Coarse registration is the initial step of aligning a point clouds with other clouds, aiming to put two point clouds in the correct position. There are many coarse registration methods, and among them, the SAC-IA (Sample Consensus Initial Alignment) is widely used. It selects corresponding point pairs by matching the geometric features of point clouds. It has high registration accuracy and fast registration speed. However, when the geometric features of the point clouds are relatively simple or not distinctive, its registration performance may not be very effective. With the development of color image sensors, acquiring color point clouds has become increasingly important. The RGB information of point clouds can effectively compensate for the shortcomings of the SAC-IA algorithm. The color characteristics of the interested points are extracted by fusing the color information of feature points and their neighboring points, including the first-order moment of point clouds color and the CFH of the point clouds, based on the traditional SAC-IA algorithm. Experiments have shown that the improved SAC-IA algorithm has better accuracy and robustness.
Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used — optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1–3 voxels is achieved.
In this paper, we proposed a novel method to extract shape feature based on dual-tree complex wavelet. First, with the two level dual-tree complex wavelet transformations, we can get two low frequency components of the first level, which are used as wavelet moment invariants formed from approximation coefficients. Then, we calculate means and variance for each of the six detailed components in the second level since it contains different directions information of the shape. Using the Principal Component Analysis (PCA), twenty features can be reduced to five maximum useful features which contribute to shape matching.
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