By Matthias Hofer
Compatible for radiology citizens and radiographers, this "workbook" is an invaluable advisor in imminent ultrasound scanners. Designed as a self-learning instrument, it introduces a few of the purposes folks scanners in line with organs, together with positioning, step by step descriptions of the process, consultant ultrasound scans, explanatory drawings and an summary of crucial size facts. eventually, a self-assessment list on the finish of every bankruptcy will help readers to observe their development and overview their wisdom.
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Extra resources for Ultrasound Teaching Manual Basics of Performing, Interpreting Scans
Example text
Patient A was diagnosed with a grade III tumor, and the classification result shows the presence of regions of grade II and grade III tumors. But because a tumor is diagnosed by the most malignant tissue present, the result of the statistical method is in agreement with histopathology. 10, it was clear that some voxels in the tumorous region have equal probability of belonging to grade II and grade III classes. 11. Classification results of patients A–D. Classification was performed by applying specific thresholds to define classification accuracies.
Thus, a crude (rather low) estimate of the effective frequency IMAGE DATA REPRESENTATION: PIXEL SIZE AND IMAGE RESOLUTION 37 limit is given by flim ≈ 1 σ that is, the inverse of the standard deviation of the original Gaussian g(x). Leaving further mathematical details as well as considerations concerning the consequences of violating the assumptions (discrete point samples from a bandlimited signal) aside, the sampling theorem implies that a reasonable estimate of the sampling rate required for approximately restoring the continuous Gaussian g(x) from discrete samples is two times the above limiting frequency, that is, 2/σ .
Finite Mixture Models. New York: John Wiley and Sons; 2000. 14. Halkidi M, Batistakis Y, Vazirgiannis M. On clustering validation techniques. J Intell Inf Syst 2001;17:107–145. 15. Noordam JC, van den Broek WHAM. Multivariate image segmentation based on geometrically guided fuzzy C-means clustering. J Chemom 2002;16:1–11. 16. Teppola P, Mujunen SP, Minkkinen P. Adaptive fuzzy C-means clustering in process monitoring. Chemom Intell Lab Syst 1999;45:23–38. 17. Smolinski A, Walczak B, Einax JW. Hierarchical clustering extended with visual complements of environmental data set.
