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M L Giger

Publications and source records attributed to M L Giger.

At least 37 records · Page 2Linked to original sources
PubMed1999

Characterization of bone quality using computer-extracted radiographic features.

Both bone mineral density (BMD) and trabecular structure are important determinates of bone mechanical properties. However, neither BMD or trabecular structural features can completely explain the variations in bone mechanical properties. In this study, we combine BMD and bone structural features to characterize bone mechanical behavior. Radiographs were obtained from 34 femoral neck specimens excised during total hip arthroplasties. Each neck radiograph was digitized and a region of interest (ROI) was selected from the medial side of the femoral neck. Textural features, the global Minkoswski dimension and trabecular orientation, were extracted from each ROI image using Minkowski dimension analysis. The BMD of each specimen was measured using dual-energy x-ray absorptiometry (DXA) and subsequently normalized by bone size as measured from a standard pelvis radiograph. Mechanical testing was performed on the trabecular bone cubes machined from each femoral neck to yield bone mechanical properties. Multiple regression was performed to select the best features to predict bone mechanical properties. The results suggest that, using multiple predictors including normalized BMD structural features, and patient age, the coefficients of determination (R2) improved over the use of BMD alone. For bone strength, the R2 was improved from 0.24 using conventional BMD to 0.48 using a four-predictor model. Similar results were obtained in the prediction of Young's modulus, i.e., the R2 was improved from 0.25 to 0.55 in going from the model using conventional BMD to a four-predictor model. This study demonstrates the contributions of normalized BMD, structural features, and age to bone mechanical properties, and suggests a potential method for the noninvasive evaluation of bone mechanical properties.

Absorptiometry, Photon↗
PubMed1999

Computerized radiographic texture measures for characterizing bone strength: a simulated clinical setup using femoral neck specimens.

We are investigating computerized methods to ultimately characterize bone trabecular pattern from clinical skeletal radiographs. In this paper, we present a "phantom" for potential use in the development and evaluation of computerized methods for characterizing radiographic trabecular patterns and ultimately bone strength. Femoral neck specimens were excised during total hip arthroplasties from subjects exhibiting a range of diseases. To mimic the femoral neck in vivo, a "simulated clinical" setup was implemented in which specimens were exposed under conditions that yielded radiographs similar in appearance to standard pelvis radiographs. Fourier-based and fractal-based texture measures were used in the computer analysis; including RMS variation, first moment of the power spectrum, angular-dependent forms of these measures, and fractal dimension. The texture measures obtained from the "simulated clinical" specimen films correlated modestly with those from direct exposure "verification" films of the specimens (r= 0.59-0.69; p<0.0001). From our study, we conclude that the femoral neck specimen "phantoms" may be useful in the development and evaluation of computerized methods for analyzing bone trabecular patterns from skeletal radiographs. The use of a phantom that simulates the clinical radiographic examination allows for repeat exposures without the concern of excessive radiation exposure to a patient.

Adult↗
PubMed1999

Feature selection with limited datasets.

Computer-aided diagnosis has the potential of increasing diagnostic accuracy by providing a second reading to radiologists. In many computerized schemes, numerous features can be extracted to describe suspect image regions. A subset of these features is then employed in a data classifier to determine whether the suspect region is abnormal or normal. Different subsets of features will, in general, result in different classification performances. A feature selection method is often used to determine an "optimal" subset of features to use with a particular classifier. A classifier performance measure (such as the area under the receiver operating characteristic curve) must be incorporated into this feature selection process. With limited datasets, however, there is a distribution in the classifier performance measure for a given classifier and subset of features. In this paper, we investigate the variation in the selected subset of "optimal" features as compared with the true optimal subset of features caused by this distribution of classifier performance. We consider examples in which the probability that the optimal subset of features is selected can be analytically computed. We show the dependence of this probability on the dataset sample size, the total number of features from which to select, the number of features selected, and the performance of the true optimal subset. Once a subset of features has been selected, the parameters of the data classifier must be determined. We show that, with limited datasets and/or a large number of features from which to choose, bias is introduced if the classifier parameters are determined using the same data that were employed to select the "optimal" subset of features.

Bias↗
PubMed1998

Computerized delineation and analysis of costophrenic angles in digital chest radiographs.

RATIONALE AND OBJECTIVES: The authors developed a computerized method for delineating the costophrenic angles in digital posteroanterior chest radiographs to derive quantitative information that allows for detection of abnormal blunting of the costophrenic angle. MATERIALS AND METHODS: An automated lung-segmentation scheme was used, and small regions of interest were placed in the approximate position of the costophrenic angles in 600 clinical posteroanterior chest radiographs to define a subimage for further analysis. The diaphragmatic aspect of the costophrenic angle was delineated based on column-wise contrast information, and the costal aspect was delineated based on row-wise gray-level maxima. The angle formed by the convergence of these two aspects provided the basis for assessing abnormality. Curve fitting was then performed on these segments to form a continuous costophrenic angle delineation. RESULTS: The computer-determined angles for 1,166 hemithoraces were compared with independent diagnostic assessments by a radiologist. An encouraging level of agreement was found between these two measurements, with the area under the receiver operating characteristic curve attaining a value of 0.83. CONCLUSION: This delineation method enhances the automated lung-segmentation scheme. Quantitative information obtained from the costophrenic angles can be used for automatic evaluation of the presence of costophrenic angle blunting, which may indicate the presence of pleural effusion.

Area Under Curve↗
PubMed1998

Automated lung segmentation in digitized posteroanterior chest radiographs.

RATIONALE AND OBJECTIVES: The authors developed and tested a gray-level thresholding-based approach to automated lung segmentation in digitized posteroanterior chest radiographs. MATERIALS AND METHODS: Gray-level histogram analysis was initially performed to establish a range of thresholds for use during an iterative global gray-level thresholding technique. Local gray-level threshold analysis was then performed on the output of global thresholding. The resulting contours were subjected to several smoothing processes, including a rolling-ball technique. The final contours closely approximated the boundaries of the aerated lung regions. The method was applied to a database of 600 posteroanterior chest images. Radiologists rated the accuracy and completeness of the contours with a five-point scale. RESULTS: Results of the subjective rating evaluation indicated that this method was accurate, with 79% of the assigned ratings reflecting moderately or highly accurate segmentation and only 8% of the ratings indicating moderately or highly inaccurate segmentation. CONCLUSION: This gray-level thresholding-based approach provides accurate automated lung segmentation in digital posteroanterior chest radiographs.

Algorithms↗
PubMed1998

Automated computerized classification of malignant and benign masses on digitized mammograms.

RATIONALE AND OBJECTIVES: To develop a method for differentiating malignant from benign masses in which a computer automatically extracts lesion features and merges them into an estimated likelihood of malignancy. MATERIALS AND METHODS: Ninety-five mammograms depicting masses in 65 patients were digitized. Various features related to the margin and density of each mass were extracted automatically from the neighborhoods of the computer-identified mass regions. Selected features were merged into an estimated likelihood of malignancy by, using three different automated classifiers. The performance of the three classifiers in distinguishing between benign and malignant masses was evaluated by receiver operating characteristic analysis and compared with the performance of an experienced mammographer and that of five less experienced mammographers. RESULTS: Our computer classification scheme yielded an area under the receiver operating characteristic curve (Az) value of 0.94, which was similar to that for an experienced mammographer (Az = 0.91) and was statistically significantly higher than the average performance of the radiologists with less mammographic experience (Az = 0.81) (P = .013). With the database used, the computer scheme achieved, at 100% sensitivity, a positive predictive value of 83%, which was 12% higher than that for the performance of the experienced mammographer and 21% higher than that for the average performance of the less experienced mammographers (P < .0001). CONCLUSION: Automated computerized classification schemes may be useful in helping radiologists distinguish between benign and malignant masses and thus reducing the number of unnecessary biopsies.

Breast Neoplasms↗
PubMed1998

Automated seeded lesion segmentation on digital mammograms.

Segmenting lesions is a vital step in many computerized mass-detection schemes for digital (or digitized) mammograms. We have developed two novel lesion segmentation techniques-one based on a single feature called the radial gradient index (RGI) and one based on simple probabilistic models to segment mass lesions, or other similar nodular structures, from surrounding background. In both methods a series of image partitions is created using gray-level information as well as prior knowledge of the shape of typical mass lesions. With the former method the partition that maximizes the RGI is selected. In the latter method, probability distributions for gray-levels inside and outside the partitions are estimated, and subsequently used to determine the probability that the image occurred for each given partition. The partition that maximizes this probability is selected as the final lesion partition (contour). We tested these methods against a conventional region growing algorithm using a database of biopsy-proven, malignant lesions and found that the new lesion segmentation algorithms more closely match radiologists' outlines of these lesions. At an overlap threshold of 0.30, gray level region growing correctly delineates 62% of the lesions in our database while the RGI and probabilistic segmentation algorithms correctly segment 92% and 96% of the lesions, respectively.

Algorithms↗
PubMed1998

Automated lung segmentation in digital lateral chest radiographs.

We are developing a fully automated computerized scheme for segmenting the lung fields in digital lateral chest radiographs. Existing computer-aided diagnostic (CAD) schemes and automated lung segmentation methods have focused exclusively on the posteroanterior view, despite the diagnostic importance of the lateral view. Information from the lateral radiograph is routinely incorporated by radiologists in their decision-making process, and thus computer analysis of lateral images may potentially add another dimension to current CAD schemes. Automated analysis of the lung fields in lateral images will necessarily require accurate segmentation. Our scheme employs an initial procedure to eliminate external and subcutaneous pixels. Global gray-level histogram analysis then allows for the identification of a range of gray-level thresholds. An iterative gray-level thresholding scheme is implemented using this range of thresholds to construct a series of binary images in which contiguous regions are identified and geometrically analyzed. Regions determined to be outside the lungs are prevented from contributing to binary images at later iterations. Adaptive local gray-level thresholding is applied along the initial contour that results from the global thresholding procedure to extend the contour closer to the true lung borders. This local thresholding method uses regions of interest of various dimensions, depending on the enclosed anatomy. Smoothing and polynomial curve fitting complete the segmentation. A database of 100 normal and 100 abnormal lateral images was analyzed. Quantitative comparison of computer-segmented lung regions with lung regions manually delineated by two radiologists indicated that 83% and 84% of normal and abnormal images, respectively, displayed segmentation contours within three standard deviations of the mean inter-radiologist contour degree-of-overlap value.

Automation↗
PubMed1998

Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging.

Contrast-enhanced magnetic resonance imaging (MRI) of the breast is known to reveal breast cancer with higher sensitivity than mammography alone. The specificity is, however, compromised by the observation that several benign masses take up contrast agent in addition to malignant lesions. The aim of this study is to increase the objectivity of breast cancer diagnosis in contrast-enhanced MRI by developing automated methods for computer-aided diagnosis. Our database consists of 27 MR studies from 27 patients. In each study, at least four MR series of both breasts are obtained using FLASH three-dimensional (3D) acquisition at 90 s time intervals after injection of Gadopentetate dimeglumine (Gd-DTPA) contrast agent. Each series consists of 64 coronal slices with a typical thickness of 2 mm, and a pixel size of 1.25 mm. The study contains 13 benign and 15 malignant lesions from which features are automatically extracted in 3D. These features include margin descriptors and radial gradient analysis as a function of time and space. Stepwise multiple regression is employed to obtain an effective subset of combined features. A final estimate of likelihood of malignancy is determined by linear discriminant analysis, and the performance of classification by round-robin testing and receiver operating characteristics (ROC) analysis. To assess the efficacy of 3D analysis, the study is repeated in two-dimensions (2D) using a representative slice through the middle of the lesion. In 2D and in 3D, radial gradient analysis and analysis of margin sharpness were found to be an effective combination to distinguish between benign and malignant masses (resulting area under the ROC curve: 0.96). Feature analysis in 3D was found to result in higher performance of lesion characterization than 2D feature analysis for the majority of single and combined features. In conclusion, automated feature extraction and classification has the potential to complement the interpretation of radiologists in an objective, consistent, and accurate way.

Biophysical Phenomena↗
PubMed1997

Automated registration of ventilation-perfusion images with digital chest radiographs.

RATIONALE AND OBJECTIVES: The authors have developed an automated computerized technique for registering radionuclide lung scan images with digital chest radiographs. METHODS: Threshold analysis was used to construct contours around the high-activity regions of radionuclide ventilation-perfusion images. Analogous contours were constructed around the lung regions of the corresponding digitized radiographs. Contour dimensions and anatomic landmark locations were then used to superimpose the radiographic, ventilation, and perfusion images. RESULTS: Evaluation of 25 sets of images indicated that the scheme provided adequate to excellent registration in 91% of the pairwise combinations. CONCLUSION: This automated scheme for registering ventilation-perfusion images with digital chest radiographs has the potential to aid radiologists in the interpretation of these images.

Adult↗
PubMed1997

Development of an improved CAD scheme for automated detection of lung nodules in digital chest images.

Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital chest images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference image (which corresponds to the subtraction of a signal-enhanced image and a signal-suppressed image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per chest image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one chest image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.

Adult↗
PubMed1996

An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms.

RATIONALE AND OBJECTIVES: We evaluated the potential usefulness of a computer-assisted diagnostic (CAD) scheme incorporating the wavelet transform for detecting clustered microcalcifications in mammograms. METHODS: A wavelet transform technique was applied to the detection of clustered microcalcifications. We examined several wavelets to study their effectiveness in detecting subtle microcalcifications. We used a database consisting of 39 mammograms containing 41 clusters of microcalcifications. The performance of the wavelet-based CAD scheme was evaluated using free-response receiver operating characteristic analysis. RESULTS: The CAD scheme with the wavelet transform was useful in detecting some of the subtle microcalcifications that were not detected by our previous scheme, which was based on the difference-image technique. When the two schemes were combined, the overall performance was improved to a sensitivity of approximately 95%, with a false-positive rate of 1.5 clusters per image. CONCLUSION: The wavelet transform approach can improve the detection of subtle clustered microcalcifications.

Calcinosis↗
PubMed1996

An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms.

A shift-invariant artificial neutral network (SIANN) has been applied to eliminate the false-positive detections reported by a rule-based computer aided-diagnosis (CAD) scheme developed in our laboratory. Regions of interest (ROIs) were selected around the centers of the rule-based CAD detections and analyzed by the SIANN. In our previous study, background-trend correction and pixel-value normalization were used as the preprocessing of the ROIs prior to the SIANN. A ROI is classified as a positive ROI, if the total number of microcalcifications detected in the ROI is greater than a certain number. In this study, modifications were made to improve the performance of the SIANN. First, the preprocessing is removed because the result of the background-trend correction is affected by the size of ROIs. Second, image-feature analysis is employed to the output of the SIANN in an effort to eliminate some of the false detections by the SIANN. In order to train the SIANN to detect microcalcifications and also to extract image features of microcalcifications, the zero-mean-weight constraint and training-free-zone techniques have been developed. A cross-validation training method was also applied to avoid the overtraining problem. The performance of the SIANN was evaluated by means of ROC analysis using a database of 39 mammograms for training and 50 different mammograms for testing. The analysis yielded an average area under the ROC curve (A(z)) of 0.90 for the testing set. Approximately 62% of false-positive clusters detected by the rule-based scheme were eliminated without any loss of the true-positive clusters by using the improved SIANN with image feature analysis techniques.

Biophysical Phenomena↗
PubMed1996

Density correction of peripheral breast tissue on digital mammograms.

When digital mammograms are viewed on video displays, evaluation of the skin and subcutaneous tissue is often difficult and may require special window settings. An algorithm has been developed for selective enhancement (ie, density correction) of the dark peripheral portions of the breast on mammograms. After an automated segmentation of the digital mammogram and identification of the skin line, a fitted enhancement curve is generated to selectively enhance all pixels within a certain distance from the skin to match the density of the center part of the breast. After enhancement, skin and breast parenchyma can be evaluated simultaneously without the need for different window settings. When tested on a set of 400 digitized mammograms, the density correction algorithm significantly (P < .0001) increased the maximum area of breast tissue visualized simultaneously at window width settings of 0.5-2.0 delta OD (optical density). Artifacts interfering with interpretation were observed in less than 1%. The algorithm for correcting the density of peripheral breast tissue substantially facilitates and improves the display of digital mammograms and thus will be a valuable component of an integrated workstation for computer-aided diagnosis in mammography.

Algorithms↗
PubMed1996

Malignant and benign clustered microcalcifications: automated feature analysis and classification.

PURPOSE: To develop a method for differentiating malignant from benign clustered microcalcifications in which image features are both extracted and analyzed by a computer. MATERIALS AND METHODS: One hundred mammograms from 53 patients who had undergone biopsy for suspicious clustered microcalcifications were analyzed by a computer. Eight computer-extracted features of clustered microcalcifications were merged by an artificial neural network. Human input was limited to initial identification of the microcalcifications. RESULTS: Computer analysis allowed identification of 100% of the patients with breast cancer and 82% of the patients with benign conditions. The accuracy of computer analysis was statistically significantly better than that of five radiologists (P = .03). CONCLUSION: Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.

Breast Diseases↗
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↗