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PubMed2004

Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations.

BACKGROUND: Modern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns. RESULTS: The effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches. CONCLUSIONS: We conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns.

Algorithms↗
PubMed2000

Computer-aided diagnosis of small pulmonary nodules.

Computer-aided methods are now being developed for the detection and characterization of pulmonary nodules found in CT images, based on techniques from computer vision, image processing, and pattern classification. With the increasing resolution of modern CT scanners, computer methods provide continually improving accuracy, reproducibility, and utility in analyzing the larger numbers of images acquired in a lung screening exam or diagnostic study. This article describes the fundamental tools and issues involved in computer-aided nodule detection and characterization, as we move from two-dimensional toward three-dimensional automated methods. In particular, we focus on the new domain of "small" pulmonary nodules.

Artificial Intelligence↗
PubMed2006

Structure from planar motion.

Planar motion is arguably the most dominant type of motion in surveillance videos. The constraints on motion lead to a simplified factorization method for structure from planar motion when using a stationary perspective camera. Compared with methods for general motion, our approach has two major advantages: a measurement matrix that fully exploits the motion constraints is formed such that the new measurement matrix has a rank of at most 3, instead of 4; the measurement matrix needs similar scalings, but the estimation of fundamental matrices or epipoles is not needed. Experimental results show that the algorithm is accurate and fairly robust to noise and inaccurate calibration. As the new measurement matrix is a nonlinear function of the observed variables, a different method is introduced to deal with the directional uncertainty in the observed variables. Differences and the dual relationship between planar motion and planar object are also clarified. Based on our method, a fully automated vehicle reconstruction system has been designed.

Algorithms↗
PubMed2002

Tracking leukocytes in vivo with shape and size constrained active contours.

Inflammatory disease is initiated by leukocytes (white blood cells) rolling along the inner surface lining of small blood vessels called postcapillary venules. Studying the number and velocity of rolling leukocytes is essential to understanding and successfully treating inflammatory diseases. Potential inhibitors of leukocyte recruitment can be screened by leukocyte rolling assays and successful inhibitors validated by intravital microscopy. In this paper, we present an active contour or snake-based technique to automatically track the movement of the leukocytes. The novelty of the proposed method lies in the energy functional that constrains the shape and size of the active contour. This paper introduces a significant enhancement over existing gradient-based snakes in the form of a modified gradient vector flow. Using the gradient vector flow, we can track leukocytes rolling at high speeds that are not amenable to tracking with the existing edge-based techniques. We also propose a new energy-based implicit sampling method of the points on the active contour that replaces the computationally expensive explicit method. To enhance the performance of this shape and size constrained snake model, we have coupled it with Kalman filter so that during coasting (when the leukocytes are completely occluded or obscured), the tracker may infer the location of the center of the leukocyte. Finally, we have compared the performance of the proposed snake tracker with that of the correlation and centroid-based trackers. The proposed snake tracker results in superior performance measures, such as reduced error in locating the leukocyte under tracking and improvements in the percentage of frames successfully tracked. For screening and drug validation, the tracker shows promise as an automated data collection tool.

Algorithms↗
PubMed1995

Extracting knowledge from large medical databases: an automated approach.

Tools which can uncover patterns in patients' records and then make predictions based on that knowledge are and will continue to be high priority in many medical informatics groups. These tools are impacting the performance of outcome studies by discovering patterns which can then be verified with standard statistical tools. This paper demonstrates INC2.5, a general classification system, as a tool for assisting physicians in the decision making process. INC2.5 gathers information from patient records and builds a decision tree which is used to assist physicians in predicting the outcome of new patients. The decision tree will also reveal any patterns which the system found in the data. Successful results of such a system can be used to enhance outcome studies as well as to spread clinical information to areas with fewer resources.

Algorithms↗
PubMed1998

Automated detection of breast carcinomas not detected in a screening program.

PURPOSE: To investigate the possibility of automated detection of early signs of cancer that were not detected in a breast cancer screening program. MATERIALS AND METHODS: A set of 75 mammograms (in 65 women) with subtle circumscribed masses, stellate lesions, and architectural distortions that were not detected in a screening program by two radiologists was assembled and extended with 142 normal mammograms (contralateral mammograms in the same 65 women). An automated system for the detection of circumscribed masses and stellate lesions was applied to this set. RESULTS: In 22 (34%) of 65 cases, an early sign of cancer was detected at a specificity of one false-positive finding per image. At a specificity of three false-positive findings per image, 39 (60%) of the cancers were detected. Of the tumors that were classified as screening errors, seven (50%) were found at a specificity of 0.5 false-positive finding per image. CONCLUSION: A substantial proportion of cancers that were missed in a screening program, despite double reading, were found with this detection method at less than one false-positive finding per image.

Algorithms↗
PubMed2004

Automatic classification of heartbeats using ECG morphology and heartbeat interval features.

A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50,000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.

Algorithms↗
PubMed1997

Multiresolution statistical analysis of high-resolution digital mammograms.

A multiresolution statistical method for identifying clinically normal tissue in digitized mammograms is used to construct an algorithm for separating normal regions from potentially abnormal regions; that is, small regions that may contain isolated calcifications. This is the initial phase of the development of a general method for the automatic recognition of normal mammograms. The first step is to decompose the image with a wavelet expansion that yields a sum of independent images, each containing different levels of image detail. When calcifications are present, there is strong empirical evidence that only some of the image components are necessary for the purpose of detecting a deviation from normal. The underlying statistic for each of the selected expansion components can be modeled with a simple parametric probability distribution function. This function serves as an instrument for the development of a statistical test that allows for the recognition of normal tissue regions. The distribution function depends on only one parameter, and this parameter itself has an underlying statistical distribution. The values of this parameter define a summary statistic that can be used to set detection error rates. Once the summary statistic is determined, spatial filters that are matched to resolution are applied independently to each selected expansion image. Regions of the image that correlate with the normal statistical model are discarded and regions in disagreement (suspicious areas) are flagged. These results are combined to produce a detection output image consisting only of suspicious areas. This type of detection output is amenable to further processing that may ultimately lead to a fully automated algorithm for the identification of normal mammograms.

Algorithms↗
PubMed2006

Robust measures of three-dimensional vascular tortuosity based on the minimum curvature of approximating polynomial spline fits to the vessel mid-line.

The clinical recognition of abnormal vascular tortuosity is important in the diagnosis of many diseases. This paper presents a novel approach to the quantification of vascular tortuosity, using robust metrics based on unit speed parameterizations of three-dimensional (3D) curvature. The use of approximating polynomial spline-fitting obviates the need for arbitrary filtering of mid-line data which is necessary with other tortuosity indices. The metrics were tested using both two-dimensional and three-dimensional synthesized images that mimicked clinically significant pathologies: two of the three metrics were scale invariant, additive, and produced tortuosity values tailored to be independent of the resolution of the imaging system. Our methodology is designed to explicitly handle the challenge of noisy data, and is largely tolerant of the inaccuracies in the mid-line extraction. While all the proposed metrics are sensitive to gently curved vessels, the rms curvature of the smoothest path was more effective in recognizing abnormalities involving high-frequency coiling such as occurs in malignant tumors. We have also indicated how values from two projection images, such as acquired in biplane angiography, can be combined to give an accurate approximation of the 3D value of this metric. Our proposed methodology is well-suited to automated detection and measurement, which are a prerequisite for clinical implementation.

Algorithms↗
PubMed2003

Autonomous system for Web-based microarray image analysis.

Software-based feature extraction from DNA microarray images still requires human intervention on various levels. Manual adjustment of grid and metagrid parameters, precise alignment of superimposed grid templates and gene spots, or simply identification of large-scale artifacts have to be performed beforehand to reliably analyze DNA signals and correctly quantify their expression values. Ideally, a Web-based system with input solely confined to a single microarray image and a data table as output containing measurements for all gene spots would directly transform raw image data into abstracted gene expression tables. Sophisticated algorithms with advanced procedures for iterative correction function can overcome imminent challenges in image processing. Herein is introduced an integrated software system with a Java-based interface on the client side that allows for decentralized access and furthermore enables the scientist to instantly employ the most updated software version at any given time. This software tool is extended from PixClust as used in Extractiff incorporated with Java Web Start deployment technology. Ultimately, this setup is destined for high-throughput pipelines in genome-wide medical diagnostics labs or microarray core facilities aimed at providing fully automated service to its users.

Algorithms↗
PubMed2006

Automated detection of lung nodules in CT scans: false-positive reduction with the radial-gradient index.

We present a number of approaches based on the radial gradient index (RGI) to achieve false-positive reduction in automated CT lung nodule detection. A database of 38 cases was used that contained a total of 82 lung nodules. For each CT section, a complementary image known as an "RGI map" was constructed to enhance regions of high circularity and thus improve the contrast between nodules and normal anatomy. Thresholds on three RGI parameters were varied to construct RGI filters that sensitively eliminated false-positive structures. In a consistency approach, RGI filtering eliminated 36% of the false-positive structures detected by the automated method without the loss of any true positives. Use of an RGI filter prior to a linear discriminant classifier yielded notable improvements in performance, with the false-positive rate at a sensitivity of 70% being reduced from 0.5 to 0.28 per section. Finally, the performance of the linear discriminant classifier was evaluated with RGI-based features. RGI-based features achieved a substantial improvement in overall performance, with a 94.8% reduction in the false-positive rate at a fixed sensitivity of 70%. These results demonstrate the potential role of RGI analysis in an automated lung nodule detection method.

Algorithms↗
PubMedDate not supplied

Automatic identification of biological microorganisms using three-dimensional complex morphology.

We propose automated identification of microorganisms using three-dimensional (3-D) complex morphology. This 3-D complex morphology pattern includes the complex amplitude (magnitude and phase) of computationally reconstructed holographic images at arbitrary depths. Microscope-based single-exposure on-line (SEOL) digital holography records and reconstructs holographic images of the biological microorganisms. The 3-D automatic recognition is processed by segmentation, feature extraction by Gabor-based wavelets, automatic feature vector selection by graph matching, training rules, and a decision process. Graph matching combined with Gabor feature vectors measures the similarity of complex geometrical shapes between a reference microorganism and unknown biological samples. Automatic selection of the training data is proposed to achieve a fully automatic recognition system. Preliminary experimental results are presented for 3-D image recognition of Sphacelaria alga and Tribonema aequale alga.

Algorithms↗
PubMedDate not supplied

Pattern recognition in health insurance claims databases.

Information in claims databases resides in data patterns rather than in data elements. Finding this information requires new terminology, a willingness to pose questions of form rather than specific hypotheses, and a quality control system that elevates the correctness of data relations above the validity of single facts. The language of claims data is a newspeak of CPT (Current Procedural Terminology), HCPCS (Health Care Financing Agency Common Procedure Coding System), ICD (International Classification of Disease), and NDC (National Drug Codes) for pharmaceutical codes. The techniques of pattern discovery are really ways of asking the data for classes of relations, and they vary in their reliance on external information. Sometimes, the question is entirely constrained by preceding factors. Other times we may recast the natural history of disease into a claims context and ask the data to give us the shape of disease evolution. We can use highly automated systems to evaluate the relations between prespecified factors, or empirical techniques to search out common relations that we have not specified in advance. Using massive data sets requires that quality control corresponds to the nature of the high-level information that we derive from large databases.

Databases as Topic↗
PubMed2006

Pulmonary nodule detection in CT images with quantized convergence index filter.

A novel filter termed quantized convergence index filter (QCI filter) that is capable of enhancing the conspicuity of rounded lesions is proposed as part of a CAD (computer-aided diagnosis) scheme for detecting pulmonary nodules in computed tomography (CT) images. In this filter and its predecessor, the convergence index filter (CI filter), the output at a pixel represents the degree of convergence toward the pixel shown by the directions of gray-level gradients at surrounding pixels. The QCI filter and the CAD scheme were evaluated using five clinical datasets containing 50 nodules. With the support region of 9 x 9 pixels, the QCI filter showed more selective response to the nodules than the CI filter. In the CAD scheme, intermediate nodule candidates are generated based on the QCI filter output and then classified using linear discriminant analysis of eight features that are attributed to each intermediate nodule candidate. The QCI filter output level itself was used as one of the features. The scheme achieved a sensitivity of 90% with 1.67 false positives per slice. The QCI filter output level was most effective among the features in correctly classifying intermediate nodule candidates. The QCI filter is promising as a tool of preprocessing for automated pulmonary nodule detection in CT images.

Algorithms↗