Biomedical subjects

M L Giger

Publications and source records attributed to M L Giger.

101 records · Page 6Linked to original sources

PubMedDate not supplied

Investigation of basic imaging properties in digital radiography. 13. Effect of simple structured noise on the detectability of simulated stenotic lesions.

We investigated the effects of structured background noise on the detectability of stenotic lesions. Digital subtraction angiographic (DSA) images of stenotic blood vessels were simulated and superimposed onto uniform noise samples. Eighteen-alternative forced choice (18-AFC) experiments were employed to determine the detectability of the stenotic lesion in the structured-noise background of a blood vessel. In this study, the dependence of detectability on lesion size, vessel size, and incident x-ray exposure was examined. Our results indicate that the presence of structured noise in an image will reduce the detectability of a lesion. However, the relative performance of an observer when the lesion size and incident exposure were varied was the same with and without the presence of the structured background. Thus, conclusions obtained previously with regard to changes in the detectability of a lesion in the presence of uniform background noise can be applied directly to conditions in which simple structured anatomic background is present.

Angiography↗

PubMedDate not supplied

Measurement of the presampling modulation transfer function of film digitizers using a curve fitting technique.

A curve fitting technique combined with an angulated slit image has been developed for the measurement of the presampling modulation transfer function (MTF) of film digitizers. The noisy line spread functions (LSFs) acquired from an angulated slit image are fitted using a combination of two functions by means of a nonlinear least-square fitting technique. The parameters in the model function for each LSF are obtained by minimizing the residual root mean square (RMS), and then averaged over all the LSF fittings. The resulting analytical function is representative of the continuous presampled LSF. We have found that a combination of Gaussian and exponential functions provides a good fit to the LSFs obtained with film digitizers. The corresponding analytical Fourier transformation of the model function yields the presampling MTF, without Nyquist frequency limitation. Measurements of spatial resolution properties using this method were performed for two laser scanners and an optical drum scanner.

Biophysical Phenomena↗

PubMedDate not supplied

Computerized detection of pulmonary nodules in digital chest images: use of morphological filters in reducing false-positive detections.

Currently, radiologists can fail to detect lung nodules in up to 30% of actually positive cases. If a computerized scheme could alert the radiologist to locations of suspected nodules, then potentially the number of missed nodules could be reduced. We are developing such a computerized scheme that involves a difference-image approach and various feature-extraction techniques. In this paper, we describe our use of digital morphological processing in the reduction of computer-identified false-positive detections. A feature-extraction technique, which includes the sequential application of nonlinear filters of erosion and dilation, is employed to reduce the camouflaging effect of ribs and vessels on nodule detection. This additional feature-extraction technique reduced the true-positive rate of the computerized scheme by 13% and the false-positive rate by 50%. In a comparison of the scheme with and without the additional feature-extraction technique, inclusion of the additional technique increased the detection sensitivity by about half at the level of three to four false-positive detections per chest image.

False Positive Reactions↗

PubMedDate not supplied

Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images.

A computerized scheme is being developed for the detection of masses in digital mammograms. Based on the deviation from the normal architectural symmetry of the right and left breasts, a bilateral subtraction technique is used to enhance the conspicuity of possible masses. The scheme employs two pairs of conventional screen-film mammograms (the right and left mediolateral oblique views and craniocaudal views), which are digitized by a TV camera/Gould digitizer. The right and left breast images in each pair are aligned manually during digitization. A nonlinear bilateral subtraction technique that involves linking multiple subtracted images has been investigated and compared to a simple linear subtraction method. Various feature-extraction techniques are used to reduce false-positive detections resulting from the bilateral subtraction. The scheme has been evaluated using 46 pairs of clinical mammograms and was found to yield a 95% true-positive rate at an average of three false-positive detections per image. This preliminary study indicates that the scheme is potentially useful as an aid to radiologists in the interpretation of screening mammograms.

Breast Neoplasms↗

PubMedDate not supplied

Comparison of imaging properties of a computed radiography system and screen-film systems.

To compare the diagnostic quality of images obtained with a computed radiography (CR) system based on storage phosphor technology with that obtained with conventional screen-film systems, a dual-image recording technique was devised. With this technique, a CR imaging plate is placed behind a screen-film system in a conventional cassette. This makes it possible to obtain two images simultaneously, one from each system, in a clinical examination with the same patient positioning, the same degree of patient motion, the same geometric unsharpness, and no additional exposure. The modulation transfer functions (MTFs) of the CR system with and without the dual-image recording technique were greater at low frequencies, but lower at high frequencies, that the MTFs of the screen-film systems used. The noise Wiener spectra of the CR images at the plane of the imaging plate were greater than those of the screen-film systems, but were comparable to those of the screen-film systems at the plane of the printed film due to the reduction in image size. Clinical chest images obtained with the dual-image recording technique appeared comparable, probably because of the image size reduction and the use of mild unsharp mask processing.

Humans↗

PubMedDate not supplied

Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks.

Artificial neural networks have been applied to the differentiation of actual "true" clusters from normal parenchymal patterns and also to the differentiation of actual clusters from false-positive clusters as reported by a computerized scheme for the detection of microcalcifications in digital mammograms. The differentiation was carried out in both the spatial and frequency domains. The performance of the neural networks was evaluated quantitatively by means of receiver operating characteristic (ROC) analysis. It was found that the networks could distinguish clustered microcalcifications from normal nonclustered areas in the frequency domain, and that they could eliminate approximately 50% of false-positive clusters of microcalcifications while preserving 95% of the positive clusters, when applied to the results of the automated detection scheme. A large, comprehensive training database is needed for neural networks to perform reliably in clinical situations.

Breast Diseases↗

PubMedDate not supplied

Application of the EM algorithm to radiographic images.

The expectation maximization (EM) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabilities of the EM algorithm when applied to radiographic images is investigated. This application does not involve reconstruction. The performance of the EM algorithm is quantitatively evaluated using a "perceived" signal-to-noise ratio (SNR) as the image quality metric. This perceived SNR is based on statistical decision theory and includes both the observer's visual response function and a noise component internal to the eye-brain system. For a variety of processing parameters, the relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to compare quantitatively the effects of the EM algorithm with two other image enhancement techniques: global contrast enhancement (windowing) and unsharp mask filtering. The results suggest that the EM algorithm's performance is superior when compared to unsharp mask filtering and global contrast enhancement for radiographic images which contain objects smaller than 4 mm.

Algorithms↗

PubMedDate not supplied

Computer-aided detection of clustered microcalcifications: an improved method for grouping detected signals.

A computerized scheme for the automated detection of clustered microcalcifications from digital mammograms is being developed. This scheme is one part of an overall package for computer-aided diagnosis (CAD), the purpose of which is to assist radiologists in detecting and diagnosing breast cancer. One important step in the computer detection scheme is to group or cluster microcalcifications, since clustered microcalcifications are more clinically significant than are isolated microcalcifications. Previously a "growing" technique in which signals (possible microcalcifications) were clustered by grouping those that were within some predefined distance from the center of the growing cluster was used. In this paper, a new technique for grouping signals, which consists of two steps, is introduced. First, signals that may be several pixels in area are reduced to single pixels by means of a recursive transformation. Second, the number of signals (nonzero pixels) within a small region, typically 3.2 x 3.2 mm, are counted. Only if three or more signals are present within such a region are they preserved in the output image. In this way, isolated signals are eliminated. Furthermore, this method can eliminate falsely detected clusters, which were identified by a previous detection scheme, based on the spatial distribution of signals within the cluster. The differences in performance of the CAD scheme for detecting clustered microcalcifications using the old and new clustering techniques was measured using 78 mammograms, containing 41 clusters.(ABSTRACT TRUNCATED AT 250 WORDS)

Breast Diseases↗

PubMedDate not supplied

Development of a high quality film duplication system using a laser digitizer: comparison with computed radiography.

A high quality film-duplication system was developed in order to improve the image quality of duplicated radiographs and to recover improperly exposed films. The system consists of a laser film digitizer, a laser film printer, a workstation, and a magneto-optical disk. Radiographs are digitized by the laser digitizer, processed by the computer for image enhancement, and then printed on a film by the laser printer. A nonlinear density-correction technique is employed in recovering improperly exposed radiographs using the H&D curve of the screen-film system. Using the new duplication system in our department, the average recovery rate was over 80% for chest and abdominal films rejected due to over- or underexposed. The basic imaging properties of the duplication system were compared with those of a Computed Radiography (CR) system and a conventional screen-film system. For low spatial frequencies, the MTF of the CR system is superior to that of the digital duplication system; however, for high spatial frequencies, the MTF of the duplication system is superior. The noise in the duplication system is about half of that in the CR system.

Biophysical Phenomena↗

PubMedDate not supplied

Automatic segmentation of liver structure in CT images.

The segmentation and three-dimensional representation of the liver from a computed tomography (CT) scan is an important step in many medical applications, such as in the surgical planning for a living-donor liver transplant and in the automatic detection and documentation of pathological states. A method is being developed to automatically extract liver structure from abdominal CT scans using a priori information about liver morphology and digital image-processing techniques. Segmentation is performed sequentially image-by-image (slice-by-slice), starting with a reference image in which the liver occupies almost the entire right half of the abdomen cross section. Image processing techniques include gray-level thresholding, Gaussian smoothing, and eight-point connectivity tracking. For each case, the shape, size, and pixel density distribution of the liver are recorded for each CT image and used in the processing of other CT images. Extracted boundaries of the liver are smoothed using mathematical morphology techniques and B-splines. Computer-determined boundaries were compared with those drawn by a radiologist. The boundary descriptions from the two methods were in agreement, and the calculated areas were within 10%.

Biophysical Phenomena↗

PubMedDate not supplied

Computerized detection of pulmonary nodules on CT scans.

Helical computed tomography (CT) is the most sensitive imaging modality for detection of pulmonary nodules. However, a single CT examination produces a large quantity of image data. Therefore, a computerized scheme has been developed to automatically detect pulmonary nodules on CT images. This scheme includes both two- and three-dimensional analyses. Within each section, gray-level thresholding methods are used to segment the thorax from the background and then the lungs from the thorax. A rolling ball algorithm is applied to the lung segmentation contours to avoid the loss of juxtapleural nodules. Multiple gray-level thresholds are applied to the volumetric lung regions to identify nodule candidates. These candidates represent both nodules and normal pulmonary structures. For each candidate, two- and three-dimensional geometric and gray-level features are computed. These features are merged with linear discriminant analysis to reduce the number of candidates that correspond to normal structures. This method was applied to a 17-case database. Receiver operating characteristic (ROC) analysis was used to evaluate the automated classifier. Results yielded an area under the ROC curve of 0.93 in the task of classifying candidates detected during thresholding as nodules or nonnodules.

Algorithms↗