ABSTRACT
In surgical knee replacement, the damaged knee joint is replaced with artificial prostheses. An accurate clinical evaluation must be carried out before applying knee prostheses to ensure optimal outcome from surgical operations and to reduce the probability of having long-term problems. Useful information can be inferred from estimates of the stress acting onto the bone-prosthesis system of the knee joint. This information can be exploited to tailor the prosthesis to the patient's anatomy. We present a compound system for pre-operative surgical planning based on structural simulation of the bone-prosthesis system, exploiting patient-specific data.
ABSTRACT
A new method is proposed to unambiguously define a geometric partitioning of 3D models of female thorax. A breast partitioning scheme is derived from simple geometric primitives and well-defined anatomical points. Relevant measurements can be extrapolated from breast partition. Our method has been tested on a number of breast 3D models acquired by means of a commercial scanner on real clinical cases.
Subject(s)
Breast/anatomy & histology , Breast/surgery , Image Interpretation, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Mammaplasty/methods , Outcome Assessment, Health Care/methods , Surgery, Computer-Assisted/methods , Algorithms , Artificial Intelligence , Female , Humans , Image Enhancement/methods , Pattern Recognition, Automated/methods , Prognosis , Reproducibility of Results , Sensitivity and Specificity , Treatment OutcomeABSTRACT
The efficiency of lossless compression algorithms for fixed-palette images (indexed images) may change if a different indexing scheme is adopted. Many lossless compression algorithms adopt a differential-predictive approach. Hence, if the spatial distribution of the indexes over the image is smooth, greater compression ratios may be obtained. Because of this, finding an indexing scheme that realizes such a smooth distribution is a relevant issue. Obtaining an optimal re-indexing scheme is suspected to be a hard problem and only approximate solutions have been provided in literature. In this paper, we restate the re-indexing problem as a graph optimization problem: an optimal re-indexing corresponds to the heaviest Hamiltonian path in a weighted graph. It follows that any algorithm which finds a good approximate solution to this graph-theoretical problem also provides a good re-indexing. We propose a simple and easy-to-implement approximation algorithm to find such a path. The proposed technique compares favorably with most of the algorithms proposed in literature, both in terms of computational complexity and of compression ratio.
