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Biomedical subjects

R A Schmidt

Publications and source records attributed to R A Schmidt.

At least 73 records · Page 4Linked to original sources
PubMed1995

Generalized motor programs for rapid bimanual tasks: a two-level multiplicative-rate model.

A model is proposed for the timing of rapid bimanual movements. It combines (a) the notion of a generalized motor program (GMP) with invariant relative timing, (b) the two-level concept of timing control with a central level of control and a peripheral level where the observations are made, and (c) the hypothesis that a single GMP simultaneously controls both limbs. Our method is based on the analysis of temporal intervals measured among landmarks taken from the bimanual kinematic traces. We show that sets of tetrad ratios--each composed of two pairs of covariances among four temporal intervals in the actions--should be equal to 1.0 if the hypothesis is correct. In addition, we show that these tetrad ratios should deviate systematically from 1.0 under certain, biologically realizable violations of the model. Data from human subjects show that the results generally conform to the basic model. Simulations are used to illustrate other violations of the model and to explore characteristics of the sampling distribution of the tetrad ratios under the model.

Hand↗
PubMed1995

Computer-aided detection of clustered microcalcifications on digital mammograms.

A computer-aided diagnosis scheme to assist radiologists in detecting clustered microcalcifications from mammograms is being developed. Starting with a digital mammogram, the scheme consists of three steps. First, the image is filtered so that the signal-to-noise ratio of microcalcifications is increased by suppression of the normal background structure of the breast. Secondly, potential microcalcifications are extracted from the filtered image with a series of three different techniques: a global thresholding based on the grey-level histogram of the full filtered image, an erosion operator for eliminating very small signals, and a local adaptive grey-level thresholding. Thirdly, some false-positive signals are eliminated by means of a texture analysis technique, and a non-linear clustering algorithm is then used for grouping the remaining signals. With this method, the scheme can detect approximately 85% of true clusters, with an average of two false clusters detected per image.

Breast Diseases↗
PubMed1995

Clinical use of digital mammography: the present and the prospects.

Digital mammography is likely to replace the current routine breast imaging technology in the future because it offers advantages that should lead to both improved image quality and interpretation. Hopefully, this will result in earlier detection in breast screening programs and decreased mortality from the most frequently diagnosed of all cancers after skin cancer, which is far less deadly. At present, digital mammography has a limited clinical role; in the United States, it has been used for several years to localize lesions for tissue sampling using small field of view digital detectors. Once whole breast digital detectors are available, it seems clear that applying computer techniques to enhance and analyze the collected digital data will become routine. Results reported over the last decade indicate that computer-aided diagnosis can improve radiologists' observational performance, and it is likely that computer techniques to routinely enhance the decision-making ability of the average to below-average radiologist to the level of an expert will be developed. There are obstacles to these advances, but the combination of realizable technological solutions and the importance of the breast cancer problem clinically should provide sufficient where-withal and impetus to make digital mammography a clinical reality.

Breast Neoplasms↗
PubMed1995

Automated segmentation of digitized mammograms.

RATIONALE AND OBJECTIVES: Fast and reliable segmentation of digital mammograms into breast and nonbreast regions is an important prerequisite for further image analysis. We are developing a segmentation algorithm that is fully automated and can operate independent of type of digitizing system, image orientation, and image projection. METHODS: The algorithm identifies unexposed and direct-exposure image regions and generates a border surrounding the valid breast region, which can then be used as input for further image analysis. The program was tested on 740 digitized mammograms; the segmentation results were evaluated by two expert mammographers and two medical physicists. RESULTS: In 97% of the mammograms, the segmentation results were rated as acceptable for use in computer-aided diagnostic schemes. Segmentation problems encountered in the remaining 22 images (2.9%) were most often caused by digitization artifacts or poor mammographic technique. CONCLUSION: The developed algorithm can serve as a component of an "intelligent" workstation for computer-aided diagnosis in mammography.

Algorithms↗
PubMed1995

Bartonella (Rochalimaea) species as a cause of apparent "culture-negative" endocarditis.

Bartonella quintana (formerly Rochalimaea quintana) is a recently recognized cause of apparent "culture-negative" endocarditis. We describe a 39-year-old, homeless man who developed aortic valve endocarditis caused by B. quintana. He had a history of alcoholism and was seronegative for the human immunodeficiency virus. We established that B. quintana was the cause of the endocarditis on the basis of the isolation of B. quintana from blood cultures, the compatibility of histochemical stains of cardiac valve tissue, the reactivity of the polymerase chain reaction specific for B. quintana on cardiac valve tissue, and the failure to isolate an alternative causative organism despite extensive efforts. This is the second report of endocarditis caused by B. quintana and the fourth report of endocarditis caused by a Bartonella species. On the basis of the findings of this report and those of other recent reports, further study is warranted to determine the overall role of Bartonella species in apparent culture-negative endocarditis.

Adult↗
PubMed1995

Analysis of spiculation in the computerized classification of mammographic masses.

Spiculation is a primary sign of malignancy for masses detected by mammography. In this study, we developed a technique that analyzes patterns and quantifies the degree of spiculation present. Our current approach involves (1) automatic lesion extraction using region growing and (2) feature extraction using radial edge-gradient analysis. Two spiculation measures are obtained from an analysis of radial edge gradients. These measures are evaluated in four different neighborhoods about the extracted mammographic mass. The performance of each of the two measures of spiculation was tested on a database of 95 mammographic masses using ROC analysis that evaluates their individual ability to determine the likelihood of malignancy of a mass. The dependence of the performance of these measures on the choice of neighborhood was analyzed. We have found that it is only necessary to accurately extract an approximate outline of a mass lesion for the purposes of this analysis since the choice of a neighborhood that accommodates the thin spicules at the margin allows for the assessment of margin spiculation with the radial edge-gradient analysis technique. The two measures performed at their highest level when the surrounding periphery of the extracted region is used for feature extraction, yielding Az values of 0.83 and 0.85, respectively, for the determination of malignancy. These are similar to that achieved when a radiologist's ratings of spiculation (Az = 0.85) are used alone. The maximum value of one of the two spiculation measures (FWHM) from the four neighborhoods yielded an Az of 0.88 in the classification of mammographic mass lesions.

Automation↗
PubMed1995

Familial pancreatic adenocarcinoma: association with diabetes and early molecular diagnosis.

We report a large pedigree in which pancreatic cancer is inherited in an autosomal dominant fashion. Diabetes and exocrine insufficiency was observed in all family members who eventually developed pancreatic cancer. The presence of diabetes, often years before the diagnosis of cancer, allowed identification of those people who had inherited the predisposing allele and who were thus at high risk for the development of malignancy. This family shows that genetic factors can have a striking effect on the development of pancreatic malignancy and diabetes mellitus. Moreover, preclinical diagnosis of pancreatic cancer in family members provided a unique opportunity to study early molecular changes that accompany the development of human pancreatic cancer. Finally, the molecular approach applied here to the early diagnosis of pancreatic cancer may prove valuable in this family for identification of subjects at risk.

Adenocarcinoma↗
PubMed1995

Computerized detection of clustered microcalcifications: evaluation of performance on mammograms from multiple centers.

To investigate the performance of a computerized method for the automated detection of clustered microcalcifications in digitized mammograms from a variety of screening centers, the authors invited 118 radiologists to bring up to five mammograms to their scientific exhibit at the 1993 meeting of the Radiological Society of North America (RSNA). Forty-three mammograms from 14 sites were brought to the exhibit, where they were digitized and analyzed. Results of the analysis on the RSNA cases were compared with those obtained on a standard database of 39 mammograms collected from two centers. The performance of the detection algorithm on the RSNA images was lower than that achieved on the standard database. This lower performance was due in part to the higher fraction of very subtle clustered microcalcifications in the RSNA cases, as well as the apparent dependence of the algorithm on image characteristics (eg, contrast and noise), which varied from center to center. The authors conclude that the algorithm is robust and accurate enough to undergo clinical testing. When it is implemented clinically, the computerized scheme must be customized to the image characteristics at each specific screening center to obtain optimal performance.

Breast Diseases↗
PubMed1995

Reinnervation of the rat bladder with a somatic nerve and a striated muscle flap.

PURPOSE: Current techniques of ventral sacral root stimulation to regain voluntary motor control of the decentralized urinary bladder depend upon intact parasympathetic innervation of the detrusor. We investigated techniques that might allow restoration of motor control of the bladder after efferent parasympathetic impairment. MATERIALS AND METHODS: In a chronic rat model, we evaluated whether motor control of a peripherally denervated bladder could be restored by transplantation of autologous excitable tissues and subsequent electrostimulation. Either a somatic nerve or a striated muscle flap was used as the transplant. RESULTS: Four months after the initial surgery, electrostimulation of the somatic nerve implant provoked bladder contractions--a response that was blocked by atropine. Stimulation of the nerve innervating the striated muscle flap also provoked bladder contractions; these were not affected by atropine and were slightly reduced by hexamethonium. CONCLUSION: Reinnervation of the bladder with somatic nerves or striated muscles is possible in principle. Future experiments will clarify the clinical significance of electrostimulation of such implants.

Animals↗
PubMed1994

Computerized characterization of mammographic masses: analysis of spiculation.

Although general rules for the differentiation between benign and malignant breast lesions exist, only 10 to 20% of masses referred for surgical breast biopsy are actually malignant. We are developing, as an aid to radiologists, a computerized scheme for the classification of masses appearing on mammograms to reduce the number of false-positive diagnoses of malignancies. The classification scheme involves the extraction of the margin of masses in order to quantify the degree of spiculation, which, in turn, is related to the likelihood of malignancy. When two measures of spiculation are used as input to an artificial neural network, the scheme achieves a performance similar to that achieved when radiologist's spiculation ratings alone are used for a clinical database of 53 masses. The computerized classification scheme therefore has the potential to effectively aid radiologists in determining appropriate patient management.

Breast Neoplasms↗
PubMed1994

Uterine papillary serous carcinoma: patterns of metastatic spread.

Uterine papillary serous carcinoma (UPSC) is a distinct histologic type of endometrial cancer which is associated with a high relapse rate and poor prognosis. Between 1983 and 1993, 50 patients with UPSC of the endometrium were surgically staged. Thirty-three patients had pure UPSC and 17 had UPSC admixed with other histologies. Extrauterine disease was found in 36 women (72%). Lymph node metastases were present in 36% of women without myometrial invasion, 50% with inner one-half invasion, and 40% with outer one-half invasion. Similarly, the presence of intraperitoneal disease or positive washings did not correlate with increasing myometrial invasion. Grade and histology (mixed vs pure) were also not predictive of extrauterine disease. Patients with lymphatic/vascular space invasion (LVSI) were more likely to have extrauterine disease (85%); however, even without LVSI the incidence of extrauterine disease was 58% (P = 0.05). Unlike endometrioid adenocarcinomas, grade and depth of myometrial invasion were not significant predictors for extrauterine disease. This study reinforces the need for complete surgical staging in all patients with UPSC regardless of depth of invasion.

Aged↗
PubMed1994

Computerized detection of masses in digital mammograms: investigation of feature-analysis techniques.

Mammographic screening of asymptomatic women has shown effectiveness in the reduction of breast cancer mortality. We are developing a computerized scheme for the detection of mammographic masses as an aid to radiologists in mammographic screening programs. Possible masses on digitized screen/film mammograms are initially identified using a nonlinear bilateral-subtraction technique, which is based on asymmetric density patterns occurring in corresponding portions of right and left mammograms. In this study, we analyze the characteristics of actual masses and nonmass detections to develop feature-analysis techniques with which to reduce the number of nonmass (ie, false-positive) detections. These feature-analysis techniques involve (1) the extraction of various features (such as area, contrast, circularity and border-distance based on the density and geometric information of masses in both processed, and original breast images), and (2) tests of the extracted features to reduce nonmass detections. Cumulative histograms of both actual-mass detections and nonmass detections are used to characterize extracted features and to determine the cutoff values used in the feature tests. The effectiveness of the feature-analysis techniques is evaluated in combination with the computerized detection scheme that uses the nonlinear bilateral-subtraction technique using free-response receiver operating characteristic analysis and 77 patient cases (308 mammograms). Results show that the feature-analysis techniques effectively improve the performance of the computerized detection scheme: about 35% false-positive detections were eliminated without loss in sensitivity when the feature-analysis techniques were used.

Breast Neoplasms↗
PubMed1994

Acquired immunodeficiency syndrome-related lymphoma of the lung presenting as empyema thoracis.

B-cell lymphoma in patients infected with the human immunodeficiency virus is usually a disseminated process that occasionally involves the lungs. Surgical diagnosis is often necessary to distinguish this from other neoplasms or opportunistic infections of the lung. We report a case of pulmonary B-cell lymphoma in a patient infected with human immunodeficiency virus who presented with a left empyema thoracis and an associated left lower lobe abscess secondary to bronchial obstruction. Resection was performed and the patient subsequently recovered from the acute process and survived an additional 6 months. This report demonstrates that surgical intervention may be necessary for both the diagnosis of pulmonary lymphoma and the definitive management of infectious complications that may arise as a result of pulmonary neoplastic disease in patients with acquired immunodeficiency syndrome.

Adult↗
PubMed1994

Schwannoma of the left atrium: diagnostic evaluation and surgical resection.

A case of primary benign schwannoma of the left atrium is reported. A 35-year-old man presented with new onset atrial fibrillation. Noninvasive evaluation revealed a 4 x 7-cm mediastinal mass either directly posterior to or involving the left atrium. The histologic diagnosis and specific location of the mass were established by incisional biopsy through a left thoracotomy. On cardiopulmonary bypass the tumor was resected by widely excising the posterior left atrium. The residual 20-cm2 left atrial defect was patched with pericardium. This unusual case illustrates that left atrial tumors originating near the pulmonary veins may be nonmyxomatous neoplasms of neural origin. Diagnosis may be difficult and require a surgical transthoracic approach.

Adult↗
PubMed1994

Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network.

A computer-aided diagnosis (CAD) scheme has been developed in our laboratory for the detection of clustered microcalcifications in digital mammograms. In this study, we apply a shift-invariant neural network to eliminate false-positive detections reported by the CAD scheme. The shift-invariant neural network is a multilayer back-propagation neural network with local, shift-invariant interconnections. The advantage of the shift-invariant neural network is that the result of the network is not dependent on the locations of the clustered microcalcifications in the input layer. The neural network is trained to detect each individual microcalcification in a given region of interest (ROI) reported by the CAD scheme. A ROI is classified as a positive ROI if the total number of microcalcifications detected in the ROI is greater than a certain number. The performance of the shift-invariant neural network was evaluated by means of a jackknife (or holdout) method and ROC analysis using a database of 168 ROIs, as reported by the CAD scheme when applied to 34 mammograms. The analysis yielded an average area under the ROC curve (Az) of 0.91. Approximately 55% of false-positive ROIs were eliminated without any loss of the true-positive ROIs. The result is considerably better than that obtained in our previous study using a conventional three-layer, feed-forward neural network. The effect of the network structure on the performance of the shift-invariant neural network is also studied.

Biophysical Phenomena↗
PubMed1994

Effect of case selection on the performance of computer-aided detection schemes.

The choice of clinical cases used to train and test a computer-aided diagnosis (CAD) scheme can affect the test results (i.e., error rate). In this study, we deliberately modified the components of our testing database to study the effects of this modification on measured performance. Using a computerized scheme for the automated detection of breast masses from mammograms, it was found that the sensitivity of the scheme ranged between 26% and 100% (at a false positive rate of 1.0 per image) depending on the cases used to test the scheme. Even a 20% change in the cases comprising the database can reduce the measured sensitivity by 15%-25%. Because of the strong dependence of measured performance on the testing database, it is difficult to estimate reliably the accuracy of a CAD scheme. Furthermore, it is questionable to compare different CAD schemes when different cases are used for testing. Sharing databases, creating a common database, or using a quantitative measure to characterize databases are possible solutions to this problem. However, none of these solutions exists or is practiced at present. Therefore, as a short-term solution, it is recommended that the method used for selecting cases, and histograms or mean and standard deviations of relevant image features be reported whenever performance data are presented.

Breast Neoplasms↗
PubMed1994

Computerized detection of masses in digital mammograms: automated alignment of breast images and its effect on bilateral-subtraction technique.

An automated technique for the alignment of right and left breast images has been developed for use in the computerized analysis of bilateral breast images. In this technique, the breast region is first identified in each digital mammogram by use of histogram analysis and morphological filtering operations. The anterior portions of the tracked breast border and computer-identified nipple positions are selected as landmarks for use in image registration. The paired right and left breast images, either from mediolateral oblique or craniocaudal views, are then registered relative to each other by use of a least-squares matching method. This automated alignment technique has been applied to our computerized detection scheme that employs a nonlinear bilateral-subtraction method for the initial identification of possible masses. The effectiveness of using bilateral subtraction in identifying asymmetries between corresponding right and left breast images is examined by comparing detection performances obtained with various computer-simulated misalignments of 40 pairs of clinical mammograms. Based on free-response receiver operating characteristic and regression analyses, the detection performance obtained with the automated alignment technique was found to be higher than that obtained with simulated misalignments. Detection performance decreased gradually as the amount of simulated misalignment increased. These results indicate that automatic alignment of breast images is possible and that mass-detection performance appears to improve with the inclusion of asymmetric anatomic information but is not sensitive to slight misalignment.

Breast Neoplasms↗