Computer-aided diagnosis in chest radiography

Chest radiographs account for more than half of all radiological examinations; the chest is the mirror of health and disease. This thesis is about techniques for computer analysis of chest radiographs. It describes methods for texture analysis and segmenting the lung fields and rib cage in a chest film. It includes a description of an automatic system for detecting regions with abnormal texture, that is applied to a database of images from a tuberculosis screening program

Computer Simulation and the Practice of Oral Medicine and Radiology

The practice of Oral Medicine and Radiology has long been considered an art form. Collecting and collimating the enormous amount of information each patient brings has always tested the best of our abilities as diagnosticians. However, as the tide of smartphones, cheaper data access, and automation rises, it threatens to wash away all that we have held sacrosanct about conventional clinical practices. In this tussle between what is traditional and what is tantalizing, it is time to question, as diagnosticians, how much can we accede to the invasion of algorithms. How does computer simulation affect the practice of diagnosis in the field of Oral Medicine and Radiology

Segmentation of lung fields in digital chest radiographs by artificial neural networks

Lung field segmentation is a basic step for virtually any quantitative procedure. In this view, due to the imaging process and the complexity of the imaged district, an efficient use of prior anatomical knowledge is crucial. In this report we describe a new approach to lung field segmentation which is based on fuzzy boundary modeling and a neural network architecture including supervised multilayer networks and topology preserving maps

Computer-aided detection of interstitial lung diseases: A texture approach

We have developed the flexible scheme for computer-aided detection (CAD) of interstitial lung diseases on chest radiographs. These schemes enable us to perform diagnostics in the broad circumstances of pneumonia and other interstitial lung diseases. It is applied in the case of children pneumonia when conditions are difficult to standardize. In the adults' case the schemes of CAD are more adaptive, as there are more characteristic interstitial lung tissue's changes to all kinds of pathological conditions. Even in the norm of drawing there are more visible and more highlighted features, leading to better results. The CAD scheme works as follows. For the first of all, we are using adopted algorithms of active contours to select the area of lungs, and then to divide this area into subareas - regions of interest (40 different ROI). Then ROIs were subjected to the 2-dimensional Daubechies wavelet transform, and only main transformation was used. For every transformation 12 texture measures were calculated. Principal component analysis (PCA) was used to extract 2 main components for each ROI, and these components were compared to predictive component region

Системы поддержки принятия решений в хирургии

ХИРУРГИЯРЕШЕНИЯ КЛИНИЧЕСКИЕ, ИНФОРМАЦИОННЫЕ СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯКОМПЬЮТЕРНЫЕ СИСТЕМ

Проблемы применения технологий машинного обучения в задачах автоматической обработки медицинских изображений

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