  Accurate delineation of pathological lungs from computed tomography (CT)
images remains mostly unsolved because available methods fail to provide a
reliable generic solution due to high variability of abnormality appearance.
Local descriptor-based classification methods have shown to work well in
annotating pathologies; however, these methods are usually computationally
intensive which restricts their widespread use in real-time or near-real-time
clinical applications. In this paper, we present a novel approach for fast,
accurate, reliable segmentation of pathological lungs from CT scans by
combining region-based segmentation method with local descriptor classification
that is performed on an optimized sampling grid. Our method works in two
stages; during stage one, we adapted the fuzzy connectedness (FC) image
segmentation algorithm to perform initial lung parenchyma extraction. In the
second stage, texture-based local descriptors are utilized to segment abnormal
imaging patterns using a near optimal keypoint analysis by employing centroid
of supervoxel as grid points. The quantitative results show that our
pathological lung segmentation method is fast, robust, and improves on current
standards and has potential to enhance the performance of routine clinical
tasks.
