Information obtained from ultrasound images of fetal heads is often used to screen for various types of physical abnormality. In particular, at around 16 to 23 weeks' gestation two-dimensional cross-sections are examined to assess whether a fetus is affected by Neural Tube Defects, a class of disorders that includes Spina Bifida. Unfortunately, ultrasound images are of relatively poor quality and considerable expertise is required to extract meaningful information from them. Developing an ultrasound image recognition method that does not rely upon an experienced sonographer is of interest. In the course of this work we review standard statistical image analysis techniques, and explain why they are not appropriate for the ultrasound image data that we have. A new iterative method for edge detection based on a kernel function is developed and discussed. We then consider ways of improving existing techniques that have been applied to ultrasound Images. Storvik (1994)'s algorithm is based on the minimisation of a certain energy function by simulated annealing. We apply a cascade type blocking method to speed up this minimisation and to improve the performance of the algorithm when the noise level is high. Kass, Witkin and Terzopoulos (1988)'s method is based on an active contour or 'snake' which is deformed in such a way as to minimise a certain energy function. We suggest modifications to this energy function and use simulated annealing plus iterated conditional modes to perform the associated minimisation. We demonstrate the effectiveness of the new edge detection method, and of the improvements to the existing techniques by means of simulation studies.

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