Detection and visualization of surface-pockets to enable phenotyping studies.
In this paper, we propose a technique for detecting pockets on a surface-of-interest. A sequence of propagating fronts converging to the target surface is used as the basis for inspection. We compute a correspondence function between the initial and the target surface. This leads to a natural definition of the local feature size measured as the evolution distance between mapped points. Surface pockets are then extracted as salient clusters embedded in the feature space. The level-set initialization also determines the scale-space of the extracted pockets. Results are presented on a case-study in which the focus is to chronicle the phenotyping differences in genetically modified mouse placenta. Our results are validated based on manually verified ground-truth.
Mosaliganti
Kishore
K
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. mosaligk@cse.ohio-state.edu
Janoos
Firdaus
F
Sharp
Richard
R
Ridgway
Randall
R
Machiraju
Raghu
R
Huang
Kun
K
Wenzel
Pamela
P
deBruin
Alain
A
Leone
Gustavo
G
Saltz
Joel
J
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D005260:Female; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D051379:Mice; D010363:Pattern Recognition, Automated / Q000379:methods*; D010641:Phenotype; D010920:Placenta / Q000378:metabolism* / Q000473:pathology*; D010922:Placenta Diseases / Q000235:genetics* / Q000473:pathology*; D011247:Pregnancy; D011270:Pregnancy, Animal; D015203:Reproducibility of Results; D016160:Retinoblastoma Protein / Q000235:genetics*; D012680:Sensitivity and Specificity; D013499:Surface Properties; D014584:User-Computer Interface
D016160:Retinoblastoma Protein