Automatic lung segmentation based on image decomposition and wavelet transform

Accurately segmenting lungs from medical images is still a challenge due to some negative factors involved in the work, such as inhomogeneous intensities, juxta-pleural nodules, image noises and so on. To deal with the problem, in this paper, we present a novel algorithm to segment lungs from CT images in an accurate and automatical fashion. In our algorithm, an image decomposition based filtering strategy is first introduced to denoise lung CT images while preserving their lung contours. Lungs are then segmented from the CT images by wavelet transformation combining with a group of morphological operations. The segmentations are further refined by a contour correction approach, which is built on a fast corner detection technique, to correct and smooth the extracted lung contours. Experimental results show that our algorithm has better performance than a set of classical approaches, and it achieved an averaged Dice similarity coefficient of 98.04% and Jaccard's similarity index of 94.91% on lung CT image segmentation compared with ground truths. Our algorithm can correctly segment lung tissues from lung CT images and is helpful for radiologists' diagnosis of lung diseases.