NOTE the special time

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                   B O S T O N   U N I V E R S I T Y
                      Computer Science Department
                          C O L L O Q U I U M
                     Wednesday, September 18, 11:00 AM
                       (Coffee served at 10:45 AM)
                         Seminar Room / MCS 135

   Landmark Detection in the Chest and Registration of Lung Surfaces with
                  an Application to Nodule Registration

                            Master's Defense
                     Harrison Hong, Boston University


Abstract:

Lung cancer remains the leading cause of cancer death in the US.  The
overall 5-year survival rate is only 15%, but early detection and
resection of pulmonary nodules in Stage I can improve the prognosis to
67%.  The curability of early stage lung cancer has motivated
researchers to propose lung cancer screening.  Repeated computed
tomography (CT) scans are needed in order for the radiologist to
determine whether a small nodule is growing.  Since these scans
contain hundreds of images, it is time consuming for the radiologist
to manually follow up nodules.

In my thesis, I propose an automated system for registering CT images
temporally.  The system detects anatomical landmarks, in particular,
the trachea, sternum, and spine, using an attenuation-based template
matching approach.  It computes the optimal rigid-body transformation
that aligns the corresponding landmarks in two CT scans of the same
patient.  This transformation then provides an initial registration of
the lung surfaces segmented from the two scans.

The initial surface alignment is refined step by step in an iterative
closest-point (ICP) process.  To establish the correspondence of lung
surface points, Elias' nearest neighbor algorithm was adopted.  This
method improves the processing time of the original ICP algorithm from
O(kn log n) to O(kn), where k is the number of iterations and n the
number of surface points.  The surface transformation is applied to
align nodules in the initial CT scan with nodules in the follow-up
scan.

For 56 out of 58 nodules in the initial CT scans of 10 patients,
nodule correspondences in the follow-up scans are established
correctly.  Our methods, therefore, can potentially facilitate the
radiologist's evaluation of pulmonary nodules on chest CT for interval
growth.


Thesis Committee: 
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Major Advisor: Margrit Betke, Computer Science Dept., B.U.
Second Reader: Jane P. Ko, Radiology Dept., N.Y.U. Medical School
Third Reader:  Shang-Hua Teng, Computer Science Dept., B.U.
Fourth Reader: Stan Sclaroff, Computer Science Dept., B.U.