Biomedical subjects

C E Floyd

Publications and source records attributed to C E Floyd.

At least 19 recordsLinked to original sources

PubMed2001

Multifractal texture analysis of perfusion lung scans as a potential diagnostic tool for acute pulmonary embolism.

A computer-assisted diagnostic (CAD) tool was developed for the diagnosis of acute pulmonary embolism (PE) in perfusion lung scans. Forty-five scans (with angiographic proof) were included in the study. The CAD tool was composed of two modules. The first module performs multifractal texture analysis on the posterior view of the perfusion scan. The second module is a decision algorithm that merges the multifractal parameters into a diagnosis regarding the presence or absence of PE. Linear and non-linear decision models were evaluated for the diagnostic task. A consensus neural network significantly outperformed all decision models including the physicians.

Acute Disease↗

PubMed2001

A neural network approach to breast cancer diagnosis as a constraint satisfaction problem.

A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer diagnosis using mammographic and patient history findings. Initially, the diagnostic decision to biopsy was formulated as a constraint satisfaction problem. Then, an associative memory type neural network was applied to solve the problem. The proposed network has a flexible, nonhierarchical architecture that allows it to operate not only as a predictive tool but also as an analysis tool for knowledge discovery of association rules. The CSNN was developed and evaluated using a database of 500 nonpalpable breast lesions with definitive histopathological diagnosis. The CSNN diagnostic performance was evaluated using receiver operating characteristic analysis (ROC). The results of the study showed that the CSNN ROC area index was 0.84+/-0.02. The CSNN predictive performance is competitive with that achieved by experienced radiologists and backpropagation artificial neural networks (BP-ANNs) presented before. Furthermore, the study illustrates how CSNN can be used as a knowledge discovery tool overcoming some of the well-known limitations of BP-ANNs.

Age Factors↗

PubMed2001

Application of the mutual information criterion for feature selection in computer-aided diagnosis.

The purpose of this study was to investigate an information theoretic approach to feature selection for computer-aided diagnosis (CAD). The approach is based on the mutual information (MI) concept. MI measures the general dependence of random variables without making any assumptions about the nature of their underlying relationships. Consequently, MI can potentially offer some advantages over feature selection techniques that focus only on the linear relationships of variables. This study was based on a database of statistical texture features extracted from perfusion lung scans. The ultimate goal was to select the optimal subset of features for the computer-aided diagnosis of acute pulmonary embolism (PE). Initially, the study addressed issues regarding the approximation of MI in a limited dataset as it is often the case in CAD applications. The MI selected features were compared to those features selected using stepwise linear discriminant analysis and genetic algorithms for the same PE database. Linear and nonlinear decision models were implemented to merge the selected features into a final diagnosis. Results showed that the MI is an effective feature selection criterion for nonlinear CAD models overcoming some of the well-known limitations and computational complexities of other popular feature selection techniques in the field.

Diagnosis, Computer-Assisted↗

PubMed2001

Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers.

We propose to investigate the use of subregion Hotelling observers (SRHOs) in conjunction with perceptrons for the computerized classification of suspicious regions in chest radiographs for being nodules requiring follow up. Previously, 239 regions of interest (ROIs), each containing a suspicious lesion with proven classification, were collected. We chose to investigate the use of SRHOs as part of a multilayer classifier to determine the presence of a nodule. Each SRHO incorporates information about signal, background, and noise correlation for classification. For this study, 225 separate Hotelling observers were set up in a grid across each ROI. Each separate observer discriminates an 8 by 8 pixel area. A round robin sampling scheme was used to generate the 225 features, where each feature is the output of the individual observers. These features were then rank ordered by the magnitude of the weights of a perceptron. Once rank ordered, subsets of increasing number of features were selected to be used in another perceptron. This perceptron was trained to minimize mean squared error and the output was a continuous variable representing the likelihood of the region being a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis and reported as the area under the curve (Az). The classifier was optimized by adding additional features until the Az declined. The optimized subset of observers then were combined using a third perceptron. A subset of 80 features was selected which gave an Az of 0.972. Additionally, at 98.6% sensitivity, the classifier had a specificity of 71.3% and increased the positive predictive value from 60.7% to 84.1 %. Preliminary results suggest that using SRHOs in combination with perceptrons can provide a successful classification scheme for pulmonary nodules. This approach could be incorporated into a larger computer aided detection system for decreasing false positives.

Algorithms↗

PubMed2001

Imaging characteristics of an amorphous silicon flat-panel detector for digital chest radiography.

PURPOSE: To evaluate the imaging characteristics of an amorphous silicon flat-panel detector (FPD) for digital chest radiography. MATERIALS AND METHODS: The 41 x 41-cm digital FPD is constructed on a single monolithic glass substrate with a structured cesium iodide scintillator layer and an amorphous silicon thin-film transistor array for image readout. Basic imaging characteristics of the FPD and associated image processing system were assessed on acquired images, including linearity, repeatability, uniformity of response, modulation transfer function (MTF), noise power spectrum, detective quantum efficiency (DQE), contrast sensitivity, and scatter content. Results with the FPD system were compared to those with a storage phosphor computed radiography (CR) system. RESULTS: Images obtained with the FPD demonstrated excellent uniformity, repeatability, and linearity, as well as MTF and DQE that were superior to those with the storage phosphor CR system. The contrast and scatter content of images acquired with the FPD were equivalent to those acquired with the storage phosphor system. CONCLUSION: The FPD provides radiographic images with excellent inherent physical image quality.

Evaluation Studies as Topic↗

PubMed2000

Segmentation of suspicious clustered microcalcifications in mammograms.

We have developed a multistage computer-aided diagnosis (CAD) scheme for the automated segmentation of suspicious microcalcification clusters in digital mammograms. The scheme consisted of three main processing steps. First, the breast region was segmented and its high-frequency content was enhanced using unsharp masking. In the second step, individual microcalcifications were segmented using local histogram analysis on overlapping subimages. For this step, eight histogram features were extracted for each subimage and were used as input to a fuzzy rule-based classifier that identified subimages containing microcalcifications and assigned the appropriate thresholds to segment any microcalcifications within them. The final step clustered the segmented microcalcifications and extracted the following features for each cluster: the number of microcalcifications, the average distance between microcalcifications, and the average number of times pixels in the cluster were segmented in the second step. Fuzzy logic rules incorporating the cluster features were designed to remove nonsuspicious clusters, defined as those with typically benign characteristics. A database of 98 images, with 48 images containing one or more microcalcification clusters, provided training and testing sets to optimize the parameters and evaluate the CAD scheme, respectively. The results showed a true positive rate of 93.2% and an average of 0.73 false positive clusters per image. A comparison of our results with other reported segmentation results on the same database showed comparable sensitivity and at the same time an improved false positive rate. The performance of the CAD scheme is encouraging for its use as an automatic tool for efficient and accurate diagnosis of breast cancer.

Biophysical Phenomena↗

PubMed2000

Anthropomorphic versus geometric chest phantoms: a comparison of scatter properties.

Previously, we have used an anthropomorphic chest phantom to study scatter reduction in digital chest radiography. Image metrics, such as scatter fractions, contrast, noise, and resolution, are not easily measured due to the anatomical structure in the phantom. A geometric chest phantom, recently developed for quality control purposes, offers the possibility of being used to calculate image quality measurements. Here, we compare the scatter properties of the two phantoms to determine if the geometric phantom can be used in our studies of scatter compensation techniques. A calibrated photostimulable phosphor system was used to acquire images of the two phantoms. An array of beam stops was placed in front of each phantom to calculate scatter fractions. Each phantom had approximately 2 in. of polystyrene material added to the posterior to increase scatter fractions to those normally seen in patients. Exposure parameters were 300 mA for 0.009 sec with a source to image distance of 100 cm. Energies were varied from 60 to 130 kVp. Scatter fractions were determined for different areas of anatomy for each energy and each phantom. For all energies examined, the two phantoms compare well for scatter fractions in each of six regions. For example, at 95 kVp, the geometric phantom had average scatter fractions of 0.72 and 0.88 in the lung and mediastinum regions, respectively. These values were 0.74 and 0.90 for the anatomic phantom. For comparison, measurements of scatter fractions in patients at these values have been reported as 0.65 and 0.90 for the lung and mediastinum regions. The geometric phantom is an excellent tool which can be used in place of the anthropomorphic phantom for studies involving scatter compensation. In addition to having a gray level histogram typical of a human chest, this phantom has uniform regions where image quality measurements can be calculated.

Anthropometry↗

PubMed2000

Improved image quality in digital mammography with image processing.

PURPOSE: The effect of image processing, specifically Bayesian image estimation (BIE), on digital mammographic images is studied. BIE is an iterative, nonlinear statistical estimation technique that has previously been used in chest radiography to reduce image scatter content and improve the contrast-to-noise ratio (CNR). We adapt this technique to digital mammography and examine its effect. METHODS/MATERIALS: Images of the American College of Radiologists (ACR) breast phantom were acquired on a calibrated digital mammography system at a normal mammographic exposure both with and without a grid. An iterative Bayesian estimation algorithm was formulated and used to process the images acquired without a grid. Quantitative scatter fractions were measured and compared for the image acquired with the grid, the image acquired without the grid, and the image acquired without the grid and processed by the Bayesian algorithm. CNR values were also computed for the four visible masses in the ACR phantom before and after processing and compared to a grid. RESULTS: Initial images acquired without an antiscatter grid had scatter fractions of 0.46. Processing this image with BIE reduced the scatter content to under 0.04. In comparison, the image acquired with a grid had scatter of 0.19. BIE processing accounted for CNR improvements from 29% to 219% for the masses seen in the ACR phantom as compared to the unprocessed image. Visibility of the four masses in the phantom was improved. CONCLUSIONS: Bayesian image estimation can be used with digital mammography to reduce scatter fractions. This technique is very useful as it can reduce scatter content effectively without introducing any adverse effects, such as grid line aliasing. Bayesian processing can also increase image CNR, which may potentially increase the visualization of subtle masses. Preliminary work shows an improvement in CNR to values greater than that provided by a standard grid.

Algorithms↗

PubMed2000

Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions.

OBJECTIVE: We present case-based reasoning computer software developed from mammographic findings to provide support for the clinical decision to perform biopsy of the breast. SUBJECTS AND METHODS: The case-based reasoning system is designed to support the decision to perform biopsy in those patients who have suspicious findings on diagnostic mammography. Currently, between 66% and 90% of biopsies are performed on benign lesions. Our system is designed to help decrease the number of benign biopsies without missing malignancies. Clinicians interpret the mammograms using a standard reporting lexicon. The case-based reasoning system compares these findings with a database of cases with known outcomes (from biopsy) and returns the fraction of similar cases that were malignant. This malignancy fraction is an intuitive response that the clinician can then consider when making the decision regarding biopsy. RESULTS: The system was evaluated using a round-robin sampling scheme and performed with an area under the receiver operating characteristic curve of 0.83, comparable with the performance of a neural network model. If only the cases returning a malignancy fraction of greater than a threshold of 0.10 are sent to biopsy, no malignancies would be missed, and the number of benign biopsies would be decreased by 25%. At a threshold of 0.21, 98%, of the malignancies would be biopsied, and the number of benign biopsies would be decreased by 41%. CONCLUSION: This preliminary investigation indicates that the case-based reasoning approach to computer-aided diagnosis has the potential to improve the accuracy of breast cancer diagnosis on mammography.

Adult↗

PubMed1999

Effect of patient history data on the prediction of breast cancer from mammographic findings with artificial neural networks.

RATIONALE AND OBJECTIVES: The authors evaluated the contribution of medical history data to the prediction of breast cancer with artificial neural network (ANN) models based on mammographic findings. MATERIALS AND METHODS: Three ANNs were developed: The first used 10 Breast Imaging Reporting and Data System (BI-RADS) variables; the second, the BI-RADS variables plus patient age; the third, the BI-RADS variables, patient age, and seven other history variables, for a total of 18 inputs. Performance of the ANNs and the original radiologist's impression were evaluated with five metrics: receiver operating characteristic area index (Az); specificity at given sensitivities of 100%, 98%, and 95%; and positive predictive value. RESULTS: All three ANNs consistently outperformed the radiologist's impression over all five performance metrics. The patient-age variable was particularly valuable. Adding the age variable to the basic ANN model, which used only the BI-RADS findings, significantly improved Az (P = .028). In fact, replacing all history data with just the age variable resulted in virtually no changes for Az or specificity at 98% sensitivity (P = .324 and P = .410, respectively). CONCLUSION: Patient age was an important variable for the prediction of breast cancer from mammographic findings with the ANNs. For this data set, all history data could be replaced with age alone.

Adult↗

PubMed1999

Markov random field modeling in posteroanterior chest radiograph segmentation.

Previously, the authors presented an algorithm that identifies lung regions in a digitized posteroanterior chest radiograph (DCR) by labeling each pixel as either lung or nonlung. In this manuscript, the inherent flexibility of this algorithm is demonstrated as the algorithm is generalized to identify multiple anatomical regions in a DCR. Specifically, each pixel is classified as belonging to one of six anatomical region types: lung, subdiaphragm, heart, mediastinum, body, or background. The algorithm determines the optimal set of pixel classifications, xOPT, for a given set of DCR pixel gray level values y via a probabilistic approach that defines xOPT as the particular segmentation that maximizes the conditional distribution P(x/y). A spatially varying Markov random field (MRF) model is used that incorporates spatial and textural information of each possible region type. MRF modeling provides the form of P(x/y), and Iterated Conditional Modes is used to converge to the distribution maximum of P(x/y) thus obtaining the optimal segmentation for a given DCR. Results show the algorithm being able to correctly classify 90.0% +/- 3.4% of the pixels in a DCR.

Algorithms↗

PubMed1998

Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck.

PURPOSE: This study was performed to investigate the feasibility of predicting survival in squamous cell carcinoma of the head and neck (SCCHN) with an artificial neural network (ANN), and to compare ANN performance with conventional models. METHODS AND MATERIALS: Data were analyzed from a Phase III trial in which patients with locally advanced SCCHN received hyperfractionated irradiation with or without concurrent cisplatin and 5-fluorouracil. Of the 116 randomized patients, 95 who had 2-year follow-up and all required data were evaluated. ANN and logistic regression (LR) models were constructed to predict 2-year total survival using round-robin cross-validation. A modified staging model was also examined. RESULTS: The best LR model used tumor size, nodal stage, and race to predict survival. The best ANN used nodal stage, tumor size, stage, and resectability, and hemoglobin. Treatment type did not predict 2-year survival and was not included in either model. Using the respective best feature sets, the area under the receiver operating characteristic curve (Az) for the ANN was 0.78 +/- 0.05, showing more accurate overall performance than LR (Az = 0.67 +/- 0.05, p = 0.07). At 70% sensitivity, the ANN was 72% specific, while LR was 54% specific (p = 0.08). At 70% specificity, the ANN was 72% sensitive, while LR was 54% sensitive (p = 0.07). When both models used the five predictive variables best for an ANN, Az for LR decreased [Az = 0.61 +/- 0.06, p < 0.01 (ANN)]. The models performed equivalently when using the three variables best for LR. The best ANN also compared favorably with staging [Az = 0.60 +/- 0.07, p = 0.02 (ANN)]. CONCLUSIONS: An ANN modeled 2-year survival in this data set more accurately than LR or staging models and employed predictive variables that could not be used by LR. Further work is planned to confirm these results on larger patient samples, examining longer follow-up to incorporate treatment type into the model.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed1998

Predictive model for the diagnosis of intraabdominal abscess.

RATIONALE AND OBJECTIVES: The authors investigated the use of an artificial neural network (ANN) to aid in the diagnosis of intraabdominal abscess. MATERIALS AND METHODS: An ANN was constructed based on data from 140 patients who underwent abdominal and pelvic computed tomography (CT) between January and December 1995. Input nodes included data from clinical history, physical examination, laboratory investigation, and radiographic study. The ANN was trained and tested on data from all 140 cases by using a round-robin method and was compared with linear discriminate analysis. A receiver operating characteristic curve was generated to evaluate both predictive models. RESULTS: CT examinations in 50 cases were positive for abscess. This finding was confirmed by means of laboratory culture of aspirations from CT-guided percutaneous drainage in 38 patients, ultrasound-guided percutaneous drainage in five patients, surgery in five patients, and characteristic appearance on CT scans without aspiration in two patients. CT scans in 90 cases were negative for abscess. The sensitivity and specificity of the ANN in predicting the presence of intraabdominal abscess were 90% and 51%, respectively. Receiver operating characteristic analysis showed no statistically significant difference in performance between the two predictive models. CONCLUSION: The ANN is a useful tool for determining whether an intraabdominal abscess is present. It can be used to set priorities for CT examinations in order to expedite treatment in patients believed to be more likely to have an abscess.

Abdominal Abscess↗

PubMed1998

Characteristics of regions suspicious for pulmonary nodules at chest radiography.

RATIONALE AND OBJECTIVES: This study was performed to determine physical characteristics of areas on chest radiographs that are suspicious but not definitive for the presence of a pulmonary nodule and the characteristics of areas that contain an obvious nodule. MATERIALS AND METHODS: Two groups of patients were identified: those who had an area at plain radiography that was suspicious for a pulmonary nodule and underwent fluoroscopy for further evaluation (138 patients, 142 areas) and those who had an obvious nodule at plain radiography who underwent computed tomography for further evaluation (72 patients, 97 areas). The measured characteristics of the region of interest included size, circularity, compactness, contrast, and location. RESULTS: A comparison of the data show that while there was some difference between these groups of patients with regard to location of the nodules, there were essentially no differences with regard to size, circularity, compactness, and contrast of the regions of interest. CONCLUSION: Size, circularity, compactness, contrast, and location are not sufficient to distinguish pulmonary nodules from other suspicious regions on the chest radiograph.

Fluoroscopy↗

PubMed1998

Acute pulmonary embolism: cost-effectiveness analysis of the effect of artificial neural networks on patient care.

PURPOSE: To evaluate the cost-effectiveness of artificial neural networks for diagnosis in patients suspected of having acute pulmonary embolism who are typically referred for pulmonary angiography. MATERIALS AND METHODS: Four diagnostic strategies were explored to help define the diagnostic role of neural networks in patients suspected of having pulmonary embolism in whom nondiagnostic ventilation-perfusion lung scans were obtained. First, a network was used to determine which patients could be directly referred for treatment without angiography. Second, the network was applied to determine in which patients treatment could be withheld. Third, the network was used to distinguish patients in whom the network gave indeterminate responses and who should proceed to angiography. Each strategy was compared with use of angiography in terms of morbidity, mortality, and cost per life saved. RESULTS: The use of the neural network reduced the average cost per patient by more than one-half relative to the cost of angiography. Morbidity and mortality rates were also comparable to or lower than those associated with angiography. The results were consistent regardless of the prevalence of disease. CONCLUSION: The use of neural networks in the diagnosis of pulmonary embolism is a promising way to improve cost-effectiveness in the care of patients with nondiagnostic lung scans.

Acute Disease↗

PubMed1997

Fractal texture analysis in computer-aided diagnosis of solitary pulmonary nodules.

RATIONALE AND OBJECTIVES: The authors investigated the use of fractal texture characterization to improve the accuracy of solitary pulmonary nodule computer-aided diagnosis (CAD) systems. METHODS: Thirty chest radiographs were acquired from patients who had no pulmonary nodules. Thirty regions were selected that were considered remotely suspicious-looking for nodules. Artificial nodules of multiple shapes, sizes, and orientations were added at subtle levels of contrast to 30 non-suspicious-looking regions of the radiographs. Fractal dimensions of the 60 "nodule candidates" were calculated to quantify the texture of each region. Four radiologists also interpreted the images. RESULTS: The fractal dimension of each possible nodule provided statistically significant (P < .05) differentiation between regions that contained an artificial nodule and those that did not. The area under the receiver operating characteristic curve for the fractal analysis was significantly better (P < .05) than that for the radiologists. CONCLUSION: Fractal texture characterization provides useful information for the classification of potential solitary pulmonary nodules with CAD algorithms.

Diagnosis, Computer-Assisted↗

PubMed1997

Improved Bayesian image estimation for digital chest radiography.

PURPOSE: Previously, we have shown that Spatially Varying Bayesian Image Estimation (SVBIE) can be used to reduce scatter and improve contrast-to-noise ratios (CNR) in digital chest radiographs with no degradation of image resolution. This previous algorithm used a model for scatter compensation that was derived for emission tomography. Here, we develop and evaluate a new iterative SVBIE technique that incorporates a scatter model derived for projection radiography. MATERIALS AND METHODS: Portable digital radiographs of an anthropomorphic chest phantom were obtained along with quantitative scatter measurements using a calibrated photostimulable phosphor system. The new iterative SVBIE technique was applied to the phantom image to reduce scatter. Scatter fraction reduction, CNR improvement, and resolution degradation were evaluated. RESULTS: Residual scatter fractions were reduced to less than 2% in the lungs and 30% in the mediastinum at 14 iterations. CNR was improved by approximately 50% in the lung region and 187% in the mediastinum. Resolution was not degraded. CONCLUSIONS: The new SVBIE technique can reduce scatter to levels far below those provided by an antiscatter grid and can increase CNR without loss of resolution. The new technique outperforms the previous Bayesian techniques.

Bayes Theorem↗

PubMed1997

Quality control phantom for digital chest radiography.

PURPOSE: To develop and test a chest phantom for routine quality control testing of digital radiography systems. MATERIALS AND METHODS: The phantom was constructed from sheets of copper, aluminum, and acrylic, which were cut and arranged to yield a radiographic projection resembling that of a human thorax. Regional test objects allowed quantitative assessment of optical density, contrast detail, and spatial resolution. Validation tests were performed to assess image stability in a stable imaging environment and sensitivity to changes in image quality when they occur. RESULTS: The phantom yielded consistent pseudoclinical images when used in a routine quality control program and facilitated detection of simulated problems that were induced in imaging system performance. CONCLUSION: The chest phantom enables quantitative, full-system testing of digital radiography system as they are used clinically for chest radiography.

Humans↗