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13 March 2013Parsing radiographs by integrating landmark set detection and multi-object active appearance models
This work addresses the challenging problem of parsing 2D radiographs into salient anatomical regions such as
the left and right lungs and the heart. We propose the integration of an automatic detection of a constellation
of landmarks via rejection cascade classifiers and a learned geometric constellation subset detector model with
a multi-object active appearance model (MO-AAM) initialized by the detected landmark constellation subset.
Our main contribution is twofold. First, we propose a recovery method for false positive and negative landmarks
which allows to handle extreme ranges of anatomical and pathological variability. Specifically we (1) recover
false negative (missing) landmarks through the consensus of inferences from subsets of the detected landmarks,
and (2) choose one from multiple false positives for the same landmark by learning Gaussian distributions for the
relative location of each landmark. Second, we train a MO-AAM using the true landmarks for the detectors and
during test, initialize the model using the detected landmarks. Our model fitting allows simultaneous localization
of multiple regions by encoding the shape and appearance information of multiple objects in a single model. The
integration of landmark detection method and MO-AAM reduces mean distance error of the detected landmarks
from 20.0mm to 12.6mm. We assess our method using a database of scout CT scans from 80 subjects with widely
varying pathology.
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Albert Montillo, Qi Song, Xiaoming Liu, James V. Miller, "Parsing radiographs by integrating landmark set detection and multi-object active appearance models," Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690H (13 March 2013); https://doi.org/10.1117/12.2007138