We have been developing radiographic texture analysis (RTA) for assessing osteoporosis and the related risk of fracture.
Currently, analyses are performed on heel images obtained from a digital imaging device, the GE/Lunar PIXI, that yields
both the bone mineral density (BMD) and digital images (0.2-mm pixels; 12-bit quantization). RTA is performed on the
image data in a region-of-interest (ROI) placed just below the talus in order to include the trabecular structure in the
analysis. We have found that variations occur from manually selecting this ROI for RTA. To reduce the variations, we
present an automatic method involving an optimized Canny edge detection technique and parameterized bone
segmentation, to define bone edges for the placement of an ROI within the predominantly calcaneus portion of the
radiographic heel image. The technique was developed using 1158 heel images and then tested on an independent set of
176 heel images. Results from a subjective analysis noted that 87.5% of ROI placements were rated as "good". In
addition, an objective overlap measure showed that 98.3% of images had successful ROI placements as compared to
placement by an experienced observer at an overlap threshold of 0.4. In conclusion, our proposed method for automatic
ROI selection on radiographic heel images yields promising results and the method has the potential to reduce intra- and
inter-observer variations in selecting ROIs for radiographic texture analysis.
Tomosynthesis is emerging as a promising modality for breast imaging. Several manufacturers have developed prototype
units and have acquired clinical and phantom data. Scanning configurations of these prototypes vary. So far, studies
relating scanning configuration to image quality have been limited to those geometries that could be implemented on a
particular prototype. To overcome this limitation, we are developing a model of breast tomosynthesis image acquisition
system, which models the formation of the x-ray image and x-ray detector.
The x-ray image of an object is computed analytically for a polychromatic x-ray beam. Objects consist of volumetric
regions that are bounded by either a planar, ellipsoidal, cylindrical or conical surface, allowing for a variety of objects. xray
scatter is computed by convolving the image with a scatter point-spread function. Poisson noise according to the
entrance exposure is added to the image.
The x-ray detector in this model is composed of a phosphor screen followed by a detector array. X-ray interactions in the
screen are modeled as depth-dependent. The optical output of the screen is converted into digital units using a gain factor
which was assumed to be Gaussian distributed.
To validate this data model, we acquired images of a contrast-detail phantom on a stereotactic biopsy unit. The x-ray
source is mounted on an arm that pivots in a plane about the detector center. The x-ray detector consists of a Min-R type
screen fiber-optically coupled to a CCD camera.
To compare actual and simulated data, we compared line profiles as well as several automatically extracted image
features such as contrast-to-noise ratio, contrast, area and radial gradient index. Good agreement was found between
simulation and physical data, indicating that we can now use this model to explore image quality for various
tomosynthesis scanning configurations.
We have developed a method for producing simulated mammograms from high fidelity breast specimen radiographs. The method has the advantage of having access to all the truth information for the lesions. By modeling different parts of a screen-film system, we simulated the output of the system, and compared it to the real mammography images from the same samples. In this work we show how our simulation program produces realistic mammography images and also the observer study that tests how well the observers can distinguish the real and simulated images. Preliminary results from the ROC study show that the observers could not distinguish the two types of images very well.
Mammographic parenchymal patterns have been shown to be associated with breast cancer risk. Fractal-based texture analyses, including box-counting methods and Minkowski dimension, were performed within parenchymal regions of normal mammograms of BRCA1/BRCA2 gene mutation carriers and within those of women at low risk for developing breast cancer. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of the computerized radiographic markers in the task of distinguishing between high and low-risk subjects. A multifractal phenomenon was observed with the fractal analyses. The high frequency component of fractal dimension from the conventional box-counting technique yielded an Az value of 0.84 in differentiating between two groups, while using the LDA to estimate the fractal dimension yielded an Az value of 0.91 for the high frequency component. An Az value of 0.82 was obtained with fractal dimensions extracted using the Minkowski algorithm.
We previously developed bone texture analysis methods to assess bone strength on digitized radiographs. Here, we compare the analyses performed on digitized screen-film to those obtained on peripheral bone densitometry images. A leg phantom was imaged with both a PIXI (GE Medical Systems; Milwaukee, WI) bone densitometer (0.200-mm pixel size) and a screen-film system, with the films being subsequently digitized by a laser film digitizer (0.100-mm pixel size). The phantom was radiographically scanned multiple times with the densitometer at the default parameters and for increasing exposure times. Fourier-based texture features were calculated from regions of interest from images from both modalities. The bone densitometry images contained more quantum noise than the radiographs resulting in increased values for the first moment of the power spectrum texture feature (1.22 times higher than from the standard radiograph). Presence of such noise may adversely affect the texture feature's ability to distinguish between strong and weak bone. By either increasing the exposure time or averaging multiple scans in the spatial frequency domain, we showed a reduction in the effect of the quantum mottle on the first moment of the power spectrum.