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Results for “Pattern Recognition, Automated”

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At least 127 records · Page 7Linked to original sources
PubMed1995

Quantitative analysis of computed tomography scans of the lungs for the diagnosis of pulmonary emphysema. A validation study of a semiautomated contour detection technique.

RATIONALE AND OBJECTIVES: To develop an analytic software package based on automated contour detection for the objective and reproducible assessment of emphysema from computed tomography (CT) scans. METHODS: A semiautomated technique was developed for the definition of lung contours in CT cross-sections followed by the assessment of pulmonary CT parameters describing the disease state. For 78 images, the semiautomated contour detection was performed and compared with contours drawn by an experienced radiologist by calculating the systematic area difference (bias) and differences in pulmonary CT parameters such as the mean lung density (MLD). In addition, intraobserver and interobserver variabilities were determined in a subset of 15 images. RESULTS: The areas enclosed by the semiautomatically detected contours were slightly larger than the manual ones (bias < 2.1%). The biases in the observer studies were smaller in the semiautomated versus the manual case (0.3% vs. 1.3%). The standard deviation of the MLD differences with a manual analysis was larger by a factor of five than in the semiautomated case. On average, manual analysis required 2 minutes, 18 seconds per lung; this time was reduced to 11.5 to 29 seconds with the semiautomated approach, depending on the respiration state. CONCLUSIONS: The semiautomated approach is preferred over the manual approach because of its higher consistency and its shorter analysis time.

Adult↗
PubMed1998

Maximum A posteriori classification of DNA structure from sequence information.

We introduce an algorithm, LLLAMA, which combines simple pattern recognizers into a general method for estimating the entropy of a sequence. Each pattern recognizer exploits a partial match between subsequences to build a model of the sequence. Since the primary features of interest in biological sequence domains are subsequences with small variations in exact composition, LLLAMA is particularly suited to such domains. We describe two methods, LLLAMA-length and LLLAMA-alone, which use this entropy estimate to perform maximum a posteriori classification. We apply these methods to several problems in three-dimensional structure classification of short DNA sequences. The results include a surprisingly low 3.6% error rate in predicting helical conformation of oligonucleotides. We compare our results to those obtained using more traditional methods for automated generation of classifiers.

Algorithms↗
PubMed1980

Evaluation of pattern recognition rules for the apex of the heart.

The observer variability and accuracy of rules for the automatic recognition of the left ventricular (LV) apex are reported. To form a data base for examining this problem 18 co-workers were asked to independently identify the apex of 79 left ventriculograms (LVgrams) in the 30-degree right anterior oblique projection at end-disastole (ED) and end-systole (ES), and to assign a confidence score from 0% to 100% for each apex. The group consisted of five clinical cardiologists (G1), six trained analysts (G2), and seven other experienced observers (G3). The LVgrams were selected to include those from 31 normal individuals and those from 48 patients with mixed akinesis/dykinesis as coded by conventional scoring techniques. There was no significant difference in the confidence for the ED vs ES apices. However, confidence of G1 was significantly lower than that of G2. The standard deviation in the observer-detected apex correlated (r = -0.92) with the mean observer confidence, which suggests a basis for quantifying "confidence" in the automated apex detection. The apex determined with respect to the superior aspect of the aortic valve proved superior to all other tested methods. the concept of "peakedness index" is introduced and used to estimate observer variability.

Adult↗
PubMedDate not supplied

Ellipse test for the reduction of false positive signals in automated cytology.

One of the major problems in automated cytology is the elimination of false positive 'abnormal cell' alarms caused by objects such as overlapping cell pairs, leukocyte clusters, etc. The paper describes an algorithm for the separation of images of abnormal cell nuclei and non=nuclear objects in computer image analysis systems for automated cytology. The algorithm involves the measurement of the agreement between the object outline and a computer ellipse of equal area, aspect ratio and orientation. Results obtained with the CERVISCAN experimental computer image analysis system show that the algorithm gives good discrimination between abnormal cell nuclei and typical non-nuclear objects found in cervical scrape specimens prepared specially for automated analysis.

Cell Nucleus↗
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↗
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↗
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↗
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↗