Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Pattern Recognition, Automated”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30Linked to original sources
PubMed2004

Parametric shape modeling using deformable superellipses for prostate segmentation.

Automatic prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing boundary segments, and complex prostate anatomy. One popular approach has been the use of deformable models. For such techniques, prior knowledge of the prostate shape plays an important role in automating model initialization and constraining model evolution. In this paper, we have modeled the prostate shape using deformable superellipses. This model was fitted to 594 manual prostate contours outlined by five experts. We found that the superellipse with simple parametric deformations can efficiently model the prostate shape with the Hausdorff distance error (model versus manual outline) of 1.32 +/- 0.62 mm and mean absolute distance error of 0.54 +/- 0.20 mm. The variability between the manual outlinings and their corresponding fitted deformable superellipses was significantly less than the variability between human experts with p-value being less than 0.0001. Based on this deformable superellipse model, we have developed an efficient and robust Bayesian segmentation algorithm. This algorithm was applied to 125 prostate ultrasound images collected from 16 patients. The mean error between the computer-generated boundaries and the manual outlinings was 1.36 +/- 0.58 mm, which is significantly less than the manual interobserver distances. The algorithm was also shown to be fairly insensitive to the choice of the initial curve.

Algorithms↗
PubMed2004

Automatic segmentation and plaque characterization in atherosclerotic carotid artery MR images.

In vivo MRI provides a means to non-invasively image and assess the morphological features of atherosclerotic carotid arteries. To assess quantitatively the degree of vulnerability and the type of plaque, the contours of the lumen, outer boundary of the vessel wall and plaque components, need to be traced. Currently this is done manually, which is time-consuming and sensitive to inter- and intra-observer variability. The goal of this work was to develop an automated contour detection technique for tracing the lumen, outer boundary and plaque contours in carotid MR short-axis black-blood images. Seventeen patients with carotid atherosclerosis were imaged using high-resolution in vivo MRI, generating a total of 50 PD- and T1-weighted MR images. These images were automatically segmented using the algorithm presented in this work, which combines model-based segmentation and fuzzy clustering to detect the vessel wall, lumen and lipid core boundaries. The results demonstrate excellent correspondence between automatic and manual area measurements for lumen (r = 0.92) and outer (r = 0.91), and acceptable correspondence for fibrous cap thickness (r = 0.71). Though further optimization is required, our algorithm is a powerful tool for automatic detection of lumen and outer boundaries, and characterization of plaque in atherosclerotic vessels.

Aged↗
PubMed2006

Projection profile analysis for automated detection of abnormalities in chest radiographs.

Abnormalities in chest images often present as abnormal opacity or abnormal asymmetry. We have developed a novel method for automated detection of abnormalities in chest radiographs by use of these features. Our method is based on an analysis of the projection profile obtained by projecting the pixels data of a frontal chest image on to the mediolateral axis. Two indices, lung opacity index and lung symmetry index, are computed from the projection profile. Lung opacity index and lung symmetry index are then combined to detect gross abnormalities in chest radiographs. The values of lung opacity index are found to be 0.38 +/- 0.05 and 0.37 +/- 0.06 for normal right and left lung, respectively. The values of lung symmetry index are found to be 0.018 +/- 0.014 for normal chest images. The discrimination for the combination of the two indices is evaluated by linear discriminant analysis and receiver operating characteristic (ROC) analysis. Area Az under the ROC curve with the combination of the two indices in the classification of normal and abnormal chest images is 0.963.

Adolescent↗
PubMed2006

A novel respiratory detection method based on automated analysis of ultrasound diaphragm video.

This paper proposes a novel respiratory detection method based on diaphragm motion measurements using a 2D ultrasound unit. The proposed method extracts a respiratory signal from an automated analysis of the internal diaphragm motion during breathing. The respiratory signal may be used for gating. Ultrasound studies of diaphragm breathing motion were performed on four volunteers. The ultrasound video stream was captured and transferred to a personal computer and decomposed into individual image frames. After straightforward image analysis, region of interest selection, and filtering, the mutual information (MI) and correlation coefficients (CCs) between a selected reference frame and all other frames were computed. The resulting MI and CC values were discovered to produce a signal corresponding to the respiratory cycle in both phase and magnitude. We also studied the diaphragm motion of two volunteers during repeated deep inspiration breath holds (DIBH) and found a slight relaxation motion of the diaphragm during the DIBH, suggesting that the residual motion may be important for treatments delivered at this breathing phase. Applying the proposed respiratory detection method to these ultrasound studies, we found that the MI and CC values demonstrate the relaxation behavior, indicatingthat our method may be used to determine the radiation triggering time for a DIBH technique.

Algorithms↗
PubMed2006

3-D brain segmentation towards the integration of DTI and MRI modalities.

This study introduces a 3-D segmentation method together with a graphical user interface (GUI) as means to effectively automate the process of segmentation with the ultimate objective of integrating and visualizing diffusion tensor imaging (DTI) with magnetic resonance imaging (MRI) in a fully automated 3-D brain imaging system. A secondary objective is to reduce significantly the segmentation time required to extract key landmarks of the brain in contrast to the manual process currently used at many hospital settings. The results provided will prove this important assertion. The inter-correlation coefficient revealed 96.1% accuracy in segmenting all of the processed data, which consequently led to effective registration of the DTI and MRI modalities since they involve the same landmarks. The average speed of segmentation was just 35 seconds, a reduction of over 20 times of what is required for manual segmentation. In order to create a highly integrated interface, the segmentation results serve as input to a registration algorithm we are currently investigating and whose preliminary results support the significance of relying on an effective segmentation process. T1-weighted 3D Gradient Echo MR and DT images from 16 patients at Miami Children's Hospital were used for evaluation purposes.

Algorithms↗
PubMedDate not supplied

An example of abnormal glow curves identification in personnel thermoluminescent dosimetry.

The personal Dosimetry Service Seibersdorf analyses monthly a large number of thermoluminescent dosimeters (TLD). The dosimeters consist of LiF chips, and the readout is carried out with an automated Harshaw 8800 reader system. In some cases, the luminescent glow curves of the routine analysis do not have the expected form as a result of external chemical contamination, hardware problems, poor heat transfer, etc. It is therefore necessary to investigate the reasons for the irregularity of these curves. An algorithm for the investigation of the routine curves was developed. It is based on the fact that the shape of an abnormal glow curve differs from the shape of a normal one. An interesting type of abnormal glow curves in the routine service was found. Some dosimeters of a certain client, a steel industry, exhibit glow curves with an atypical shape and very high signals. In those dosimeters, a possible chemical contamination in the form of a powder was discovered, which interferes with the dosimetric signal. A quantitative analysis of that powder was made by means of inductively coupled plasma emission spectroscopy (ICP-OES) after microwave dissolution. Elements like aluminium, barium, calcium and others were found. Such elements are used in different combinations as thermoluminescent materials.

Algorithms↗
PubMed2006

MACGT: multi-dimensional automated clustering genotyping tool for analysis of microarray-based mini-sequencing data.

SUMMARY: Multi-dimensional Automated Clustering Genotyping Tool (MACGT) is a Java application that clusters complex multi-dimensional vector data derived from single nucleotide polymorphism (SNP) genotyping experiments using mini-sequencing based microarray chemistries such as arrayed primer extension (APEX). Spot intensity output files from microarray experiments across multiple samples are imported into MACGT. The datasets can include four channels of intensity data for each spot, replica spots for each SNP probe and multiple probe types (APEX and allele-specific APEX probes) on both DNA strands for each SNP. MACGT automatically clusters these multi-dimensionality datasets for each SNP across multiple samples. Incorporation of additional array datasets from known samples that have previously validated SNP genotype calls allows unknown samples to be automatically assigned a genotype based on the clustering, along with numerical measures of confidence for each genotype call. Calling accuracy by MACGT exceeds 98% when applied to genotyping data from APEX microarrays, and can be increased to >99.5% by applying thresholds to the confidence measures.

Algorithms↗
PubMed2005

Subspace-based prototyping and classification of chromosome images.

Chromosomes are essential genomic information carriers. Chromosome classification constitutes an important part of routine clinical and cancer cytogenetics analysis. Cytogeneticists perform visual interpretation of banded chromosome images according to the diagrammatic models of various chromosome types known as the ideograms, which mimic artists' depiction of the chromosomes. In this paper, we present a subspace-based approach for automated prototyping and classification of chromosome images. We show that 1) prototype chromosome images can be quantitatively synthesized from a subspace to objectively represent the chromosome images of a given type or population, and 2) the transformation coefficients (or projected coordinate values of sample chromosomes) in the subspace can be utilized as the extracted feature measurements for classification purposes. We examine in particular the formation of three well-known subspaces, namely the ones derived from principal component analysis (PCA), Fisher's linear discriminant analysis, and the discrete cosine transform (DCT). These subspaces are implemented and evaluated for prototyping two-dimensional (2-D) images and for classification of both 2-D images and one-dimensional profiles of chromosomes. Experimental results show that previously unseen prototype chromosome images of high visual quality can be synthesized using the proposed subspace-based method, and that PCA and the DCT significantly outperform the well-known benchmark technique of weighted density distribution functions in classifying 2-D chromosome images.

Algorithms↗
PubMed2004

Interactive 3D editing tools for image segmentation.

Segmentation is an important part of image processing, which often has a large impact on quantitative image analysis results. Fully automated operator independent segmentation procedures that successfully work in a population with a larger biological variation are extremely difficult to design and usually some kind of operator intervention is required, at least in pathological cases. We developed a variety of 3D editing tools that can be used to correct or improve results of initial automatic segmentation procedures. Specifically we will discuss and show examples for three types of editing tools that we termed: hole-filling (tool 1), point-bridging (tool 2), and surface-dragging (tool 3). Each tool comes in a number of flavors, all of which are implemented in a truly 3D manner. We describe the principles, evaluate efficiency and flexibility, and discuss advantages and disadvantages of each tool. We further demonstrate the superiority of the 3D approach over the time-consuming slice-by-slice editing of 3D datasets, which is still widely used in medical image processing today. We conclude that performance criteria for automatic segmentation algorithms may be eased significantly by including 3D editing tools early in the design process.

Algorithms↗
PubMedDate not supplied

A hill-climbing approach for automatic gridding of cDNA microarray images.

Image and statistical analysis are two important stages of cDNA microarrays. Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment used to print the arrays, rotations, misalignments, high contamination with noise and artifacts, and the enormous amount of data generated, solving the gridding problem by means of an automatic system is not trivial. Existing techniques to solve the automatic grid segmentation problem cover only limited aspects of this challenging problem and require the user to specify the size of the spots, the number of rows and columns in the grid, and boundary conditions. In this paper, a hill-climbing automatic gridding and spot quantification technique is proposed which takes a microarray image (or a subgrid) as input and makes no assumptions about the size of the spots, rows, and columns in the grid. The proposed method is based on a hill-climbing approach that utilizes different objective functions. The method has been found to effectively detect the grids on microarray images drawn from databases from GEO and the Stanford genomic laboratories.

Algorithms↗
PubMed2004

Gridline: automatic grid alignment in DNA microarray scans.

We present a new automatic grid alignment algorithm for detecting two-dimensional (2-D) arrays of spots in DNA microarray images. Our motivation for this work is the lack of automation in high-throughput microarray data analysis that leads to a) spatial inaccuracy of located spots and hence inaccuracy of extracted information from a spot and b) inconsistency of extracted features due to manual selection of grid alignment parameters. The proposed grid alignment algorithm is novel in the sense that 1) it can detect irregularly row- and column-spaced spots in a 2-D array, 2) it is independent of spot color and size, 3) it is general to localize a grid of other primitive shapes than the spot shapes, 4) it can perform grid alignment on any number of input channels, 5) it reduces the number of free parameters to minimum by data driven optimization of most algorithmic parameters, and 6) it has a built-in speed versus accuracy tradeoff mechanism to accommodate user's requirements on performance time and accuracy of the results. The developed algorithm also automatically identifies multiple blocks of 2-D arrays, as it is the case in microarray images, and compensates for grid rotations in addition to grid translations.

Algorithms↗
PubMed2005

Automated atlas-based clustering of white matter fiber tracts from DTMRI.

A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundles of fiber tracts in the human brain is desired. To include the anatomical knowledge in the clustering, we invoke an atlas of fiber tracts, labeled by the number of bundles of interest. In this work, we construct such an atlas and use it to cluster all fiber tracts in the white matter. To build the atlas, we start with a set of labeled ROIs specified by an expert and extract the fiber tracts initiating from each ROI. Affine registration is used to project the extracted fiber tracts of each subject to the atlas, whereas their B-spline representation is used to efficiently compare them to the fiber tracts in the atlas and assign cluster labels. Expert visual inspection of the result confirms that the proposed method is very promising and efficient in clustering of the known bundles of fiber tracts.

Algorithms↗
PubMed2004

Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis.

Cardiac beat classification is a key process in the detection of myocardial ischemic episodes in the electrocardiographic signal. In the present study, we propose a multicriteria sorting method for classifying the cardiac beats as ischemic or not. Through a supervised learning procedure, each beat is compared to preclassified category prototypes under five criteria. These criteria refer to ST segment changes, T wave alterations, and the patient's age. The difficulty in applying the above criteria is the determination of the required method parameters, namely the thresholds and weight values. To overcome this problem, we employed a genetic algorithm, which, after proper training, automatically calculates the optimum values for the above parameters. A task-specific cardiac beat database was developed for training and testing the proposed method using data from the European Society of Cardiology ST-T database. Various experimental tests were carried out in order to adjust each module of the classification system. The obtained performance was 91% in terms of both sensitivity and specificity and compares favorably to other beat classification approaches proposed in the literature.

Age Factors↗
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

Decision tree based information integration for automated protein classification.

We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. We achieve accurate classification by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique, based on decision trees, is rooted in machine learning which shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components. We use two sequence- and three structure-comparison tools as component classifiers. Given a protein structure and using the joint hypothesis, we first determine if the protein belongs to an existing category (family, superfamily, fold) in the SCOP hierarchy. For the proteins that are predicted as members of the existing categories, we compute their family-, superfamily-, and fold-level classifications using the consensus classifier. We show that we can significantly improve the classification accuracy compared to the individual component classifiers. In particular, we achieve error rates that are 3-12 times less than the individual classifiers' error rates at the family level, 1.5-4.5 times less at the superfamily level, and 1.1-2.4 times less at the fold level.

Algorithms↗
PubMed2006

A framework for protein structure classification and identification of novel protein structures.

BACKGROUND: Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important. RESULTS: In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds. CONCLUSION: We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available at http://www.eecs.umich.edu/periscope/procc.

Artificial Intelligence↗
PubMed2005

Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience.

The purpose of this study was to develop an automated scheme to facilitate detection of localized ground-glass opacity (GGO) in the lung at computed tomography (CT). Institutional review board approval and informed consent were not required. Two radiologists reviewed CT images from 14 patients (five men, nine women) who had lung cancer or metastasis and whose malignancy was classified as GGO. The lung region was sampled and completely covered with contiguous, 50% overlapping regions of interest (ROIs) measuring 30 x 30 pixels in size. The lung area within each ROI was analyzed to compute texture features and gaussian curve fitting features. Performance of the artificial neural networks (ANNs) measured by using the area under the receiver operating characteristic curve was 0.92. With a threshold of 0.9, the sensitivity of the ANN for detecting GGO ROIs was 94.3% (280 of 297 ROIs), and the positive predictive value was 29.1% (280 of 963 ROIs). A computerized scheme may hold promise in facilitating detection of localized GGO at CT.

Aged↗
PubMed2005

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

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

Algorithms↗