Antonio García-Manso

Study of the Effect of Breast Tissue Density on Detection of Masses in Mammograms

One of the parameters that are usually stored for mammograms is the BI-RADS density, which gives an idea of the breast tissue composition. In this work, we study the effect of BI-RADS density in our ongoing project for developing an image-based CAD system to detect masses in mammograms. This system consists of two stages. First, a blind feature extraction is performed for regions of interest (ROIs), using Independent Component Analysis (ICA). Next, in the second stage, those features form the input vectors to a classifier, neural network, or SVM classifier. To train and test our system, the Digital Database for Screening Mammography (DDSM) was used. The results obtained show that the maximum variation in the performance of our system considering only prototypes obtained from mammograms with a concrete value of density (both for training and test) is about 7%, yielding the best values for density equal to 1, and the worst for density equal to 4, for both classifiers. Finally, with the overall results (i.e., using prototypes from mammograms with all the possible values of densities), we obtained a difference in performance that is only 2% lower than the maximum, also for both classifiers.

Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction

BackgroundBreast cancer continues to be a leading cause of cancer deaths among women, especially in Western countries. In the last two decades, many methods have been proposed to achieve a robust mammography‐based computer aided detection (CAD) system. A CAD system should provide high performance over time and in different clinical situations. I.e., the system should be adaptable to different clinical situations and should provide consistent performance.MethodsWe tested our system seeking a measure of the guarantee of its consistent performance. The method is based on blind feature extraction by independent component analysis (ICA) and classification by neural networks (NN) or SVM classifiers. The test mammograms were from the Digital Database for Screening Mammography (DDSM). This database was constructed collaboratively by four institutions over more than 10 years. We took advantage of this to train our system using the mammograms from each institution separately, and then testing it on the remaining mammograms. We performed another experiment to compare the results and thus obtain the measure sought. This experiment consists in to form the learning sets with all available prototypes regardless of the institution in which them were generated, obtaining in that way the overall results.ResultsThe smallest variation from comparing the results of the testing set in each experiment (performed by training the system using the mammograms from one institution and testing with the remaining) with those of the overall result, considering the success rate for an intermediate decision maker threshold, was roughly 5%, and the largest variation was roughly 17%. But, if we considere the area under ROC curve, the smallest variation was close to 4%, and the largest variation was about a 6%.ConclusionsConsidering the heterogeneity in the datasets used to train and test our system in each case, we think that the variation of performance obtained when the results are compared with the overall results is acceptable in both cases, for NN and SVM classifiers. The present method is therefore very general in that it is able to adapt to different clinical situations and provide consistent performance.

Study of a mammographic CAD performance dependence on the considered mammogram set

This work analyzes the influence of the set of mammograms used in the training processes of a computer aided diagnosis system on the overall performance. We used the mammograms provided by the Digital Database for Screening Mammography, one of the most extended research database. The obtained results seem to suggest an effect on the performance values obtained in a CAD system with different database subsets. Therefore, in order to make valid comparisons between CAD systems, the specification of the mammogram set used to test the system is of the utmost importance.

Microcalcifications detection in digital mammograms

A method is presented for detecting minimally sized microcalcifications on ma mammograms to add extra security to the radiologists classification. The method imitates the normal procedure followed by the specialist, and is easily implemented on low-cost PCs. As input, it accepts the usual digital mammograms. Tested against one of the most extensive databases - the DDSM of the University of South Florida - it gave a 100% success rate. For any suspicious regions (the so-called regions-of-interest or ROI) a separate image of suitable size is generated and displayed. The system also allows feature vectors to be generated for use in an automatic classifying system - such as a neural network (NN) - to determine the malignancy of the ROIs that were detected.

Semi-automatic Measure and Identification of Allergenic Airborne Pollen

Current lifestyle in developed countries makes the practice of outdoor activities to be almost mandatory. But, since these practices such as trekking, biking, horseback, or simply running or walking in urban parks, are made in nature (at least outdoors) not everyone can practice them in optimal physical conditions at any time of the year. We are referring to those who suffer from pollinosis or “hay fever”.

Robustness of a CAD system on digitized mammograms

In this paper we study the robustness of our CAD system, since this is one of the main factors that determine its quality. A CAD system must guarantee consistent performance over time and in various clinical situations. Our CAD system is based on the extraction of features from the mammographic image by means of Independent Component Analysis, and machine learning classifiers, such as Neural Networks and Support Vector Machine. To measure the robustness of our CAD system we have used the digitized mammograms of the USFs DDSM database, because this database was built by digitizing mammograms from four different institutions (four different scanner) during more than 10 years. Thus, we can use the mammograms digitized with one scanner to train the system and the remaining to evaluate the performance, what gives us a measure of the robustness of our CAD system.

Image analysis applied to morphological assessment in bovine livestock

Morphological assessment is one important parameter considered in conservation and improvement programs of bovine livestock. This assessment process consists of scoring an animal attending to its morphology, and is normally carried out by highly-qualified staff. In this paper, a system designed to provide an assessment based on a lateral image of the cow is presented. The system consists of two main parts: a feature extractor stage, to reduce the information of the cow in the image to a set of parameters, and a neural network stage to provide a score considering that set of parameters. For the image analysis section, a model of the object is constructed by means of point distribution models (PDM). Later, that model is used in the searching process within each image, that is carried out using genetic algorithm (GA) techniques. As a result of this stage, the vector of weights that describe the deviation of the given shape from the mean is obtained. This vector is used in the second stage, where a multilayer perceptron is trained to provide the desired assessment, using the scores given by experts for selected cows. The system has been tested with 124 images corresponding to 44 individuals of a special rustic breed, with very promising results, taking into account that the information contained in only one view of the cow is not complete.

Application of Neural Networks to Morphological Assessment in Bovine Livestock

In conservation and improvement programs of bovine livestock, an important parameter is morphological assessment, which consist of scoring an animal attending to its morphology, and is always performed by highly-qualified staff.

Effect of BI-RADS assessment in improving CAD of masses

In this work we study how the BI-RADS assessment could help to improve the performance of a CAD (Computer Aided Diagnosis) image-based system in the task of masses diagnosis Our system is based on the use of Independent Component Analysis (ICA) as feature extractor from mammographic images, and Neural Networks as a final classifier For our tests, the “Digital Database for Screening Mammography” (DDSM) has been used, particularly the subset BCRP_MASS1 The best results were obtained when we used the image data (with feature extraction by means of ICA) together with the BI-RADS assessment provided by DDSM database.

Comparing feature extraction techniques and classifiers in the handwritten letters classification problem

The aim of this study is to compare the performance of two feature extraction techniques, Independent Component Analysis (ICA) and Principal Component Analysis (PCA) and also to compare two different kinds of classifiers, Neural Networks and Support Vector Machine (SVM). To this aim, a system for handwritten letters recognition was developed, which consist of two stages: a feature extraction stage using either ICA or PCA, and a classifier based on neural networks or SVM. To test the performance of the system, the subset of uppercase letters of the NIST#19 database was used. From the results of our tests, it can be concluded that when a neural network is used as classifier, the results are very similar with the two feature extraction techniques (ICA and PCA). But when the SVM classifier is used, the results are quite different, performing better the feature extractor based on ICA.