We propose techniques to analyze bilateral asymmetry in mammograms by combining directional information, morphological measures, and geometric moments related to density distributions. The procedure starts by detecting the breast boundary and the pectoral muscle edge (in mediolateral-oblique, or MLO, views). All artifacts outside the breast boundary as well as the pectoral muscle region are eliminated. A breast density model based upon a Gaussian mixture model is then used to segment the fibroglandular disks of the mammograms. Rose diagrams are used to map the magnitude and directional information related to the fibroglandular tissue filtered using multiresolution Gabor wavelets. The directional data of the left and right mammograms are aligned by using the straight lines perpendicular to the corresponding pectoral muscle edges and subtracted to obtain difference rose diagrams. Directional features are obtained from the difference rose diagrams and used to characterize the changes caused by the development of breast cancer in the form of bilateral asymmetry or architectural distortion. An additional set of features including Hu's moments, eccentricity, stretch, area, and average density are extracted from the segmented fibroglandular disks. The differences between the pairs of the features for the left and right mammograms are used as measures for the analysis of asymmetry. The techniques were applied to 88 mammograms from the Mini-MIAS database. Classification accuracies of up to 84.4% were achieved, with sensitivity and specificity rates of 82.6% and 86.4%, respectively.
In this paper we present preliminary results to automatically segment multiple sclerosis (MS) lesions in multispectral magnetic resonance datasets using support vector machines (SVM). A total of eighteen studies (each composed of T1-, T2-weighted and FLAIR images) acquired from a 3T GE Signa scanner was analyzed. A neuroradiologist used a computer-assisted technique to identify all MS lesions in each study. These results were used later in the training and testing stages of the SVM classifier. A preprocessing stage including anisotropic diffusion filtering, non-uniformity intensity correction, and intensity tissue normalization was applied to the images. The SVM kernel used in this study was the radial basis function (RBF). The kernel parameter (γ) and the penalty value for the errors were determined by using a very loose stopping criterion for the SVM decomposition. Overall, a 5-fold cross-validation accuracy rate of 80% was achieved in the automatic classification of MS lesion voxels using the proposed SVM-RBF classifier.
The aim of this work was to determine a methodology to selection of the best features subset and artificial neural network (ANN) topology to classify masses lesions. The backpropagation training algorithm was used to adjust the weights of ANN. A total of 118 regions of interest images were chosen (68 benign and 50 malignant lesions). In a first step, images were submitted to a combined process of thresholding, mathematical morphology, and region growing techniques. After, fourteen texture features (Haralick descriptors) and fourteen shape features (circularity, compactness, Gupta descriptors, Shen descriptors, Hu descriptors, Fourier descriptor and Wee descriptors) were extracted. The Jeffries-Matusita method was used to select the best features. Three shape features sets and three texture features sets were selected. The Receiver Operating Characteristic (ROC) analyses were conducted to evaluated the classifier performance. The best result for shape feature set was accurate classification rate of 98.21%, specificity of 98.37%, sensitivity of 98.00% and the area under ROC curve of 0.99, for a ANN with 5 hidden units. The best result for texture feature set was accurate classification rate of 97.08%, specificity of 98.53%, sensitivity of 95.11% and the area under ROC curve of 0.98, for an ANN with 4 hidden units.
This paper proposes a method of evaluating x-ray tube focal spots and the corresponding image sharpness by computer simulation based on the transfer functions theory. This theory was chosen due to its quantitative as well as qualitative response for the radiographic systems performance, which provides less subjective evaluations and better predictions about the characteristics of the imaging process. The present method uses as input data the effective focal spot dimensions in the field center and the value of the target angulation. An ideal pinhole which scans the entire radiation field is simulated. It allows to obtain the point spread function (PSFs) for any region of interest. The modulation transfer functions (MTFs) are then determined from 2D Fourier transformation from the PSFs. This provides to evaluate the focal spot projection in all field locations and therefore to predict the sharpness of the associated image. Furthermore the computer simulation reduces greatly the number of practical procedures required for obtaining the data which provides the MTF evaluation of radiographic systems.
The magnitude of the image geometric unsharpness depends on the location in the field where the object is hit by the x- ray beam. This phenomenon is known as field characteristics and is caused by the target plane angulation. This yields different effective focal spot sizes and shapes when it is 'seen' from different directions and locations in the x-ray field . Due to the effect of the field characteristics, a more detailed evaluation of focal spot behavior in radiology systems is needed. Hence the focal spot should be evaluated in all field locations, which is very complex with experimental procedures, although feasible by computer simulation. This work describes an algorithm with the aim of determining the size and shape of effective focal spots in any location of the radiation field, on the basis of the focal spot size measurement in the filed center. The results obtained by the program have agreed with those obtained by pinholes matrix exposures in several radiology faculties. The program has proved efficient in computing the size of the focal spot projections for mammography systems, with a standard deviation around 0.03-0.04 mm.
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