Abstract
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.
Highlights
The radiographs are the most common examination tools in medical practice
Diseases are often indicated by the analysis of lung textures, searching for specific formations inside lungs
It is difficult for c Vilnius University, 2017 radiologists to detect and characterize lesions on a chest radiograph when those lesions are low in contrast and/or overlap with ribs and large pulmonary vessels
Summary
The radiographs are the most common examination tools in medical practice. It plays an important role in the diagnosis of pulmonary diseases, in radiography formed by fluorescence (used in biomedical research), in radiographs clinical role in chiropractic, in diagnosing of tuberculosis, etc. [5, 6, 8, 16]. Diseases are often indicated by the analysis of lung textures, searching for specific formations inside lungs. It is difficult for c Vilnius University, 2017 radiologists to detect and characterize lesions on a chest radiograph when those lesions are low in contrast and/or overlap with ribs and large pulmonary vessels. In order to implement CAD methodology, the appropriate computer system was developed The task for such system was to analyze images of radiography in DICOM format and recognize lungs, as well as its texture patterns, calculating appropriate characteristics. Cryptogenic organizing pneumonia (COP): A pneumonia-like interstitial lung disease, but without an infection present. The applications are ranging from segmentation of specific anatomical structures and detection of lesions, to differentiation between pathological and healthy tissue in lung and other organs
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