Blockwise Classification of Lung Patterns in Unsegmented CT Images

Abstract

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.