title: Improved segmentation of low-contrast lesions using sigmoid edge model.
abstract: OBJECTIVE
  The intensity profile of an image in the vicinity of a tissue's boundary is modeled by a step/ramp function. However, this assumption does not hold in cases of low-contrast images, heterogeneous tissue textures, and where partial volume effect exists. We propose a hybrid algorithm for segmentation of CT/MR tumors in low-contrast, noisy images having heterogeneous/homogeneous or hyper-/hypo-intense abnormalities. We also model a smoothed noisy intensity profile by a sigmoid function and employ it to find the true location of boundary more accurately.
  
  
  METHODS
  A novel combination of the SVM, watershed, and scattered data approximation algorithms is employed to initially segment a tumor. Small and large abnormalities are treated distinctly. Next, the proposed sigmoid edge model is fitted to the normal profile of the border. The estimated parameters of the model are then utilized to find true boundary of a tissue.
  
  
  RESULTS
  We extensively evaluated our method using synthetic images (contaminated with varying levels of noise) and clinical CT/MR data. Clinical images included 57 CT/MR volumes consisting of small/large tumors, very low-/high-contrast images, liver/brain tumors, and hyper-/hypo-intense abnormalities. We achieved a Dice measure of [Formula: see text] and average symmetric surface distance of [Formula: see text] mm. Regarding IBSR dataset, we fulfilled Jaccard index of [Formula: see text]. The average run-time of our code was [Formula: see text] s.
  
  
  CONCLUSIONS
  Individual treatment of small and large tumors and boundary correction using the proposed sigmoid edge model can be used to develop a robust tumor segmentation algorithm which deals with any types of tumors.
authors.affiliation: Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran. a.foruzan@shahed.ac.ir.
authors.forename: Amir Hossein
authors.initials: AH
authors.lastname: Foruzan
authors.affiliation: Intelligent Image Processing Lab, College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan.
authors.forename: Yen-Wei
authors.initials: YW
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D014057:Tomography, X-Ray Computed
keywords: Medical image processing; Scattered data approximation; Sigmoid edge model; Tumor segmentation; Watershed algorithm