Blockwise Classification of Lung Patterns in Unsegmented CT Images
Luiza Dri Bagesteiro, Lucas Ferrari de Oliveira, Daniel Weingaertner · 2015
Diagnosis of lung diseases is usually accomplished by detecting abnormal characteristics in Computed Tomography (CT) scans. We report an initial study for classifying texture patterns in High-Resolution lung CTs using the Completed Local Binary Pattern (CLBP) descriptor with a Support Vector Machine (SVM). The main contribution of the proposed method is that it does not depend on a previously segmented lung, as it performs a coarse segmentation by classifying body areas outside the lungs. The classified patterns are: non lung, normal lung tissue, emphysema, ground-glass opacity, fibrosis and micronodules. Using image blocks of 32x32 pixels, extracted from a public dataset with 113 patients, correct block wise classification of non lung patterns was achieved with an accuracy of 98.91%. Regarding normal and pathological lung patterns, a mean accuracy of 91.81% was obtained. This is similar to the reported results in literature which used a presegmented lung.