Volumetric difference evaluation of registered three-dimensional pre-operative and post-operative CT dental data.

OBJECTIVE: The purpose of this study is to propose a complete methodology for automatically registering three-dimensional (3D) pre-operative and post-operative CT scan dental volumes as well as to provide a toolset for quantifying and evaluating their volumetric differences. METHODS: The proposed methodology was applied to cone beam CT (CBCT) data from 20 patients in order to assess the volume of augmented bone in the alveolar region. In each case, the pre-operative and post-operative data were registered using a 3D affine-based scheme. The performance of the 3D registration algorithm was evaluated by measuring the average distance between the edges of the registered sets. The differences between the registered sets were assessed through 3D subtraction radiography. The volume of the differences was finally evaluated by defining regions of interest in each slice of the subtracted 3D data and by combining all respective slices to model the desired volume of interest. The effectiveness of the algorithm was verified by applying it to several reference standard-shaped objects with known volumes. RESULTS: Satisfactory alignment was achieved as a low average offset of 1.483 ± 1.558 mm was recorded between the edges of the registered sets. Moreover, the estimated volumes closely matched the volumes of the reference objects used for verification, as the recorded volume differences were less than 0.4 mm(3) in all cases. CONCLUSION: The proposed method allows for automatic registration of 3D CBCT data sets and the volumetric assessment of their differences in particular areas of interest. The proposed approach provides accurate volumetric measurements in three dimensions, requiring minimal user interaction.

A contrast correction method for dental images based on histogram registration.

Contrast correction is often required in digital subtraction radiography when comparing medical data acquired over different time periods owing to dissimilarities in the acquisition process. This paper focuses on dental radiographs and introduces a novel approach for correcting the contrast in dental image pairs. The proposed method modifies the subject images by applying typical registration techniques on their histograms. The proposed histogram registration method reshapes the histograms of the two subject images in such a way that these images are matched in terms of their contrast deviation. The method was extensively tested over 4 sets of dental images, consisting of 72 registered dental image pairs with unknown contrast differences as well as 20 dental pairs with known contrast differences. The proposed method was directly compared against the well-known histogram-based contrast correction method. The two methods were qualitatively and quantitatively evaluated for all 92 available dental image pairs. The two methods were compared in terms of the contrast root mean square difference between the reference image and the corrected image in each case. The obtained results were also verified statistically using appropriate t-tests in each set. The proposed method exhibited superior performance compared with the well-established method, in terms of the contrast root mean square difference between the reference and the corrected images. After suitable statistical analysis, it was deduced that the performance advantage of the proposed approach was statistically significant.

Automatic correspondence on medical images: a comparative study of four methods for allocating corresponding points.

The accurate estimation of point correspondences is often required in a wide variety of medical image-processing applications. Numerous point correspondence methods have been proposed in this field, each exhibiting its own characteristics, strengths, and weaknesses. This paper presents a comprehensive comparison of four automatic methods for allocating corresponding points, namely the template-matching technique, the iterative closest points approach, the correspondence by sensitivity to movement scheme, and the self-organizing maps algorithm. Initially, the four correspondence methods are described focusing on their distinct characteristics and their parameter selection for common comparisons. The performance of the four methods is then qualitatively and quantitatively compared over a total of 132 two-dimensional image pairs divided into eight sets. The sets comprise of pairs of images obtained using controlled geometry protocols (affine and sinusoidal transforms) and pairs of images subject to unknown transformations. The four methods are statistically evaluated pairwise on all image pairs and individually in terms of specific features of merit based on the correspondence accuracy as well as the registration accuracy. After assessing these evaluation criteria for each method, it was deduced that the self-organizing maps approach outperformed in most cases the other three methods in comparison.