Multi-class classification of pulmonary endomicroscopic images

Optical endomicroscopy (OEM) is an emerging medical imaging tool capable of providing in-vivo, in-situ optical biopsies. Clinical pulmonary OEM procedures generate data containing a multitude of frames, making their manual analysis a highly subjective and laborious task. It is therefore essential to automatically classify the images into clinically relevant classes to aid reaching a fast and reliable diagnosis. This paper proposes a methodology to automatically classify OEM images of the distal lung. Due to their diagnostic relevance, three classification tasks are targeted: (i) differentiating between alveolar images containing predominantly elastin from those flooded with cells, (ii) separating normal from abnormal elastin frames, and (iii) multi-class classification amongst normal, abnormal, and cell frames. Local Binary Patterns along with a Support Vector Machine classifier, and One-Versus-All Error Correcting Output Codes strategy for the multi-class classification case, are employed obtaining accuracy of 92.2%, 95.2%, 90.1% for the tasks (i), (ii), (iii), respectively.