The algorithm of stereo vision and shape from shading based on endoscope imaging. (July 2022)

• Record Type: Journal Article
• Authors: Cao, Ziyi; Wang, Yaxiang; Zheng, Wenfeng; Yin, Lirong; Tang, Yushan; Miao, Wang; Liu, Shan; Yang, Bo
• Is Part Of: Biomedical signal processing and control. Volume 76(2022)
• Publication Date: 2022-07
• Subjects: Minimally invasive surgery -- Three-dimensional reconstruction -- Binocular stereo vision -- SFS method -- Endoscopic imaging; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103658
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Minimally invasive surgery has become an essential technic in medical practice and has higher requirements for the operator. This paper constructs a joint objective function based on the improved SFS and the classical stereo matching method. The framework's effectiveness is verified by analysis based on the silica-gel-heart and real-heart image datasets. The results show that the new framework can restore the whole heart surface shape and retain the local details. The proposed joint reconstruction method has better robustness and reconstruction accuracy. Abstract: With medical endoscopic equipment development, minimally invasive surgery (MIS) has gradually become an essential technical means in daily medical practice. In recent years, minimally invasive surgery has been widely used because of its small incision and quick recovery. However, at the same time, minimally invasive surgery has put forward higher requirements for the operator. A 3D reconstruction framework combined with stereo vision and Shape from Shading (SFS) was proposed to improve endoscopic imaging accuracy and reduce the difficulty of minimally invasive surgery. This paper constructs a joint objective function based on the improved SFS and the classical stereo matching method. The optimization algorithm of the depth map under the joint objective function is given. Finally, the experimental verification of the joint reconstruction algorithm is carried out. The joint reconstruction framework's effectiveness is verified by qualitative and quantitative comparison and analysis based on the silica-gel-heart model and real-heart image datasets. The experimental results show that the joint reconstruction framework can restore the heart surface shape as a whole and retain the local details. Compared with the classical stereo vision and SFS methods, the proposed joint reconstruction method has better robustness and reconstruction accuracy.