A wavelet approach for unsupervised nystagmus analysis on ENG and VOG recordings

Several algorithms are available to quantify nystagmus beats in electro nystagmography (ENG) and videooculography (VOG) recordings. These algorithms use parameterized approaches to detect the fast components of nystagmus beats. This paper proposes a wavelet approach to detect fast components of nystagmus beats. The main advantage of this approach compared to alternatives, is the completely unsupervised automated routine. The algorithm is implemented and validated in different clinical experiments. The results are compared to that of an alternative parameterized technique. Results show that the wavelet approach is suitable for automated nystagmus analysis.

Measures for the evaluation of bias field removal procedures in MRI and the design of wavelets for suppression of RF inhomogeneities

RF inhomogeneities are illumination artifacts in MR images which manifest as a multiplicative bias field. To measure the quality of an MR image with respect to RF inhomogeneities, existing multi-valued criteria are in use. Here we propose a useful conversion of these multi-valued criteria into a single measure of quality which simplifies image quality evaluation and comparison. Next, to remove such a bias field, a novel wavelet based approach is employed, that extends a previous 1D wavelet design methodology to a 2D setting. This method is found to perform well on images with strong small details. The results for brain MR images are subject to improvement, however our results hint to a future scenario for improved image quality.