TY - GEN
T1 - Automatic real-time tracking of fetal mouth in fetoscopic video sequence for supporting fetal surgeries
AU - Xu, Rong
AU - Xie, Tianliang
AU - Ohya, Jun
AU - Zhang, Bo
AU - Sato, Yoshinobu
AU - Fujie, Masakatsu G.
PY - 2013
Y1 - 2013
N2 - Recently, a minimally invasive surgery (MIS) called fetoscopic tracheal occlusion (FETO) was developed to treat severe congenital diaphragmatic hernia (CDH) via fetoscopy, by which a detachable balloon is placed into the fetal trachea for preventing pulmonary hypoplasia through increasing the pressure of the chest cavity. This surgery is so dangerous that a supporting system for navigating surgeries is deemed necessary. In this paper, to guide a surgical tool to be inserted into the fetal trachea, an automatic approach is proposed to detect and track the fetal face and mouth via fetoscopic video sequencing. More specifically, the AdaBoost algorithm is utilized as a classifier to detect the fetal face based on Haar-like features, which calculate the difference between the sums of the pixel intensities in each adjacent region at a specific location in a detection window. Then, the CamShift algorithm based on an iterative search in a color histogram is applied to track the fetal face, and the fetal mouth is fitted by an ellipse detected via an improved iterative randomized Hough transform approach. The experimental results demonstrate that the proposed automatic approach can accurately detect and track the fetal face and mouth in real-time in a fetoscopic video sequence, as well as provide an effective and timely feedback to the robot control system of the surgical tool for FETO surgeries.
AB - Recently, a minimally invasive surgery (MIS) called fetoscopic tracheal occlusion (FETO) was developed to treat severe congenital diaphragmatic hernia (CDH) via fetoscopy, by which a detachable balloon is placed into the fetal trachea for preventing pulmonary hypoplasia through increasing the pressure of the chest cavity. This surgery is so dangerous that a supporting system for navigating surgeries is deemed necessary. In this paper, to guide a surgical tool to be inserted into the fetal trachea, an automatic approach is proposed to detect and track the fetal face and mouth via fetoscopic video sequencing. More specifically, the AdaBoost algorithm is utilized as a classifier to detect the fetal face based on Haar-like features, which calculate the difference between the sums of the pixel intensities in each adjacent region at a specific location in a detection window. Then, the CamShift algorithm based on an iterative search in a color histogram is applied to track the fetal face, and the fetal mouth is fitted by an ellipse detected via an improved iterative randomized Hough transform approach. The experimental results demonstrate that the proposed automatic approach can accurately detect and track the fetal face and mouth in real-time in a fetoscopic video sequence, as well as provide an effective and timely feedback to the robot control system of the surgical tool for FETO surgeries.
KW - AdaBoost classifier
KW - CamShift algorithm
KW - Fetal face tracking
KW - Fetal mouth detection
KW - Fetoscopic video sequence
KW - Haar-like features
KW - Iterative randomized hough transform (IRHT)
UR - http://www.scopus.com/inward/record.url?scp=84878540533&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878540533&partnerID=8YFLogxK
U2 - 10.1117/12.2002803
DO - 10.1117/12.2002803
M3 - Conference contribution
AN - SCOPUS:84878540533
SN - 9780819494450
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Medical Imaging 2013
T2 - Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
Y2 - 12 February 2013 through 14 February 2013
ER -