Wavelet/PSO-Based Segmentation and Marker-Less Tracking of the Gallbladder in Monocular Calibration-free Laparoscopic Cholecystectomy

• Published in: International journal of advanced computer science & applications Vol. 9; no. 7; pp. 1 - 10
• Main Authors: Djaghloul, Haroun, Batouche, Mohamed, Jessel, Jean-Pierre, Benhocine, Abdelhamid
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2018; The Science and Information Organization
• Subjects: Augmented reality; Bioinformatics; Calibration; Cholecystectomy; Computer Science; Edge detection; Gallbladder; Image segmentation; Laparoscopy; Optimization; Population (statistical); Sensitivity enhancement; Surgical instruments; Three dimensional models; Tracking; Two dimensional analysis; Wavelet analysis; Deformable structures tracking; Wavelets; Gallbladder segmentation and tracking; Computer aided surgery (CAS); Medical image segmentation; Particles swarm optimisation; Markerless augmented reality; Monocular laparo-scopic cholecystectomy; Minimally invasive surgery (MIS)
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2018.090701

This paper presents an automatic segmentation and monocular marker-less tracking method of the gallbladder in minimally invasive laparoscopic cholecystectomy intervention that can be used for the construction of an adaptive calibration-free medical augmented reality system. In particular, the pro-posed method consists of three steps, namely, a segmentation of 2D laparoscopic images using a combination of photomet-ric population-based statistical approach and edge detection techniques, a PSO-based detection of the targeted anatomical structure (the gallbladder) and, finally, the 3D model wavelet-based multi-resolution analysis and adaptive 2D/3D registration. The proposed population-based statistical segmentation approach of 2D laparoscopic images differs from classical approaches (his-togram thresholding), in that we consider anatomical structures and surgical instruments in terms of distributions of RGB color triples. This allows an efficient handling, superior robustness and to readily integrate current intervention information. The result of this step consists in a set of point clouds with a loosely gradient information that can cover various anatomical structures. In order to enhance both sensitivity and specificity, the detection of the targeted structure (the gallbladder) is based on a modified PSO (particles swarm optimization) scheme which maximizes both internal features density and the divergence with neighboring structures such as, the liver. Finally, a multi-particles based representation of the targeted structure is constructed, thanks to a proposed wavelet-based multi-resolution analysis of the 3D model of the targeted structure which is registered adaptively with the 2D particles generated during the previous step. Results are shown on both synthetic and real data.