UWB device for breast microwave imaging: phantom and clinical validations

Microwave imaging has received increasing attention in the last decades, motivated by its application in diagnostic imaging. Such effort has been encouraged by the fact that, at microwave frequencies, it is possible to distinguish between tissues with different dielectric properties. In such framework, a novel microwave device is presented here. The apparatus, consisting of two antennas operating in air, is completely safe and non-invasive since it does not emit any ionizing radiation and it can be used for breast lesion detection without requiring any breast crushing. We use Huygens Principle to provide a novel understanding into microwave imaging; specifically, the algorithm based on this principle provides images which represent homogeneity maps of the dielectric properties (dielectric constant and/or conductivity). The experimental results on phantoms having inclusions with different dielectric constants are presented here. In addition, the capability of the device to detect breast lesions has been verified through clinical examinations on 51 breasts. We introduce a metric to measure the non-homogeneous behaviour of the image, establishing a modality to detect the presence of inclusions inside phantoms and, similarly, the presence of a lesion inside a breast

Techniques for enhancing digital images

The images obtain from either research studies or optical instruments are often corrupted with noise. Image denoising involves the manipulation of image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms and the filtering approaches available for enhancing images and/or data transmission. Spatial-domain and Transform-domain digital image filtering algorithms have been used in the past to suppress different noise models. The different noise models can be either additive or multiplicative. Selection of the denoising algorithm is application dependent. It is necessary to have knowledge about the noise present in the image so as to select the appropriated denoising algorithm. Noise models may include Gaussian noise, Salt and Pepper noise, Speckle noise and Brownian noise. The Wavelet Transform is similar to the Fourier transform with a completely different merit function. The main difference between Wavelet transform and Fourier transform is that, in the Wavelet Transform, Wavelets are localized in both time and frequency. In the standard Fourier Transform, Wavelets are only localized in frequency. Wavelet analysis consists of breaking up the signal into shifted and scales versions of the original (or mother) Wavelet. The Wiener Filter (mean squared estimation error) finds implementations as a LMS filter (least mean squares), RLS filter (recursive least squares), or Kalman filter. Quantitative measure (metrics) of the comparison of the denoising algorithms is provided by calculating the Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE) value and the Mean Absolute Error (MAE) evaluation factors. A combination of metrics including the PSNR, MSE, and MAE are often required to clearly assess the model performance

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models

Sparsity driven ultrasound imaging

An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data

Microwave Imaging for Diagnostic Application

Imaging of the human body makes a significant contribution to the diagnosis and succeeding treatment of diseases. Among the numerous medical imaging methods, microwave imaging (MWI) is an attractive approach for medical applications due to its high potential to produce images of the human body safely with cost-efficiency. A wide range of studies and research has been done with the aim of using the microwave approach for medical applications. The focus of this research is developing MWI algorithms, which is the Huygens Principle (HP) based and to validate the capability of the proposed MWI algorithm to detect skin cancer and bone lesion through phantom measurements. The probability of the HP procedure for skin cancer detection has been investigated through design, and fabrication of a heterogeneous phantom simulating the human forearm having an inclusion mimicking a skin cancer. Ultrawideband (UWB) MWI methods are then applied to the phantom. The S21 parameter measurements are collected in an anechoic chamber environment and processed via HP technique. The tumour is successfully detected after applying appropriate artefact removal procedure. The ability to successfully apply HP to detect and locate a skin cancer type inclusion in a multilayer cylindrical phantom has been verified. The feasibility study of HP-based MWI procedure for bone lesion detection has also been investigated using a dedicated phantom. Validation has been completed through measurements inside the anechoic chamber in the frequency range of 1–3 GHz using one receiving and one transmitting antennas in free space. The identification of the lesion’s presence in different bone layers has been performed on images. The quantification of the obtained images has been performed by introducing parameters such as the resolution and signal-to-clutter ratio (S/C). The impact of different frequencies and bandwidths (in the 1–3 GHz range) in lesion detection has been investigated. The findings showed that the frequency range of 1.5–2.5 GHz offered the best resolution (1.1 cm) and S/C (2.22 on a linear scale). Subtraction between S21 obtained using two slightly displaced transmitting positions has been employed to remove the artefacts; the best artefact removal has been obtained when the spatial displacement was approximately of the same magnitude as the dimension of the lesion. Subsequently, a phantom validation of a low complexity MWI device (based on HP) operating in free space in the 1-6.5 GHz frequency band using two antennas in free space has been applied. Detection has been achieved in both bone fracture lesion and bone marrow lesion scenarios using superimposition of five doublet transmitting positions after applying the rotation subtraction method to remove artefact. A resolution of 5 mm and the S/C (3.35 in linear scale) are achieved which is clearly confirming the advantage of employing multiple transmitting positions on increased detection capability. The finding of this research verifies the dedicated MWI device as a simple, safe and without any X-ray radiation, portable, and low complexity method, which is capable of been successfully used for bone lesion detection. The outcomes of this thesis may pave the way for the construction of a dedicated bone imaging system that in future could be used as a safe diagnostic device even in emergency sites

Photoacoustic tomography and sensing in biomedicine

Photoacoustics has been broadly studied in biomedicine, for both human and small animal tissues. Photoacoustics uniquely combines the absorption contrast of light or radio frequency waves with ultrasound resolution. Moreover, it is non-ionizing and non-invasive, and is the fastest growing new biomedical method, with clinical applications on the way. This review provides a brief recap of recent developments in photoacoustics in biomedicine, from basic principles to applications. The emphasized areas include the new imaging modalities, hybrid detection methods, photoacoustic contrast agents and the photoacoustic Doppler effect, as well as translational research topics

Image fusion using Wavelet Transform: A Review

An Image fusion is the development of amalgamating two or more image of common characteristic to form a single image which acquires all the essential features of original image Nowadays lots of work is going to be done on the field of image fusion and also used in various application such as medical imaging and multi spectra sensor image fusing etc For fusing the image various techniques has been proposed by different author such as wavelet transform IHS and PCA based methods etc In this paper literature of the image fusion with wavelet transform is discussed with its merits and demerit