University of Houston

With the exception of skin cancer, breast cancer is the most common cancer diagnosis among women worldwide, and post-mastectomy breast reconstruction is the third most frequently performed reconstructive procedures. Also, the continuous creation and adoption of new surgical techniques and devices have made it possible for the surgeon to reconstruct the breast in many different ways thus making the decision-making job of the surgeon much more difficult. Current trial and error methods may lead to an unsatisfactory surgical outcome. Hence, there is an urgent need for a system that can pre-operatively simulate the surgical options, visualize potential outcomes of the surgery, and consequently help the surgeon and the patient in the process of surgical planning and decision making.

In this thesis, we present a set of computational tools for breast segmentation and breast shape modeling. More specifically: 1) we propose a new algorithm for automatic segmentation of the 3D data of a breast from a 3D scan of the patient's torso; and 2) we present a second generation deformable model for compact representation of the breast shape as estimated from 3D triangular mesh data of the patient's breast. These techniques can serve as the foundation of a number of applications in pre-operative planning for breast reconstruction and for breast plastic surgery in general.