title: Topology-aware illumination design for volume rendering.
abstract: BACKGROUND
  Direct volume rendering is one of flexible and effective approaches to inspect large volumetric data such as medical and biological images. In conventional volume rendering, it is often time consuming to set up a meaningful illumination environment. Moreover, conventional illumination approaches usually assign same values of variables of an illumination model to different structures manually and thus neglect the important illumination variations due to structure differences.
  
  
  RESULTS
  We introduce a novel illumination design paradigm for volume rendering on the basis of topology to automate illumination parameter definitions meaningfully. The topological features are extracted from the contour tree of an input volumetric data. The automation of illumination design is achieved based on four aspects of attenuation, distance, saliency, and contrast perception. To better distinguish structures and maximize illuminance perception differences of structures, a two-phase topology-aware illuminance perception contrast model is proposed based on the psychological concept of Just-Noticeable-Difference.
  
  
  CONCLUSIONS
  The proposed approach allows meaningful and efficient automatic generations of illumination in volume rendering. Our results showed that our approach is more effective in depth and shape depiction, as well as providing higher perceptual differences between structures.
authors.affiliation: Xi'an Jiaotong University City College, 8715 Shangji Road, Xi'an, Shaanxi 710018, People's Republic of China.|DATA61, CSIRO, 13 Garden Street, Eveleigh, 2015, NSW, Australia.
authors.forename: Jianlong
authors.initials: J
authors.lastname: Zhou
authors.affiliation: The University of Sydney, 1 Cleveland Street, Darlington, 2008, NSW, Australia.
authors.forename: Xiuying
authors.initials: X
authors.lastname: Wang
authors.affiliation: The University of Sydney, 1 Cleveland Street, Darlington, 2008, NSW, Australia.
authors.forename: Hui
authors.initials: H
authors.lastname: Cui
authors.affiliation: The University of Sydney, 1 Cleveland Street, Darlington, 2008, NSW, Australia.
authors.forename: Peng
authors.initials: P
authors.lastname: Gong
authors.affiliation: Xi'an Jiaotong University City College, 8715 Shangji Road, Xi'an, Shaanxi 710018, People's Republic of China. mxl@mail.xjtu.edu.cn.
authors.forename: Xianglin
authors.initials: X
authors.lastname: Miao
authors.affiliation: Xi'an University of Technology, 5 Jinhua Nan Road, Xi'an, 710048, Shaanxi, People's Republic of China.
authors.forename: Yalin
authors.initials: Y
authors.lastname: Miao
authors.affiliation: Xiangtan University, Xiangtan, 411105, Hunan, People's Republic of China.
authors.forename: Chun
authors.initials: C
authors.lastname: Xiao
authors.affiliation: DATA61, CSIRO, 13 Garden Street, Eveleigh, 2015, NSW, Australia.
authors.forename: Fang
authors.initials: F
authors.lastname: Chen
authors.affiliation: The University of Sydney, 1 Cleveland Street, Darlington, 2008, NSW, Australia.
authors.forename: Dagan
authors.initials: D
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D059629:Signal-To-Noise Ratio
keywords: Automatic illumination design; Distance; Perception; Saliency; Topology; Volume rendering