KEYWORDS: Breast, 3D modeling, Mammography, Finite element methods, Data modeling, Magnetic resonance imaging, Chemical elements, Computer simulations, 3D acquisition, Tissues
Performing regular mammographic screening and comparing corresponding mammograms taken from multiple
views or at different times are necessary for early detection and treatment evaluation of breast cancer, which is
key to successful treatment. However, mammograms taken at different times are often obtained under different
compression, orientation, or body position. A temporal pair of mammograms may vary significantly due to the
spatial disparities caused by the variety in acquisition environments, including 3D position of the breast, the
amount of pressure applied, etc. Such disparities can be corrected through the process of temporal registration.
We propose to use a 3D finite element model for temporal registration of digital mammography. In this paper,
we apply patient specific 3D breast model constructed from MRI data of the patient, for cases where lesions are
detectable in multiple mammographic views across time. The 3D location of the lesion in the breast model is
computed through a breast deformation simulation step presented in our earlier work. Lesion correspondence
is established by using a nearest neighbor approach in the uncompressed breast volume. Our experiments show
that the use of a 3D finite element model for simulating and analyzing breast deformation contributes to good
accuracy when matching suspicious regions in temporal mammograms.
Establishing benchmark datasets, performance metrics and baseline algorithms have considerable
research significance in gauging the progress in any application domain. These primarily
allow both users and developers to compare the performance of various algorithms on a
common platform. In our earlier works, we focused on developing
performance metrics and establishing a substantial dataset with ground truth for
object detection and tracking tasks (text and face) in
two video domains -- broadcast news and meetings. In this paper,
we present the results of a face detection and tracking algorithm on broadcast news videos with
the objective of establishing a baseline performance for this task-domain pair.
The detection algorithm uses a statistical approach that was originally developed by
Viola and Jones and later extended by Lienhart.
The algorithm uses a feature set that is Haar-like and a cascade of
boosted decision tree classifiers as a statistical model.
In this work, we used the Intel Open Source Computer Vision Library (OpenCV) implementation
of the Haar face detection algorithm. The optimal values for
the tunable parameters of this implementation were found through
an experimental design strategy commonly used in statistical analyses of
industrial processes. Tracking was accomplished as continuous
detection with the detected objects in two frames mapped using a greedy
algorithm based on the distances between the centroids of bounding boxes.
Results on the evaluation set containing 50 sequences (≈ 2.5 mins.) using
the developed performance metrics show good performance of the algorithm reflecting the
state-of-the-art which makes it an appropriate choice as the baseline algorithm for the problem.
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