Orientation estimation of anatomical structures in medical images for object recognition

Ulaş Bağci; Jayaram K. Udupa; Xinjian Chen

doi:10.1117/12.878184

14 March 2011 Orientation estimation of anatomical structures in medical images for object recognition

Ulaş Bağci, Jayaram K. Udupa, Xinjian Chen

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79622L (2011) https://doi.org/10.1117/12.878184
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Recognition of anatomical structures is an important step in model based medical image segmentation. It provides pose estimation of objects and information about "where" roughly the objects are in the image and distinguishing them from other object-like entities. In,¹ we presented a general method of model-based multi-object recognition to assist in segmentation (delineation) tasks. It exploits the pose relationship that can be encoded, via the concept of ball scale (b-scale), between the binary training objects and their associated grey images. The goal was to place the model, in a single shot, close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. Unlike position and scale parameters, we observe that orientation parameters require more attention when estimating the pose of the model as even small differences in orientation parameters can lead to inappropriate recognition. Motivated from the non-Euclidean nature of the pose information, we propose in this paper the use of non-Euclidean metrics to estimate orientation of the anatomical structures for more accurate recognition and segmentation. We statistically analyze and evaluate the following metrics for orientation estimation: Euclidean, Log-Euclidean, Root-Euclidean, Procrustes Size-and-Shape, and mean Hermitian metrics. The results show that mean Hermitian and Cholesky decomposition metrics provide more accurate orientation estimates than other Euclidean and non-Euclidean metrics.

Citation Download Citation

Ulaş Bağci, Jayaram K. Udupa, and Xinjian Chen "Orientation estimation of anatomical structures in medical images for object recognition", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622L (14 March 2011); https://doi.org/10.1117/12.878184

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