Measuring the relative extent of pulmonary infiltrates by hierarchical classification of patient-specific image features

Abstract

Pulmonary infiltrates are common radiological findings indicating the filling of airspaces with fluid, inflammatory exudates, or cells. They are most common in cases of pneumonia, acute respiratory syndrome, atelectasis, pulmonary oedema and haemorrhage, whereas their extent is usually correlated with the extent or the severity of the underlying disease. In this paper we propose a novel pattern recognition framework for the measurement of the extent of pulmonary infiltrates in routine chest radiographs. The proposed framework follows a hierarchical approach to the assessment of image content. It includes the following: (a) sampling of the lung fields; (b) extraction of patient-specific grey-level histogram signatures from each sample; (c) classification of the extracted signatures into classes representing normal lung parenchyma and pulmonary infiltrates; (d) the samples for which the probability of belonging to one of the two classes does not reach an acceptable level are rejected and classified according to their textural content; (e) merging of the classification results of the two classification stages. The proposed framework has been evaluated on real radiographic images with pulmonary infiltrates caused by bacterial infections. The results show that accurate measurements of the infiltration areas can be obtained with respect to each lung field area. The average measurement error rate on the considered dataset reached 9.7% ± 1.0%.

Application of Phase Congruency for Discriminating Some Lung Diseases Using Chest Radiograph

“…Image normalization was done on the whole dataset to make image intensities comparable across the whole dataset [ 16 ]. LC or PNEU cases will have their chest X-ray film image exhibitting some abnormal opacity.…”

“…LC or PNEU cases will have their chest X-ray film image exhibitting some abnormal opacity. The landmarks on the spine were selected following [ 16 ] where these landmarks remain practically unaffected by the presence of infiltrates. Other than the spine, other lung anatomies especially the soft tissues are not suitable to become normalization landmark since it will be affected by the presence of infiltrates.…”

“…To eliminate problem the normalization of the chest radiograph was done on the dataset as proposed by [ 16 ].…”

“…The study of image-based assessment of pulmonary infiltrates has seen the application of a wide variety of techniques; for example, Oliveira et al [ 10 ] used wavelet, van Ginneken et al [ 12 ] and Arzhaeva et al [ 13 ] used texture, Katsuragawa and Doi [ 14 ] used contrast enhancement, Arzhaeva et al [ 15 ] used two-class classifier, and Tsevas and Iakovidis used Gabor filter features [ 16 ].…”

“…Multiresolution histogram does give texture information and in general they can discriminate between images with different spatial patterns alone without the help of other filters or features [ 30 , 31 ]. Another technique that is widely used in capturing local structural patterns from images is Gabor filter [ 16 ]. References [ 32 – 38 ] reported that Gabor filters yield excellent texture descriptors.…”