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Biomedical subjects

M L Giger

Publications and source records attributed to M L Giger.

At least 73 records · Page 4Linked to original sources
PubMed1993

Comparison of bilateral-subtraction and single-image processing techniques in the computerized detection of mammographic masses.

RATIONALE AND OBJECTIVES: Identification of regions as possible masses on digitized screen film mammograms is an important initial step in the computerized detection of breast carcinomas. Possible masses may be initially extracted using criteria based on optical densities, geometric patterns, and asymmetries between corresponding locations in right and left mammograms. In this study, the usefulness of information arising from mammographic asymmetries for the identification of mass lesions is investigated. METHODS: Two techniques are investigated--a nonlinear bilateral-subtraction technique based on image pairs and a local gray-level thresholding technique based on single images. Detection performances obtained with the two techniques in combination with various feature-analysis techniques are evaluated using 154 pairs of mammograms and compared using free-response receiver operating characteristic (FROC) analysis. RESULTS: The nonlinear bilateral-subtraction technique performed better than the local gray-level thresholding technique. CONCLUSION: The incorporation of asymmetric information appears to be useful for computerized identification of possible masses on mammograms.

Female↗
PubMed1993

Computer-aided diagnosis in chest radiography. Preliminary experience.

RATIONALE AND OBJECTIVES: Computer-aided diagnosis (CAD) schemes for chest radiography are being developed with which to alert radiologists to possible lesions, and thus potentially improve diagnostic accuracy. However, CAD schemes have not been tested on a large number of clinical cases. The authors identify design parameters that would be required for development of an intelligent workstation. METHODS: Computer-aided diagnosis programs were applied for the automated detection of lung nodules, cardiomegaly, and interstitial infiltrates to 310 consecutive chest radiographs, and were analyzed for potential usefulness and limitations. Computer-aided diagnosis output was evaluated by radiologists and physicists for accuracy and technical problems, respectively. RESULTS: Approximately 70% of the results were judged to be potentially acceptable; however, the number of false-positive findings was relatively high. Technical problems included failure to detect subtle abnormalities and the occurrence of false-positive detections caused by normal anatomical structures. CONCLUSION: Computer-aided diagnosis has the potential to be a valuable aid to radiologists in clinical practice, if certain technical problems can be overcome and if optimal operating points can be defined for clinical use.

Adult↗
PubMed1993

Clinical experience with an advanced laser digitizer for cost-effective digital radiography.

Film digitization has not been widely pursued in clinical settings mainly because of perceived limitations involving film latitude and image quality. However, a high-quality laser digitizer can be combined with wide-latitude film and specially developed digital processing techniques to achieve image quality comparable or superior to that of storage phosphor computed radiography (SPR) over a wide range of exposure. This film digitization system provides the operational advantages of digital radiography, such as consistent image density, high-quality inexpensive duplicates, and digital storage and retrieval capability. The reliability and monetary costs of the system also compare favorably with those of SPR. In the long term, technologies that employ reusable plates and do not require chemical processing to produce diagnostic images may well replace screen-film systems. Presently, however, film digitization remains a practical and cost-effective approach to digital radiography.

Cost-Benefit Analysis↗
PubMed1993

An "intelligent" workstation for computer-aided diagnosis.

Computer-aided diagnosis (CAD) involves a computerized analysis of radiographs that is used as a "second opinion" by the radiologist. The approach presented incorporates computer vision and artificial intelligence techniques and includes schemes for the analysis of lung nodules, interstitial infiltrates, and cardiomegaly seen on chest radiographs; masses and clustered microcalcifications on mammograms; and stenoses and blood flow on angiograms. The demonstration of various CAD schemes in chest radiography and mammography on a six-monitor workstation simulates one possible clinical implementation of CAD in radiology. Whether soft- or hard-copy display media are used, the radiologist can refer to the CAD results and still use the original radiograph for the final diagnosis. Although initial impressions of this simulated "intelligent" workstation are encouraging, CAD is still in a preliminary stage of development. Various methods for effectively and efficiently integrating CAD into a clinical radiology department are being investigated.

Angiography↗
PubMed1993

Computerized radiographic analysis of osteoporosis: preliminary evaluation.

Measurement of bone mass is important in determining the risk for fracture and in following the course of patients undergoing therapy for osteoporosis. Bone mineral densitometry (BMD) is a good predictor of fracture risk, but there is considerable overlap in BMD measurements between individuals with fracture risk and those without. In this study, computerized texture analysis of the trabecular pattern on conventional spine radiographs was used to evaluate bone structure as a determinant of fracture risk. Standard lumbar spine radiographs of 43 individuals were analyzed and compared with BMD measurements obtained with dual-photon absorptiometry. This method was more effective than BMD in differentiation of patients with fractures elsewhere in the spine from those with no fracture. These preliminary results suggest that this method of bone structure analysis, combined with BMD, may lead to a more sensitive and specific predictor of osteoporosis and risk of fracture.

Adult↗
PubMed1993

Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.

The authors investigated the potential utility of artificial neural networks as a decision-making aid to radiologists in the analysis of mammographic data. Three-layer, feed-forward neural networks with a back-propagation algorithm were trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists. A network that used 43 image features performed well in distinguishing between benign and malignant lesions, yielding a value of 0.95 for the area under the receiver operating characteristic curve for textbook cases in a test with the round-robin method. With clinical cases, the performance of a neural network in merging 14 radiologist-extracted features of lesions to distinguish between benign and malignant lesions was found to be higher than the average performance of attending and resident radiologists alone (without the aid of a neural network). The authors conclude that such networks may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.

Female↗
PubMed1992

Evaluation of imaging properties of a laser film digitizer.

In this paper we provide a quantitative assessment of the basic imaging properties of a laser film digitizer. The characteristic curve of the digitizer was determined in terms of the relationship between input optical density and output pixel value. Spatial resolution of the laser digitizer was characterized using the presampling modulation transfer function (MTF), which was measured using a curve fitting technique with an angulated slit. For the noise analysis, we compared the Wiener spectra of uniformly exposed film samples before and after digitization. The effects of different sampling distances and scanning directions were investigated. Our results show that the characteristic curve of the laser digitizer was linear. The presampling MTFs of the digitizer were similar at different sampling distances and were substantially greater in the vertical scanning direction than in the horizontal direction. The noise of the digitized film sample was mainly affected by the presampling MTF and structure noise of the digitizer.

Evaluation Studies as Topic↗
PubMed1992

Computerized scheme for the detection of pulmonary nodules. A nonlinear filtering technique.

To aid radiologists in the detection of lung cancer, the authors are developing a computer-aided diagnosis system that locates areas suspicious for nodules in digital chest radiographs. The system involves a difference-image approach and various feature-extraction techniques. The authors describe nonlinear filters used in the difference-image approach. A morphological open operation and a ring-shaped median filter are applied in the difference-image step for signal enhancement and signal suppression, respectively. Using 60 clinical chest radiographs, the nonlinear filtering method detected approximately 63% of actual nodules with approximately 19 false-positive results per image. The locations of the false-positive detections, however, usually did not coincide with those from the linear filtering method. Thus, by using a combination of the detections from the two methods, the false-positive rate was reduced to two to three per image at a sensitivity of 60%.

Evaluation Studies as Topic↗
PubMed1992

Potential usefulness of computerized nodule detection in screening programs for lung cancer.

RATIONALE AND OBJECTIVE: To alert radiologists to possible nodule locations and subsequently to reduce the number of false-negative diagnoses, the authors are developing a computer-aided diagnostic (CAD) scheme for the detection of lung nodules in digital chest images. METHODS: A computer-vision scheme was applied to photofluorographic films obtained in a mass survey for detection of asymptomatic lung cancer in Japan. Ninety-five patients with abnormal test results who had primary and metastatic lung cancers and 103 patients with normal test results were included. RESULTS: The sensitivity of the computer output was comparable with that of physicians in this mass survey (62%). The computer detected approximately 40% of all nodules missed in the mass survey, but missed 17 true-positive results identified in the mass survey. The CAD scheme produced an average of 15 false-positive findings per image. CONCLUSION: If the number of false-positive results can be significantly reduced, computer-vision schemes such as this may have a role in lung cancer screening programs.

False Positive Reactions↗
PubMed1992

Image feature analysis of false-positive diagnoses produced by automated detection of lung nodules.

RATIONALE AND OBJECTIVES: To reduce the number of false-negative diagnoses by radiologists, the authors are developing a computer-aided diagnosis scheme for detection of lung nodules in digital chest images. In this study, the authors attempted to reduce the number of false-positive diagnoses obtained with a previous computer scheme by incorporating additional knowledge from experienced chest radiologists into the computer scheme. METHODS: The authors applied their previous computer scheme, using less-strict criteria, to 60 clinical chest radiographs; this yielded 735 candidate nodules (23 true nodules and 712 false-positive diagnoses). These candidates were analyzed using region-growing, trend-correction, and edge-gradient techniques to determine measures by which to quantify image features of candidate nodules. RESULTS: The 712 false-positive diagnoses represented various anatomic structures that were located throughout the chest image. From this analysis, we were able to decrease the number of false-positive errors from an average of 12 to approximately 5 per image without eliminating any true nodules. CONCLUSION: Our results show that incorporating knowledge from experienced chest radiologists into the computer algorithm will play an important role in the development of computerized schemes for the detection of pulmonary nodules.

Diagnostic Errors↗
PubMed1992

Computer-aided diagnosis: development of automated schemes for quantitative analysis of radiographic images.

Preliminary results obtained with computer-aided diagnosis (CAD) from various radiographic examinations are very encouraging. However, CAD is still at an early stage of its development. It will be necessary to increase further the understanding of image features of normal and abnormal patterns, to establish databases, and to devise specific approaches for particular types of pathology. Although the existing schemes are designed to be applied to digital radiographs, similar techniques can be applied in the future to cross-sectional images such as CT, MRI, and ultrasound. We believe that CAD will become clinically practical in the near future.

Angiography↗
PubMed1991

Data compression: effect on diagnostic accuracy in digital chest radiography.

High-resolution digital images make up very large data sets that are relatively slow to transmit and expensive to store. Data compression techniques are being developed to address this problem, but significant image deterioration can occur at high compression ratios. In this study, the authors evaluated a form of adaptive block cosine transform coding, a new compression technique that allows considerable compression of digital radiographs with minimal degradation of image quality. To determine the effect of data compression on diagnostic accuracy, observer tests were performed with 60 digitized chest radiographs (2,048 x 2,048 matrix, 1,024 shades of gray) containing subtle examples of pneumothorax, interstitial infiltrate, nodules, and bone lesions. Radiographs with no compression, with 25:1 compression, and with 50:1 compression ratios were presented in randomized order to 12 radiologists. The results suggest that, with this compression scheme, compression ratios as high as 25:1 may be acceptable for primary diagnosis in chest radiology.

Humans↗
PubMed1990

Computer-aided diagnosis in chest radiology.

Digital radiography offers several important advantages over conventional systems, including abilities for image manipulation, transmission, and storage. In the long term, however, the unique ability to apply artificial intelligence techniques for automated detection and quantitation of disease may have an even greater impact on radiologic practice. Although CAD is still in its infancy, the results of several recent studies clearly indicate a major potential for the future. The concept of using computers to analyze medical images is not new, but recent advances in computer technology together with progress in implementing practical digital radiography systems have stimulated research efforts in this exciting field. Several facets of CAD are presently being developed at the University of Chicago and elsewhere for application in chest radiology as well as in mammography and vascular imaging. To date, investigators have focused on a limited number of subjects that have been, by their nature, particularly suitable for computer analysis. There is no aspect of radiologic diagnosis that could not potentially benefit from this approach, however. The ultimate goal of these endeavors is to provide a system for comprehensive automated image analysis, the results of which could be accepted or modified at the discretion of the radiologist.

Breast Diseases↗
PubMed1990

Pulmonary nodules: computer-aided detection in digital chest images.

Currently, radiologists fail to detect pulmonary nodules in up to 30% of cases with actually positive findings. Diagnoses may be missed due to camouflaging effects of anatomic background, subjective and varying decision criteria, or distractions in clinical situations. We developed a computerized method to detect locations of lung nodules in digital chest images. The method is based on a difference-image approach and feature-extraction techniques, including growth, slope, and profile tests. Computer results were used to alert 12 radiologists to possible nodule locations in 60 clinical cases. Preliminary results suggest that computer aid can improve the detection performance of radiologists.

Adult↗
PubMed1990

Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: pilot study.

An artificial neural network approach was applied to the differential diagnosis of interstitial lung diseases. The neural network was designed to distinguish between nine types of interstitial lung diseases on the basis of 20 items of clinical and radiographic information. A data base for training and testing the neural network was created with 10 hypothetical cases for each of the nine diseases. The performance of the neural network was evaluated by means of receiver operating characteristic analysis. The decision performance of the neural network was high; it was comparable to that of chest radiologists and superior to that of senior radiology residents. The preliminary results strongly suggest that the neural network approach has potential utility in the computer-aided differential diagnosis of interstitial lung diseases.

Computer Simulation↗
PubMed1990

Evaluation of radiographs developed by a new ultrarapid film processing system.

The image quality of radiographs developed by a new ultrarapid processor was evaluated to determine if faster processing causes degradation in the image. The processor used was the Konica Super-Rapid SRX-501 model. Two films designed for this processor (Konica MGH-SR and MGL-SR) were processed in 45 sec and were compared with standard rapid processing in 90 sec of corresponding conventional films (Kodak TMG and OC). Rare-earth screens (Kodak Lanex Regular and Lanex Medium) used with the new and conventional films interleaved during angiographic studies or for phantom images were assessed for image quality. The basic imaging properties of the screen-film systems were examined by measuring (1) Hurter and Driffield curves, (2) modulation transfer functions by using the slit method, and (3) noise Wiener spectra. Subjective clinical assessment showed that the images obtained with ultrarapid processing were acceptable, with increased contrast and graininess. Hurter and Driffield curve measurements confirmed higher gradients. Modulation transfer function measurements were the same as for the conventional films. Noise Wiener spectrum measurements showed a 10% increase in noise for MGH-SR vs TMG film and a 30% increase for MGL-SR vs OC film. We conclude that acceptable image quality can be obtained using ultrarapid processing, with processing time approximately 60% that of conventional rapid processing. Potential applications include all areas in which rapid availability of the radiograph for interpretation is important. Although the processor studied was the first of its kind available, our evaluation indicates that the technology is available for a new class of ultrarapid processors.

Evaluation Studies as Topic↗