Inter-scale wavelet analysis for speckle reduction in thyroid ultrasound images.

A wavelet-based method for speckle suppression in ultrasound images of the thyroid gland is introduced. The classification of image pixels as speckle or part of important image structures is accomplished within the framework of back-propagation tracking and singularity detection of wavelet transform modulus maxima, derived from inter-scale analysis. A comparative study with other de-speckling techniques, employing quantitative indices, demonstrated that our method achieved superior speckle reduction performance and edge preservation properties. Moreover, a questionnaire regarding qualitative imaging parameters, emanating from various visual observations, was employed by two experienced physicians in order to evaluate the algorithm's speckle suppression efficiency.

A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images.

A hybrid model for thyroid nodule boundary detection on ultrasound images is introduced. The segmentation model combines the advantages of the "á trous" wavelet transform to detect sharp gray-level variations and the efficiency of the Hough transform to discriminate the region of interest within an environment with excessive structural noise. The proposed method comprise three major steps: a wavelet edge detection procedure for speckle reduction and edge map estimation, based on local maxima representation. Subsequently, a multiscale structure model is utilised in order to acquire a contour representation by means of local maxima chaining with similar attributes to form significant structures. Finally, the Hough transform is employed with 'a priori' knowledge related to the nodule's shape in order to distinguish the nodule's contour from adjacent structures. The comparative study between our automatic method and manual delineations demonstrated that the boundaries extracted by the hybrid model are closely correlated with that of the physicians. The proposed hybrid method can be of value to thyroid nodules' shape-based classification and as an educational tool for inexperienced radiologists.

A theoretical model evaluating the angular distribution of luminescence emission in X-ray scintillating screens.

The aim of this study was to examine the angular distribution of the light emitted from radiation-excited scintillators in medical imaging detectors. This distribution diverges from Lambert's cosine law and affects the light emission efficiency of scintillators, hence it also affects the dose burden to the patient. In the present study, the angular distribution was theoretically modeled and was used to fit experimental data on various scintillator materials. Results of calculations revealed that the angular distribution is more directional than that predicted by Lambert's law. Divergence from this law is more pronounced for high values of light attenuation coefficient and thick scintillator layers (screens). This type of divergence reduces light emission efficiency and hence it increases the incident X-ray flux required for a given level of image brightness.

Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound.

An efficient classification algorithm is proposed for characterizing breast lesions. The algorithm is based on the cubic least squares mapping and the linear-kernel support vector machine (SVM(LSM)) classifier. Ultrasound images of 154 confirmed lesions (59 benign and 52 malignant solid masses, 7 simple cysts, and 32 complicated cysts) were manually segmented by a physician using a custom developed software. Texture and outline features and the SVM(LSM) algorithm were used to design a hierarchical tree classification system. Classification accuracy was 98.7%, misdiagnosing 1 malignant an 1 benign solid lesions only. This system may be used as a second opinion tool to the radiologists.