Open AccessHomographic Patch Feature Transform: A Robustness Registration for Gastroscopic Surgery
Author(s) -
Weiling Hu,
Xu Zhang,
Bin Wang,
Jiquan Liu,
Huilong Duan,
Ning Dai,
Jianmin Si
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0153202
Subject(s) - robustness (evolution) , artificial intelligence , image registration , computer science , computer vision , feature tracking , feature (linguistics) , pattern recognition (psychology) , feature extraction , matching (statistics) , image (mathematics) , medicine , pathology , biology , biochemistry , linguistics , philosophy , gene
Image registration is a key component of computer assistance in image guided surgery, and it is a challenging topic in endoscopic environments. In this study, we present a method for image registration named Homographic Patch Feature Transform (HPFT) to match gastroscopic images. HPFT can be used for tracking lesions and augmenting reality applications during gastroscopy. Furthermore, an overall evaluation scheme is proposed to validate the precision, robustness and uniformity of the registration results, which provides a standard for rejection of false matching pairs from corresponding results. Finally, HPFT is applied for processing in vivo gastroscopic data. The experimental results show that HPFT has stable performance in gastroscopic applications.