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

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