Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is still required for evaluating the results.
Using the arteriography examination, it is possible to check anomalies in blood vessels and diseases such as stroke, stenosis, bleeding and especially in the diagnosis of Encephalic Death in comatose individuals. Encephalic death can be diagnosed only when there is complete interruption of all brain functions, and hence the blood stream. During the examination, there may be some interference on the sensors, such as environmental factors, poor maintenance of equipment, patient movement, among other interference, which can directly affect the noise produced in angiography images. Then, we need to use digital image processing techniques to minimize this noise and improve the pixel count. Therefore, this paper proposes to use median filter and enhancement techniques for transformation of intensity using the sigmoid function together with the Wiener filter so you can get less noisy images. It’s been realized two filtering techniques to remove the noise of images, one with the median filter and the other with the Wiener filter along the sigmoid function. For 14 tests quantified, including 7 Encephalic Death and 7 other cases, the technique that achieved a most satisfactory number of pixels quantified, also presenting a lesser amount of noise, is the Wiener filter sigmoid function, and in this case used with 0.03 cuttof.
As the technology evolves, the analog mammography systems are being replaced by digital systems. The digital system uses video monitors as the display of mammographic images instead of the previously used screen-film and negatoscope for analog images. The change in the way of visualizing mammographic images may require a different approach for training the health care professionals in diagnosing the breast cancer with digital mammography. Thus, this paper presents a computational approach to train the health care professionals providing a smooth transition between analog and digital technology also training to use the advantages of digital image processing tools to diagnose the breast cancer. This computational approach consists of a software where is possible to open, process and diagnose a full mammogram case from a database, which has the digital images of each of the mammographic views. The software communicates with a gold standard digital mammogram cases database. This database contains the digital images in Tagged Image File Format (TIFF) and the respective diagnoses according to BI-RADSTM, these files are read by software and shown to the user as needed. There are also some digital image processing tools that can be used to provide better visualization of each single image. The software was built based on a minimalist and a user-friendly interface concept that might help in the smooth transition. It also has an interface for inputting diagnoses from the professional being trained, providing a result feedback. This system has been already completed, but hasn’t been applied to any professional training yet.
As all women over the age of 40 are recommended to perform mammographic exams every two years, the
demands on radiologists to evaluate mammographic images in short periods of time has increased considerably. As a tool
to improve quality and accelerate analysis CADe/Dx (computer-aided detection/diagnosis) schemes have been
investigated, but very few complete CADe/Dx schemes have been developed and most are restricted to detection and not
diagnosis. The existent ones usually are associated to specific mammographic equipment (usually DR), which makes
them very expensive. So this paper describes a prototype of a complete mammography CADx scheme developed by our
research group integrated to an imaging quality evaluation process. The basic structure consists of pre-processing
modules based on image acquisition and digitization procedures (FFDM, CR or film + scanner), a segmentation tool to
detect clustered microcalcifications and suspect masses and a classification scheme, which evaluates as the presence of
microcalcifications clusters as well as possible malignant masses based on their contour. The aim is to provide enough
information not only on the detected structures but also a pre-report with a BI-RADS classification. At this time the
system is still lacking an interface integrating all the modules. Despite this, it is functional as a prototype for clinical
practice testing, with results comparable to others reported in literature.
The purpose of this work is the evaluation and analysis of Bayesian network models in order to classify clusters of microcalcifications to supply a second opinion to the specialists in the detection of breast diseases by mammography. Bayesian networks are statistics techniques, which provide explanation about the inferences and influences among features and classes of a determinated problem. Therefore, the technique investigation will aid in obtaining more detailed information to the diagnosis in a CAD scheme. From regions of interest (ROI), containing clusters of microcalcifications, detailed image analysis, pixel to pixel; in this step shape using geometric descriptors (Hu Invariant Moments, second and third order moments and radius gyration); irregularity measure; compactness; area and perimeter extracted descriptors. By using software of Bayesian network models construction, different Bayesian network classifier models could be generated, using the extracted features mentioned above in order to verify their behavior and probabilistic influences and used as the input to Bayesian network, some tests were performed in order to build the classifier. The results of generated nets models validation correspond to an average of 10 tests made with 6 different database sub-groups. The first results of validation have shown 83.17% of correct results.
This work presents a classifier for mammographic masses using the wavelet transform as characteristics generator. It considers the BI-RADS classification, dividing mass according to their shapes: circulate, nodular and speculate. We developed procedures with two steps: the first involves a model applying one wavelet technique performing the contours analysis with simulated mass images. This procedure was used to choose the best wavelet that could generate the desired characteristics. The second procedure had the objective of applying the chosen wavelet to masses from segmented images. Both methods have as answers three classes of shape. A root-mean-square function is applied to obtain the energy measure for each level of wavelet decomposition. Thus the shape feature vectors are formed with the coefficients of the details and coefficients of approximation extracted by the energy of wavelet decomposition levels. Linear Discriminan Analysis (LDA) by using Fischer Discriminant was used to reduce the number of characteristics for the feature vector. The Mahalanobis distance was used by the classifier to verify aimed the pertinence of the images for each one the previously given classes. To test actual images, the leave-one-out method was used to the classifier training. The classifier has registered good results, compared to others reports in the corresponding literature.
Function approximation is a very important task in environments where the computation has to be based on extracting information from data samples in real world processes. So, the development of new mathematical model is a very important activity to guarantee the evolution of the function approximation area. In this sense, we will present the Polynomials Powers of Sigmoid as a linear neural network. In this paper, we will introduce one series of practical results for the Polynomials Powers of Sigmoid, where we will show some advantages of the use of the powers of sigmoid functions in relationship the traditional MLP- Backpropagation and Polynomials in functions approximation problems.