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We hypothesize that quantification of structural similarity or dissimilarity between paired mammographic regions can be effective in detecting asymmetric signs of breast cancer. Bilateral masking procedures are applied for this purpose by using automatically detected anatomical landmarks. Changes in structural information of the extracted regions are investigated using spherical semivariogram descriptors and correlation-based structural similarity indices in the spatial and complex wavelet domains. The spatial distribution of grayscale values as well as of the magnitude and phase responses of multidirectional Gabor filters are used to represent the structure of mammographic density and of the directional components of breast tissue patterns, respectively. A total of 188 mammograms from the DDSM and mini-MIAS databases, consisting of 47 asymmetric cases and 47 normal cases, were analyzed. For the combined dataset of mammograms, areas under the receiver operating characteristic curves of 0.83, 0.77, and 0.87 were obtained, respectively, with linear discriminant analysis, the Bayesian classifier, and an artificial neural network with radial basis functions, using the features selected by stepwise logistic regression and leave-one-patient-out cross-validation. Two-view analysis provided accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively.

According to the World Health Organization, breast cancer is the main cause of cancer death among women in the world. Until now, there are no effective ways of preventing this disease. Thus, early screening and detection is the most effective method for rising treatment success rates and reducing death rates due to breast cancer. Mammography is still the most used as a diagnostic and screening tool for early breast cancer detection. In this work, we propose a method to segment and classify masses using the regions of interest of mammographic images. Mass segmentation is performed using a fuzzy active contour model obtained by combining Fuzzy C-Means and the Chan-Vese model. Shape and margin features are then extracted from the segmented masses and used to classify them as benign or malignant. The generated features are usually imprecise and reflect an uncertain representation. Thus, we propose to analyze them by a possibility theory to deal with imprecise and uncertain aspect. The experimental results on Regions Of Interest (ROIs) extracted from MIAS database indicate that the proposed method yields good mass segmentation and classification results.

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Abstract-This paper describes a novel architecture of fault tolerant Solid State Mass Memory (SSMM) for satellite applications. Mass memories with low-latency time, high throughput, and storage capabilities cannot be easily implemented using space qualified components, due to the inevitable technological delay of these kind of components. For this reason, the choice of Commercial Off The Shelf (COTS) components is mandatory for this application. Therefore, the design of an electronic system for space applications, based on commercial components, must match the reliability requirements using system level methodologies [1], [2]. In the proposed architecture error-correcting codes are used to strengthen the commercial Dynamic Random Access Memory (DRAM) chips, while the system controller is developed by applying fault tolerant design solutions. The main features of the SSMM are the dynamic reconfiguration capability, and the high performances which can be gracefully reduced in case of permanent faults, maintaining part of the system functionality. This paper shows the system design methodology, the architecture, and the simulation results of the SSMM. The properties of the building blocks are described in detail both in their functionality and fault tolerant capabilities. A detailed analysis of the system reliability and data integrity is reported. The graceful degradation capability of our system allows different levels of acceptable performances, in terms of active I/O link Interfaces and storage capability. The results also show that the overall reliability of the SSMM is almost the same using different RS coding schemes, allowing a dynamic reconfiguration of the coding to reduce the latency (shorter codewords), or to improve the data integrity (longer codewords). The use of a scrubbing technique can be useful if a high SEU rate is expected, or if the data must be stored for a long period in the SSMM.The reported simulations show the behavior of the SSMM in presence of permanent and transient faults. In fact, we show that the SCU is able to recover from transient faults. On the other hand, using a spare microcontroller also hard faults can be tolerated. The distributed file system confines the unrecoverable fault effects only in a single I/O Interface. In this way, the SSMM maintains its capability to store and read data. The proposed system allows obtaining SSMM characterized by high reliability and high speed due the intrinsic parallelism of the switching matrix.