Automated quantification of three-dimensional subject motion to monitor image quality in high-resolution peripheral quantitative computed tomography.

Subject motion during acquisition of high-resolution peripheral quantitative computed tomography (HR-pQCT) results in image artifacts and interferes with quantification of bone architecture used to study bone-related diseases such as osteoporosis. We propose an automatic method to measure physical subject motion that frequently takes place during acquisition. Three measures derived from projection data are proposed to quantify motion artifacts: in-plane translation (ε(T)) and in-plane rotation (ε(R)) utilizing projection moments and longitudinal translation (ε(z)) based on tracking projection profiles. Validation was performed using a phantom containing sections of distal human cadaver radii attached to a mechanical device to precisely control in-plane rotation and longitudinal translation that was intentionally performed during HR-pQCT data acquisition. Motion measured by the new automated technique was compared to the known applied motion, and related to percent errors in morphological parameters quantifying bone properties. It was determined that of the three proposed measures, ε(T) best captured a quantified representation of image quality. ε(T) linearly relates to true physical in-plane translational motion (r(2) = 0.95, p<0.001) and is independent from longitudinal translational motion as well as the object being scanned. Additionally, ε(z) captures large longitudinal movements and combines well with ε(T) to fully characterize physical motion artifacts. The magnitude of ε(T) corresponds to morphological parameter error and is an excellent basis to select high-quality images. Morphological parameter errors from these experiments confirmed our earlier computer simulations which showed that increased subject motion resulted in artificially higher trabecular number, and artificially lower bone mineral density and cortical thickness. The magnitude and, notably, the uncertainty of the morphological errors increased with increased physical motion, and this impedes a direct linear compensation of parameter errors. The automated method presented provides a basis for consistent and objective quality assurance for HR-pQCT scanning, and addresses an important challenge for this novel imaging modality that is rapidly becoming an important basis for assessment and monitoring of bone quality.

Detection of the optic nerve head in fundus images of the retina with Gabor filters and phase portrait analysis.

We propose a method using Gabor filters and phase portraits to automatically locate the optic nerve head (ONH) in fundus images of the retina. Because the center of the ONH is at or near the focal point of convergence of the retinal vessels, the method includes detection of the vessels using Gabor filters, detection of peaks in the node map obtained via phase portrait analysis, and an intensity-based condition. The method was tested on 40 images from the Digital Retinal Images for Vessel Extraction (DRIVE) database and 81 images from the Structured Analysis of the Retina (STARE) database. An ophthalmologist independently marked the center of the ONH for evaluation of the results. The evaluation of the results includes free-response receiver operating characteristics (FROC) and a measure of distance between the manually marked and detected centers. With the DRIVE database, the centers of the ONH were detected with an average distance of 0.36 mm (18 pixels) to the corresponding centers marked by the ophthalmologist. FROC analysis indicated a sensitivity of 100% at 2.7 false positives per image. With the STARE database, FROC analysis indicated a sensitivity of 88.9% at 4.6 false positives per image.

Effect of pixel resolution on texture features of breast masses in mammograms.

The effect of pixel resolution on texture features computed using the gray-level co-occurrence matrix (GLCM) was analyzed in the task of discriminating mammographic breast lesions as benign masses or malignant tumors. Regions in mammograms related to 111 breast masses, including 65 benign masses and 46 malignant tumors, were analyzed at pixel sizes of 50, 100, 200, 400, 600, 800, and 1,000 µm. Classification experiments using each texture feature individually provided accuracy, in terms of the area under the receiver operating characteristics curve (AUC), of up to 0.72. Using the Bayesian classifier and the leave-one-out method, the AUC obtained was in the range 0.73 to 0.75 for the pixel resolutions of 200 to 800 µm, with 14 GLCM-based texture features using adaptive ribbons of pixels around the boundaries of the masses. Texture features computed using the ribbons resulted in higher classification accuracy than the same features computed using the corresponding regions within the mass boundaries. The t test was applied to AUC values obtained using 100 repetitions of random splitting of the texture features from the ribbons of masses into the training and testing sets. The texture features computed with the pixel size of 200 µm provided the highest average AUC with statistically highly significant differences as compared to all of the other pixel sizes tested, except 100 µm.

Detection of blood vessels in fundus images of the retina using Gabor wavelets.

The monitoring of the effects of diabetes, hypertension, and premature birth on the visual system can be assisted by quantitative analysis of the vascular architecture of the retina. The application of image analysis techniques in ophthalmology becomes possible after the desired features have been detected through the use of an appropriate method. We propose image processing techniques for the detection of blood vessels in the retina. The methods include the design of a bank of directionally sensitive Gabor filters for several values of the scale and elongation parameters. Forty images of the retina from the DRIVE database were used to evaluate the performance of the methods. High efficiency in the detection of blood vessels with the area under the receiver operating characteristics curve of up to 0.96 was achieved.

Three-dimensional segmentation of the tumor in computed tomographic images of neuroblastoma.

Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, and normal tissue are often intermixed. Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to therapy and in the planning of the delayed surgery for resection of the tumor. We propose methods to achieve 3-dimensional segmentation of the neuroblastic tumor. In our scheme, some of the normal structures expected in abdominal CT images are delineated and removed from further consideration; the remaining parts of the image volume are then examined for tumor mass. Mathematical morphology, fuzzy connectivity, and other image processing tools are deployed for this purpose. Expert knowledge provided by a radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are incorporated into the segmentation algorithm. In this preliminary study, the methods were tested with 10 CT exams of four cases from the Alberta Children's Hospital. False-negative error rates of less than 12% were obtained in eight of 10 exams; however, seven of the exams had false-positive error rates of more than 20% with respect to manual segmentation of the tumor by a radiologist.

Gabor filters and phase portraits for the detection of architectural distortion in mammograms.

Architectural distortion is a subtle abnormality in mammograms, and a source of overlooking errors by radiologists. Computer-aided diagnosis (CAD) techniques can improve the performance of radiologists in detecting masses and calcifications; however, most CAD systems have not been designed to detect architectural distortion. We present a new method to detect and localise architectural distortion by analysing the oriented texture in mammograms. A bank of Gabor filters is used to obtain the orientation field of the given mammogram. The curvilinear structures (CLS) of interest (spicules and fibrous tissue) are separated from confounding structures (pectoral muscle edge, parenchymal tissue edges, breast boundary, and noise). The selected core CLS pixels and the orientation field are filtered and downsampled, to reduce noise and also to reduce the computational effort required by the subsequent methods. The downsampled orientation field is analysed to produce three phase portrait maps: node, saddle, and spiral. The node map is further analysed in order to detect the sites of architectural distortion. The method was tested with 19 mammograms containing architectural distortion. In a preliminary experiment, a sensitivity of 84% was obtained at 7.8 false positives per image.