Radiographic trabecular 2D and 3D parameters of proximal femoral bone cores correlate with each other and with yield stress.

UNLABELLED: Radiographic images of bone cores taken from cadaver proximal femora provided two-dimensional parameters of projected trabecular patterns that correlated highly with conceptually equivalent three-dimensional parameters in the same cores. Measurements also highly correlated with yield stress, suggesting that both parameters have similar biomechanical qualities. INTRODUCTION: We compared morphometric measurements of trabecular patterns in two-dimensional (2D) projection radiographic images of cores from cadaver proximal femoral bones with conceptually equivalent measurements from three-dimensional microcomputed tomography (3D microCT) images. METHODS: Seven cadaver proximal femora provided 47 excised cores from seven regions. Digitized radiographs of those cores were processed with software that extracts trabecular patterns. Measurements of their distribution, geometry, and connectivity were compared with 3D parameters of similar definition derived from microCT of those cores. The relationship between 2D and 3D measurements and yield stress was also examined. RESULTS: 2D measurements strongly correlated with conceptually equivalent measurements obtained using 3D microCT. In all cases, the correlation coefficients were high, ranging from r = 0.84 (p < 0.001) to r = 0.93 (p < 0.001). The correlation coefficients between 2D and 3D measurements and yield stress of the cores were also high (r = 0.60 and 0.82, p < 0.001, respectively). CONCLUSIONS: These findings provide correlative and biomechanical evidence supporting the qualitative similarity of 2D microstructural parameters extracted from plain proximal femoral core X-ray images to conceptually equivalent 3D microstructural measurements of those same cores.

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.

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.

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.

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.

Identification of lung regions in chest radiographs using Markov random field modeling.

The authors present an algorithm utilizing Markov random field modeling for identifying lung regions in a digitized chest radiograph (DCR). Let x represent the classifications of each pixel in a DCR as either lung or nonlung. We model x as a realization of a spatially varying Markov random field. This model is developed utilizing spatial and textural information extracted from samples of lung and nonlung region-types in a training set of DCRs. With this model, the technique of Iterated Conditional Modes is used to determine the optimal classification of each pixel in a DCR. The algorithm's ability to identify lung regions is evaluated on a testing set of DCRs. The algorithm performs well yielding a sensitivity of 90.7% +/- 4.4%, a specificity of 97.2% +/- 2.0%, and an accuracy of 94.8% +/- 1.6%. In an attempt to gain insight into the meaning and level of the algorithm's performance numbers, the results are compared to those of some easily implemented classification algorithms.