Validation of bone segmentation and improved 3-D registration using contour coherency in CT data

Abstract

A method is presented to validate the segmentation of computed tomography (CT) image sequences, and improve the accuracy and efficiency of the subsequent registration of the three-dimensional surfaces that are reconstructed from the segmented slices. The method compares the shapes of contours extracted from neighborhoods of slices in CT stacks of tibias. The bone is first segmented by an automatic segmentation technique, and the bone contour for each slice is parameterized as a one-dimensional function of normalized arc length versus inscribed angle. These functions are represented as vectors within a K-dimensional space comprising the first K amplitude coefficients of their Fourier Descriptors. The similarity or coherency of neighboring contours is measured by comparing statistical properties of their vector representations within this space. Experimentation has demonstrated this technique to be very effective at identifying low-coherency segmentations. Compared with experienced human operators, in a set of 23 CT stacks (1,633 slices), the method correctly detected 87.5% and 80% of the low-coherency and 97.7% and 95.5% of the high coherency segmentations, respectively from two different automatic segmentation techniques. Removal of the automatically detected low-coherency segmentations also significantly improved the accuracy and time efficiency of the registration of 3-D bone surface models. The registration error was reduced by over 500% (i.e., a factor of 5) and 280%, and the computational performance was improved by 540% and 791% for the two respective segmentation methods.

Accuracy assessment of CT-based outer surface femur meshes

“…This is fully understandable in the case of in-vivo imaging of brain or other soft tissues, where opening up the cadaver for dissection and measurement causes a collapse of the soft tissue target structure. A possible solution is to perform relative comparisons, e.g., by comparing with another, clinically established, imaging technique [15]; by performing leave-one-out experiments when working with a statistical database approach and training set for testing the accuracy of deformable models data [20][21][22]; or by checking coherency within the same data set [23].…”

A Study on the Feasibility of Active Contours on Automatic CT Bone Segmentation

“…8,9 Most of the methods have shown success in certain anatomical structures where they have been optimized, such as carpal bones, 9 acetabulum and femoral head, 10 spinal canal, 11 pelvis, 7,12 vertebrae, 13 ribs, 14 and phalanx bones. 15 In, 8 two methods were validated on knee bone segmentation, which is also the subject of this study. The first one was a four-step process 16 that contains region-growing using local adaptive thresholds, discontinued-boundary closing, anatomically oriented boundary adjustment, and manual correction.…”

CT Image Segmentation Using Structural Analysis

“…By way of example, Simina et al [17] proposed a method based on seeded region growing [18] for CT images of human pelvic bones, and Campadelli et al [19] proposed FMM (the Fast Marching Method ) [20]. Other region segmentation methods were described in [21] to avoid erroneous slices in mesh model creation. Totally, disadvantages of conventional method are lack of versatility, in fact we have to largely modify their function or have to search optimal parameters.…”