DaBarreiro

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Diploma thesis

Real-time Image Segmentation for Occupancy Volume Reconstruction
Thesis by: Nuno Barreiro
Advisor: Nassir Navab
Supervision by: Selim Benhimane
Due date: 15.10.2007

Abstract

This work is part of the Collision Avoidance project, a project with Siemens Medical Solutions - Germany.

When having a fully automated medical device in an operating room, the control of the device takes the patient's position into account and not the rest of the environment, i.e. surgical staff and additional equipment. This project aims to detect and prevent collisions between the medical device and its dynamic environment. The collision avoidance is done thanks to an equipment of 16 cameras mounted on the ceiling of the interventional room. The combination of different views of the scene makes it possible to compute the probability of occupancy in each unit of space of the room in the neighborhood of the device.

In order to get a static (low-resolution) occupancy volume, the images acquired are processed to obtain silhouette images. Then, each voxel is projected onto the 16 images and its occupancy is tested. To do that, we need a robust segmentation. The more precise is the segmentation, the faster is the algorithm.

The student works on the segmentation part. The internship is divided into two main objectives: - First, he will implement a standard segmentation algorithm, where each camera is considered independently. The segmentation should be fast and robust to illumination changes and background clutter. - Second, he will design a more enhanced way to segment the images: the segmentation should take into account the fact that the images are the projection of the same scene; therefore, it should be consistent with the intrinsic and the extrinsic parameters of the set of the 16 cameras.

Resources

The CAMP lab MI 03.13.007

Literature

  • Criminisi A., Blake A., Cross G., Rother C, Kolmogov V.
    Bilayer segmentation of live video
    In Proc. IEEE Computer Vision and Pattern Recognition, 2006.

  • Elgammal A., Harwood D., and Davis L.
    non-parametric model for background subtraction (code available online)
    in proc. IEEE ICCV, 1999.

  • Friedman N., Russel S.
    Image segmentation in video sequences: a probabilistic approach
    in Proc. Conf. Uncertainty in Artificial Intelligence, 1997.

  • Fukui S., Iwahori Y., Itoh H., Kawanaka H., Woodham R.
    Robust Background Subtraction for Quick Illumination Changes
    IEEE Pacific-Rim Symposium on Image and Video Technology, 2006.

  • Horprasert T., Harwood D., Davis L.
    A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection
    in Proceedings of IEEE ICCV’99 FRAME-RATE Workshop, 1999.

  • Yu. T., Zang C., Cohen M., Rui Y., Wu Y.
    Monocular video Foreground/Background Segmentation by tracking Spatial-Color Gaussian Mixture Models
    IEEE Workshop on Motion and Video Computing, 2007.


  • Students.ProjectForm
    Title: Image Segmentation for Occupancy Map Reconstruction
    Abstract: This work is part of the Collision Avoidance project, a project with Siemens Medical Solutions - Germany. When having a fully automated medical device in an operating room, the control of the device takes the patient's position into account and not the rest of the environment, i.e. surgical staff and additional equipment. This project aims to detect and prevent collisions between the medical device and its dynamic environment. The collision avoidance is done thanks to an equipment of 16 cameras mounted on the ceiling of the interventional room. The combination of different views of the scene makes it possible to compute the probability of occupancy in each unit of space of the room in the neighborhood of the device. In order to get a static (low-resolution) occupancy map, the images acquired are processed to obtain silhouette images. Then, each voxel is projected onto the 16 images and its occupancy is tested. To do that, we need a robust segmentation. The more precise is the segmentation, the faster is the algorithm. The students works on the segmentation part. The internship is divided into two main objectives: - First, he will implement a standard segmentation algorithm, where each camera is considered independently. The segmentation should be fast and robust to illumination changes and background clutter. - Second, he will design a more enhanced way to segment the images: the segmentation should take into account the fact that the images are the projection of the same scene; therefore, it should be consistent with the intrinsic and the extrinsic parameters of the set of the 16 cameras.
    Student: Nuno Barreiro?
    Director: Nassir Navab
    Supervisor: Selim Benhimane Alexander Ladikos
    Type: Master Thesis
    Area:  
    Status: finished
    Start: 2007/04/16
    Finish: 2007/10/07
    Thesis (optional):  
    Picture:  


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