Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detectio

Automatic detection of clustered microcalcifications in digital mammograms

In this paper we propose a new algorithm for the detection of clustered microcalcifications using mathematical morphology and artificial neural networks. Considering each mammogram as a topographic representation, each microcalcification appears as elevation constituting a regional maxima. Morphological filters are applied, in order to remove noise and regional maxima that doesn't correspond to calcifications. Each suspicious object is marked using a binary image and finally a feed forward neural network classifies every object achieving a rate of 90% true positive detections with 0.11 false positives per image

Neural networks for the prediction of spirometric reference values

Normal lung function values are conventionally calculated according to prediction equations. The primary objective of this study is the development of a different method for the prediction of FVC and FEV1 parameters, in order to achieve better correlation of the predicted values to the real ones. Using a sample from the Greek elderly population that was separated into two groups (a training and a testing one), a number of artificial neural networks were trained. Considering that men and women were studied separately and that two parameters (FVC, FEV1) were the target of the study, four cases came up. In each case two neural networks were trained using different transfer functions, number of neurons and number of layers. When passing the inputs of the testing data set to the trained networks it was found that the outputs were well correlated with the corresponding measures of the sample. Furthermore, the match with the sample, for a number of neural networks developed, was better compared to the matches of Baltopoulos et al. study that used the same sample for developing prediction equations. This high match allows the potential use of neural networks for predicting not only FVC and FEV1 but also other spirometric parameters

Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks

In this paper we propose a new algorithm for the detection of clustered microcalcifications using mathematical morphology and artificial neural networks. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a non-uniform background. Considering each mammogram as a topographic representation, each microcalcification appears as an elevation constituting a regional maximum. Morphological filters are applied, in order to remove: (a) noise and (b) regional maxima that do not correspond to calcifications. Each candidate object is marked as such, using a binary image. The original mammogram is used for the final feature extraction step. For the classification step we employ neural network classifiers. We review the performance of two multi-layer perceptrons (MLP) and two radial basis function neural networks (RBFNN) with different number of hidden nodes. The MLP with ten hidden nodes achieved the best classification score with a true positive detection rate of 94.7% and 0.27 false positives per image. © 2007 Elsevier B.V. All rights reserved

Computer simulation of the human respiratory system for educational purposes

The main objective of this study was the development of a computer simulation system for the human respiratory system, in order to educate students of nursing. This approach was based on existing mathematical models and on our own constructed specific functions. For the development of this educational tool the appropriate software packages were used according to the special demands of this process. This system is called ReSim (Respiratory Simulation) and consists of two parts: the first part deals with pulmonary volumes and the second one represents the mechanical behavior of lungs. The target group evaluated ReSim. The outcomes of the evaluation process were positive and helped us realize the system characteristics that needed improvements. Our basic conclusion is that the extended use of such systems supports the educational process and offers new potential for learning. © 2004 Lippincott Williams & Wilkins, Inc