title: A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest.
abstract: BACKGROUND
  Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules.
  
  
  METHODS
  Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences.
  
  
  RESULTS
  Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences.
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Liao
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
authors.forename: Juanjuan
authors.initials: J
authors.lastname: Zhao
authors.affiliation: PET/CT center of Shanxi coal Central Hospital, Taiyuan, Shanxi, 030024, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Jiao
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Lei
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Qiang
authors.affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Cui
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data