A probabilistic model of emphysema based on granulometry analysis
Por:
Marcos J.V., Nava R., Ristobal G.C., Munoz-Barrutia A., Escalante-Ramirez B., Ortiz De Solorzano C.
Publicada:
1 ene 2013
Resumen:
Emphysema is associated with the destruction of lung parenchyma, resulting in abnormal enlargement of airspaces. Accurate quantification of emphysema is required for a better understanding of the disease as well as for the assessment of drugs and treatments. In the present study, a novel method for emphysema characterization from histological lung images is proposed. Elastase-induced mice were used to simulate the effect of emphysema on the lungs. A database composed of 50 normal and 50 emphysematous lung patches of size 512 x 512 pixels was used in our experiments. The purpose is to automatically identify those patches containing emphysematous tissue. The proposed approach is based on the use of granulometry analysis, which provides the pattern spectrum describing the distribution of airspaces in the lung region under evaluation. The profile of the spectrum was summarized by a set of statistical features. A logistic regression model was then used to estimate the probability for a patch to be emphysematous from this feature set. An accuracy of 87% was achieved by our method in the classification between normal and emphysematous samples. This result shows the utility of our granulometry-based method to quantify the lesions due to emphysema.© 2013 SPIE.
Filiaciones:
Marcos J.V.:
Instituto de Optica, Spanish National Research Council (CSIC), Serrano 121, Madrid 28006, Spain
Nava R.:
Posgrado en Ciencia e Ingenieria de la Computaciion, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
Ristobal G.C.:
Instituto de Optica, Spanish National Research Council (CSIC), Serrano 121, Madrid 28006, Spain
Munoz-Barrutia A.:
Cancer Imaging Laboratory, Center for Applied Medical Research, University of Navarra, Pamplona, Spain
Escalante-Ramirez B.:
Departamento de Procesamiento de Senales, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
Ortiz De Solorzano C.:
Cancer Imaging Laboratory, Center for Applied Medical Research, University of Navarra, Pamplona, Spain
|