategies for pediatric fluoroscopy in order to minimize the radiation exposure to these individuals, while maintaining effective diagnostic image quality. Modern fluoroscopy systems are often entirely automated and computer controlled. In this paper, various selectable and automated modes are examined to determine the influence of the fluoroscopy parameters upon the patient radiation exposures and image quality. These parameters include variable X-ray beam filters, automatic brightness control programs, starting kilovolt peak levels, fluoroscopic pulse rates, and other factors. Typical values of radiation exposure rates have been measured for a range of phantom thicknesses from 5 cm to 20 cm of acrylic. Other factors that have been assessed include spatial resolution, low contrast discrimination, and temporal resolution. The selection menu for various procedures is based upon the examination type, anatomical region, and patient size. For pediatric patients, the automated system can employ additional filtration, special automatic brightness control curves, pulsed fluoroscopy, and other features to reduce the patient radiation exposures without significantly compromising the image quality. The benefits gained from an optimal selection of automated programs and settings for fluoroscopy include ease of operation, better image quality, and lower patient radiation exposures.

Artificial Intelligence↗
PubMed2004

Diagnostic fidelity of the Z-buffer segmentation algorithm: preliminary assessment based on intracranial aneurysm detection.

We have developed an algorithm known as the Z-buffer segmentation (ZBS) algorithm for segmenting vascular structures from 3D MRA images. Previously we evaluated the accuracy of the ZBS algorithm on a voxel level in terms of inclusion and exclusion of vascular and background voxels. In this paper we evaluate the diagnostic fidelity of the ZBS algorithm. By diagnostic fidelity we mean that the data preserves the structural information necessary for diagnostic evaluation. This evaluation is necessary to establish the potential usefulness of the segmentation for improved image display, or whether the segmented data could form the basis of a computerized analysis tool. We assessed diagnostic fidelity by measuring how well human observers could detect aneurysms in the segmented data sets. ZBS segmentation of 30 MRA cases containing 29 aneurysms was performed. Image display used densitometric reprojections with shaded surface highlighting that were generated from the segmented data. Three neuroradiologists independently reviewed the generated ZBS images for aneurysms. The observers had 80% sensitivity (90% for aneurysms larger than 2mm) with 0.13 false positives per image. Good agreement with the gold standard for describing aneurysm size and orientation was shown. These preliminary results suggest that the segmentation has diagnostic fidelity with the original data and may be useful for improved visualization or automated analysis of the vasculature.

Algorithms↗
PubMed2002

Finding relevant references to genes and proteins in Medline using a Bayesian approach.

MOTIVATION: Mining the biomedical literature for references to genes and proteins always involves a tradeoff between high precision with false negatives, and high recall with false positives. Having a reliable method for assessing the relevance of literature mining results is crucial to finding ways to balance precision and recall, and for subsequently building automated systems to analyze these results. We hypothesize that abstracts and titles that discuss the same gene or protein use similar words. To validate this hypothesis, we built a dictionary- and rule-based system to mine Medline for references to genes and proteins, and used a Bayesian metric for scoring the relevance of each reference assignment. RESULTS: We analyzed the entire set of Medline records from 1966 to late 2001, and scored each gene and protein reference using a Bayesian estimated probability (EP) based on word frequency in a training set of 137837 known assignments from 30594 articles to 36197 gene and protein symbols. Two test sets of 148 and 150 randomly chosen assignments, respectively, were hand-validated and categorized as either good or bad. The distributions of EP values, when plotted on a log-scale histogram, are shown to markedly differ between good and bad assignments. Using EP values, recall was 100% at 61% precision (EP=2 x 10(-5)), 63% at 88% precision (EP=0.008), and 10% at 100% precision (EP=0.1). These results show that Medline entries discussing the same gene or protein have similar word usage, and that our method of assessing this similarity using EP values is valid, and enables an EP cutoff value to be determined that accurately and reproducibly balances precision and recall, allowing automated analysis of literature mining results. .

Abstracting and Indexing↗
PubMed2004

APART: automated preprocessing for NMR assignments with reduced tedium.

MOTIVATION: High-throughput NMR structure determination is a goal that will require progress on many fronts, one of which is rapid resonance assignment. An important rate-limiting step in the resonance assignment process is accurate identification of resonance peaks in the NMR spectra. Peak-picking schemes range from incomplete (which lose essential assignment connectivities) to noisy (which obscure true connectivities with many false ones). We introduce an automated preassignment process that removes false peaks from noisy peak lists by requiring consensus between multiple NMR experiments and exploiting a priori information about NMR spectra. This process is designed to accept multiple input formats and generate multiple output formats, in an effort to be compatible with a variety of user preferences. RESULTS: Automated preprocessing with APART rapidly identifies and removes false peaks from initial peak lists, reduces the burden of manual data entry, and documents and standardizes the peak filtering process. Successful preprocessing is demonstrated by the increased number of correct assignments obtained when data are submitted to an automated assignment program. AVAILABILITY: APART is available from http://sir.lanl.gov/NMR/APART.htm CONTACT: npawley@lanl.gov; rmichalczyk@lanl.gov SUPPLEMENTARY INFORMATION: Manual pages with installation instructions, procedures and screen shots can also be found at http://sir.lanl.gov/NMR/APART_Manual1.pdf.

Algorithms↗
PubMed2004

Methods for multi-category cancer diagnosis from gene expression data: a comprehensive evaluation to inform decision support system development.

Cancer diagnosis is a major clinical applications area of gene expression microarray technology. We are seeking to develop a system for cancer diagnostic model creation based on microarray data. In order to equip the system with the optimal combination of data modeling methods, we performed a comprehensive evaluation of several major classification algorithms, gene selection methods, and cross-validation designs using 11 datasets spanning 74 diagnostic categories (41 cancer types and 12 normal tissue types). The Multi-Category Support Vector Machine techniques by Crammer and Singer, Weston and Watkins, and one-versus-rest were found to be the best methods and they outperform other learning algorithms such as K-Nearest Neighbors and Neural Networks often to a remarkable degree. Gene selection techniques are shown to significantly improve classification performance. These results guided the development of a software system that fully automates cancer diagnostic model construction with quality on par with or better than previously published results derived by expert human analysts.

Algorithms↗
PubMed2003

Localization and segmentation of aortic endografts using marker detection.

A method for localization and segmentation of bifurcated aortic endografts in computed tomographic angiography (CTA) images is presented. The graft position is determined by detecting radiopaque markers sewn on the outside of the graft. The user indicates the first and the last marker, whereupon the remaining markers are automatically detected. This is achieved by first detecting marker-like structures through second-order scaled derivative analysis, which is combined with prior knowledge of graft shape and marker configuration. The identified marker centers approximate the graft sides and, derived from these, the central axis. The graft boundary is determined by maximizing the local gradient in the radial direction along a deformable contour passing through both sides. Three segmentation methods were tested. The first performs graft contour detection in the initial CT-slices, the second in slices that were reformatted to be orthogonal to the approximated graft axis, and the third uses the segmentation from the second method to find a more reliable approximation of the axis and subsequently performs contour detection. The methods have been applied to ten CTA images and the results were compared to manual marker indication by one observer and region growing aided segmentation by three observers. Out of a total of 266 markers, 262 were detected. Adequate approximations of the graft sides were obtained in all cases. The best segmentation results were obtained using a second iteration orthogonal to the axis determined from the first segmentation, yielding an average relative volume of overlap with the expert segmentations of 92%, while the interexpert reproducibility is 95%. The averaged difference in volume measured by the automated method and by the experts equals the difference among the experts: 3.5%.

Anatomy, Cross-Sectional↗
PubMed2006

Fully automated system for three-dimensional bronchial morphology analysis using volumetric multidetector computed tomography of the chest.

Recent advancements in computed tomography (CT) have enabled quantitative assessment of severity and progression of large airway damage in chronic pulmonary disease. The advent of fast multidetector computed tomography scanning has allowed the acquisition of rapid, low-dose 3D volumetric pulmonary scans that depict the bronchial tree in great detail. Volumetric CT allows quantitative indices of bronchial airway morphology to be calculated, including airway diameters, wall thicknesses, wall area, airway segment lengths, airway taper indices, and airway branching patterns. However, the complexity and size of the bronchial tree render manual measurement methods impractical and inaccurate. We have developed an integrated software package utilizing a new measurement algorithm termed mirror-image Gaussian fit that enables the user to perform automated bronchial segmentation, measurement, and database archiving of the bronchial morphology in high resolution and volumetric CT scans and also allows 3D localization, visualization, and registration.

Algorithms↗
PubMed2004

Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT.

The objective of this work was to develop and validate algorithms for detection and classification of hypodense hepatic lesions, specifically cysts, hemangiomas, and metastases from CT scans in the portal venous phase of enhancement. Fifty-six CT sections from 51 patients were used as representative of common hypodense liver lesions, including 22 simple cysts, 11 hemangiomas, 22 metastases, and 1 image containing both a cyst and a hemangioma. The detection algorithm uses intensity-based histogram methods to find central lesions, followed by liver contour refinement to identify peripheral lesions. The classification algorithm operates on the focal lesions identified during detection, and includes shape-based segmentation, edge pixel weighting, and lesion texture filtering. Support vector machines are then used to perform a pair-wise lesion classification. For the detection algorithm, 80% lesion sensitivity was achieved at approximately 0.3 false positives (FP) per slice for central lesions, and 0.5 FP per slice for peripheral lesions, giving a total of 0.8 FP per section. For 90% sensitivity, the total number of FP rises to about 2.2 per section. The pair-wise classification yielded good discrimination between cysts and metastases (at 95% sensitivity for detection of metastases, only about 5% of cysts are incorrectly classified as metastases), perfect discrimination between hemangiomas and cysts, and was least accurate in discriminating between hemangiomas and metastases (at 90% sensitivity for detection of hemangiomas, about 28% of metastases were incorrectly classified as hemangiomas). Initial implementations of our algorithms are promising for automating liver lesion detection and classification.

Algorithms↗
PubMed1979

Cytomorphologic results of preparation experiments for monolayer deposition of cervical material.

For automated prescreening methods by high resolution analysis serving as a detecting method in gynecologic mass screening programs a new monolayer deposition method of cervical material has been used. This method will be outlines briefly and the mode of evaluation as well as current cytomorphological findings will be presented. With regard to measurability of the slides prepared according to the new method a number of cytologic criteria were thought to be of particular importance. These criteria are delineated and compiled in a table. A form in which these criteria were listed was filled in by cytopathologists for each slide evaluated. When performing isolation and centrifugation procedures several new morphologic questions arose to the cytopathologist which can only partly be answered by now. If taking into account all criteria of evaluation it may be followed from the present experiences that slides of cervical material are much more suited for automated prescreening methods by high resolution analysis if prepared after isolation and centrifugation in macromolecular liquids than are conventional Papanicolaou smears or slides from suspensions with isolated cells that were not subjected to centrifugation procedures.

Cell Separation↗
PubMed1992

Computer-assisted pattern recognition model for the identification of slowly growing mycobacteria including Mycobacterium tuberculosis.

We present a computerized pattern recognition model used to speciate mycobacteria based on their restriction fragment length polymorphism (RFLP) banding patterns. DNA fragment migration distances were normalized to minimize lane-to-lane variability of band location both within and among gels through the inclusion of two internal size standards in each sample. The computer model used a library of normalized RFLP patterns derived from samples of known origin to create a probability matrix which was then used to classify the RFLP patterns from samples of unknown origin. The probability matrix contained the proportion of bands that fell within defined migration distance windows for each species in the library of reference samples. These proportions were then used to compute the likelihood that the banding pattern of an unknown sample corresponded to that of each species represented in the probability matrix. As a test of this process, we developed an automated, computer-assisted model for the identification of Mycobacterium species based on their normalized RFLP banding patterns. The probability matrix contained values for the M. tuberculosis complex, M. avium, M. intracellulare, M. kansasii and M. gordonae species. Thirty-nine independent strains of known origin, not included in the probability matrix, were used to test the accuracy of the method in classifying unknowns: 37 of 39 (94.9%) were classified correctly. An additional set of 16 strains of known origin representing species not included in the model were tested to gauge the robustness of the probability matrix. Every sample was correctly identified as an outlier, i.e. a member of a species not included in the original matrix.(ABSTRACT TRUNCATED AT 250 WORDS)

DNA, Bacterial↗
PubMed2003

Automated discrimination of polymerase chain reaction products with closely related sequences by software-based detection of characteristic peaks in product ion spectra.

A computer-based method is described for automated detection of peaks in product ion spectra that allows discrimination of structurally related polymerase chain reaction (PCR) products. PCR products of K-ras mutants having single nucleotide substitutions and isomeric sequence changes in positions 1 and 2 of codon 12 (e.g. TGT and GTT) were used as a model system. SpecDiff, a tool for differentiating pairs of mass spectra by identifying peaks that either differ in relative intensity between spectra or only appear in one of a pair of spectra, was created to help automate detection. This program was demonstrated to have great utility in detection of mutations and could also be useful as a general tool for differentiating other molecules of closely related structure.

Algorithms↗