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17 March 2008 A new method of automatic landmark tagging for shape model construction via local curvature scale
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Abstract
Segmentation of organs in medical images is a difficult task requiring very often the use of model-based approaches. To build the model, we need an annotated training set of shape examples with correspondences indicated among shapes. Manual positioning of landmarks is a tedious, time-consuming, and error prone task, and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method based on the notion of c-scale, a new local scale concept. For each boundary element b, the arc length of the largest homogeneous curvature region connected to b is estimated as well as the orientation of the tangent at b. With this shape description method, we can automatically locate mathematical landmarks selected at different levels of detail. The method avoids the use of landmarks for the generation of the mean shape. The selection of landmarks on the mean shape is done automatically using the c-scale method. Then, these landmarks are propagated to each shape in the training set, defining this way the correspondences among the shapes. Altogether 12 strategies are described along these lines. The methods are evaluated on 40 MRI foot data sets, the object of interest being the talus bone. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced annotations. The approach is applicable to spaces of any dimensionality, although we have focused in this paper on 2D shapes.
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Sylvia Rueda, Jayaram K. Udupa, and Li Bai "A new method of automatic landmark tagging for shape model construction via local curvature scale", Proc. SPIE 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, 69180N (17 March 2008); https://doi.org/10.1117/12.770570
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