A review of image watermarking applications in healthcare.

In this article, we focus on the complementary role of watermarking with respect to medical information security (integrity, authenticity ...) and management. We review sample cases where watermarking has been deployed, we conclude that watermarking has found a niche role in healthcare systems, as an instrument for protection of medical information, for secure sharing and handling of medical images. The concern of medical experts on the preservation of documents diagnostic integrity remains paramount.

Visual classification of medical data using MLP mapping.

In this work we discuss the design of a novel non-linear mapping method for visual classification based on multilayer perceptrons (MLP) and assigned class target values. In training the perceptron, one or more target output values for each class in a 2-dimensional space are used. In other words, class membership information is interpreted visually as closeness to target values in a 2D feature space. This mapping is obtained by training the multilayer perceptron (MLP) using class membership information, input data and judiciously chosen target values. Weights are estimated in such a way that each training feature of the corresponding class is forced to be mapped onto the corresponding 2-dimensional target value.

VQ-adaptive block transform coding of images.

Two new design techniques for adaptive orthogonal block transforms based on vector quantization (VQ) codebooks are presented. Both techniques start from reference vectors that are adapted to the characteristics of the signal to be coded, while using different methods to create orthogonal bases. The resulting transforms represent a signal coding tool that stands between a pure VQ scheme on one extreme and signal-independent, fixed block transformation-like discrete cosine transform (DCT) on the other. The proposed technique has superior compaction performance as compared to DCT both in the rendition of details of the image and in the peak signal-to-noise ratio (PSNR) figures.

Multidirectional and multiscale edge detection via M-band wavelet transform.

In this correspondence, the problem of directional and multiscale edge detection is considered. Orthogonal and linear-phase M-band wavelet transform is used to decompose the image into MxM channels. These channels are then combined such that each combination, which we refer to as decomposition filter, results in zero-crossings at the locations of edges corresponding to different directions and resolutions, and inherently performs regularization against noise. By applying a zero-crossing detector on the outputs of the decomposition filters, edge maps of desired resolution and direction are obtained. In addition, with the application of the Teager's energy operator at the analysis stage, it is possible to obtain a reduction in unwanted zero-crossings. Final edge maps of images are obtained through simple combinations of directional edge maps.

Multiresolution biological transient extraction applied to respiratory crackles.

A method is proposed for the detection of transients in biological signals. The method is based on enhancing the transient-to-background ratio by a series of operations such as background whitening, wavelet-based multiresolution decomposition and application of Teager's energy operator. The transients are extracted by judiciously thresholding this processed signal. The proposed detector is applied to the discrimination of crackles in pathological respiratory sounds. It is shown that both the crackle detection performance and ability to extract the transient waveforms correctly are superior to existing detectors in the literature.

Comparison of AR-based algorithms for respiratory sounds classification.

Respiratory sounds of pathological and healthy subjects were analyzed via autoregressive (AR) models with a view to construct a diagnostic aid based on auscultation. Using the AR vectors, two reference libraries, pathological and healthy, were built. Two classifiers, k-nearest neighbour (k-NN) classifier and a quadratic classifier, were designed and compared. Performances of the classifiers were tested for different model orders. The best classification results were obtained for model order 6.