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This study seeks to define the current state of the art in microwave breast imaging, and identify suitable design characteristics for ease of clinical use.

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This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.

In this paper, a detailed description and comparison of speckle reduction of medical ultrasound, and in particular echocardiography, is presented. Fifteen speckle reduction filters are described in a detailed fashion to facilitate implementation for research and evaluation. The filtering techniques considered include anisotropic diffusion, wavelet denoising, and local statistics. Common nomenclature and notation are adopted, to expedite comparison between approaches. Comparison of the filters is based on their application to simulated images, clinical videos, and a computational requirement analysis. The ultrasound simulation method provides a realistic model of the image acquisition process, and permits the use of a noise-free reference image for comparison. Application of objective quality metrics quantifies the preservation of image edges, overall image distortion, and improvement in image contrast. The computational analysis quantifies the number of operations required for each speckle reduction method. A speed-accuracy analysis of discretization methods for anisotropic diffusion is included. It is concluded that the optimal method is the OSRAD diffusion filter. This method is capable of strong speckle suppression, increasing the average SNRA of the simulated images by a factor of two. This method also shows favorable edge preservation and contrast improvement, and may be efficiently implemented.

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Abstract-Microwave Imaging (MI) has been widely investigated as a method to detect early stage breast cancer based on the dielectric contrast between normal and cancerous breast tissue at microwave frequencies. Furthermore, classification methods have been developed to differentiate between malignant and benign tumours. To successfully classify tumours using Ultra Wideband (UWB) radar, other features have to be examined other than simply the dielectric contrast between benign and malignant tumours, as contrast alone has been shown to be insuficient. In this context, previous studies have investigated the use of the Radar Target Signature (RTS) of tumours to give valuable information about the size, shape and surface texture. In this study, a novel classification method is examined, using Principal Component Analysis (PCA) to extract the most important tumour features from the RTS. Support Vector Machines (SVM) are then applied to the principal components as a method of classifying these tumours. Finally, several different classification architectures are compared. In this study, the performance of classifiers is tested using a database of 352 tumour models, comprising four different sizes and shapes, using the cross validation method.