FDTD Investigations into UWB Radar Technique of Breast Tumor Detection and Location

In this paper, a finite difference time domain (FDTD) method is applied to investigate capabilities of an ultra-wide band (UWB) radar system to detect and locate a breast tumor. The investigations are divided into three parts. The first part concerns an EM field analysis of a phantom formed by a plastic container with liquid and a small highly reflecting target. In the second part, a three-dimensional numerical breast model is used to perform more advanced studies. In the carried out 3D FDTD simulations, a quasi-plane wave is used as an incident wave. Various time snap shots of the electromagnetic field are recorded to learn about the physical phenomenon of reflection and scattering in different layers of the phantoms. The third part of the investigations concerns a two dimensional (cylindrical) image reconstruction, which is performed by means of 2D FDTD. The obtained results should form the ground for working out suitable guidelines for designing an optimal microwave breast imaging apparatus based on the UWB radar technique

A New Approach for Solving Inverse Scattering Problems with Overset Grid Generation Method

This paper presents a new approach of Forward-Backward Time-Stepping (FBTS) utilizing Finite-Difference Time-Domain (FDTD) method with Overset Grid Generation (OGG) method to solve the inverse scattering problems for electromagnetic (EM) waves. The proposed FDTD method is combined with OGG method to reduce the geometrically complex problem to a simple set of grids. The grids can be modified easily without the need to regenerate the grid system, thus, it provide an efficient approach to integrate with the FBTS technique. Here, the characteristics of the EM waves are analyzed. For the research mentioned in this paper, the ‘measured’ signals are syntactic data generated by FDTD simulations. While the ‘simulated’ signals are the calculated data. The accuracy of the proposed approach is validated. Good agreements are obtained between simulation data and measured data. The proposed approach has the potential to provide useful quantitative information of the unknown object particularly for shape reconstruction, object detection and others

Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

Cancer Detection Using Advanced UWB Microwave Technology

Medical diagnosis and subsequent treatment efficacy hinge on innovative imaging modalities. Among these, Microwave Imaging (MWI) has emerged as a compelling approach, offering safe and cost-efficient visualization of the human body. This comprehensive research explores the potential of the Huygens principle-based microwave imaging algorithm, specifically focusing on its prowess in cancer, lesion, and infection detection. Extensive experimentation employing meticulously crafted phantoms validates the algorithm’s robustness. In the context of lung infections, this study harnesses the power of Huygens-based microwave imaging to detect lung-COVID-19 infections. Employing Microstrip and horn antennas within a frequency range of 1 to 5 GHz and a multi-bistatic setup in an anechoic chamber, the research utilizes phantoms mimicking human torso dimensions and dielectric properties. Notably, the study achieves a remarkable detection capability, attaining a signal-to-clutter ratio of 7 dB during image reconstruction using S21 signals.A higher SCR ratio indicates better contrast and clarity of the detected inclusion, which is essential for reliable medical imaging. It is noteworthy that this achievement is realized in free space without necessitating coupling liquid, underscoring the algorithm’s practicality. Furthermore, the research delves into the validation of Huygens Principle (HP)-based microwave imaging in detecting intricate lung lesions. Utilizing a meticulously designed multi-layered phantom with characteristics closely mirroring human anatomy, the study spans frequency bands from 0.5 GHz to 3 GHz within an anechoic chamber. The outcomes are compelling, demonstrating consistent lesion detection within reconstructed images. Impressively, the signal-to-clutter ratio post-artifact removal surges to 13.4 dB, affirming the algorithm’s potential in elevating medical imaging precision. To propel the capabilities of MWI further, this research unveils a novel device: 3D microwave imaging rooted in Huygens principle. Leveraging MammoWave device’s capabilities, the study ventures into 3D image reconstruction. Dedicated phantoms housing 3D structured inclusions, each embodying distinct dielectric properties, serve as the experimental bedrock. Through an intricate interplay of data acquisition and processing, the study attains a laudable feat: seamless 3D visualization of inclusions across various z-axis planes, accompanied by minimal dimensional error not exceeding 7.5%. In a parallel exploration, spiral-like measurement configurations enter the spotlight. These configurations, meticulously tailored along the z-axis, yield promising results. The research unveils an innovative approach to reducing measurement time while safeguarding imaging fidelity. Notably, spiral-like measurements achieve a notable 50% reduction in measurement time, albeit with slight trade-offs. Signal-to-clutter ratios experience a modest reduction, and there is a minor increase in dimensional analysis error, which remains within the confines of 3.5%. The research findings serve as a testament to MWI’s efficacy across diverse medical domains. The success in lung infection and lesion detection underscores its potential impact on medical diagnostics. Moreover, the foray into 3D imaging and the strategic exploration of measurement configurations lay the foundation for future advancements in microwave imaging technologies. As a result, the outcomes of this research promise to reshape the landscape of accurate and efficient medical imaging modalities

3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated transmission coefficients between all possible combinations of antenna pairs. These transmission data were used both to train and evaluate the performance of the classification algorithm and to investigate its ability to distinguish patients with versus without intracranial bleeding. We studied how classification results were affected by the number of healthy subjects and patients used to train the algorithm, and in particular, we were interested in investigating how many samples were needed in the training dataset to obtain classification results better than chance. Our results indicated that at least 200 subjects, i.e., 100 each of the healthy subjects and bleeding patients, were needed to obtain classification results consistently better than chance (p < 0.05 using Student\u27s t-test). The results also showed that classification results improved with the number of subjects in the training data. With a sample size that approached 1000 subjects, classifications results characterized as area under the receiver operating curve (AUC) approached 1.0, indicating very high sensitivity and specificity

Classifying Breast Tumors using Medical Microwave Radar Imaging

Medical Microwave Imaging (MMI) has been studied in the past years to develop techniques to detect breast cancer at the earliest stages of development. Particularly, ultra-wideband (UWB) micro-wave radar imaging systems can detect and classify tumors as benign or malignant since this technique yields information about the size and shape of tumors. In this study we used this technology to classify tumors. The primary goal of this dissertation is two-folded. First, producing breast tumor numerical mod-els and using them in 2D MMI simulations that recreate the conditions of a UWB microwave radar imaging system. The breast tumor numerical produced resemble real tumor morphologies since they are made from breast MRI exams segmentations. Second, the data of the backscattered UWB microwave signals produced by the MMI simulations was used to classify tumors according to their size and histol-ogy, which is relevant to assess potential of UWB microwave radar imaging systems as a reliable alter-native method for the classification of breast tumors in the field of Medical Microwave Imaging. The Classification Algorithms used in this work were Pseudo Linear Discriminant Analysis (Pseudo-LDA), Pseudo Quadratic Discriminant Analysis (pseudo-QDA), and k-Nearest Neighbors (KNN), alongside with a feature extraction algorithm – Principal Component Analysis (PCA).A Imagem Médica por Microondas (do inglês, MMI) tem sido estudada nos últimos anos de forma a desenvolver técnicas de deteção do cancro da mama nas primeiras fases de desenvolvimento. Em particular, os sistemas de imagem de radar por microondas em banda ultralarga (do inglês UWB) podem detetar e classificar os tumores como benignos ou malignos, uma vez que esta técnica produz informação sobre o tamanho e a forma dos tumores. Neste estudo, utilizámos esta tecnologia para classificar os tumores. A dissertação tem dois objetivos principais. Primeiro, produzir fantomas de tumores mamários e utilizá-los em simulações de MMI em 2D que recriam as condições de um sistema de imagem de radar por microondas UWB. Os fantomas numéricos de tumores mamários produzidos possuem morfologias semelhantes a tumores reais, uma vez que são feitos a partir de segmentações de exames de ressonância magnética da mama. Em segundo lugar, as reflexões dos sinais de microondas UWB produzidos pelas simulações de MMI foram utilizados para classificar tumores de acordo com o seu tamanho e histologia, o que é relevante para avaliar o potencial dos sistemas de imagem de radar por microondas UWB como um método alternativo e fiável para a classificação de tumores mamários no campo da MMI. Os Algo-ritmos de Classificação utilizados neste trabalho foram a Pseudo Linear Discriminant Analysis (Pseudo-LDA), Pseudo Quadratic Discriminant Analysis (pseudo-QDA), e a K-Nearest Neighbors (KNN), jun-tamente com um algoritmo de extração de features - Análise de Componentes Principais (do inglês PCA)

Similarity Measurement of Breast Cancer Mammographic Images Using Combination of Mesh Distance Fourier Transform and Global Features

Similarity measurement in breast cancer is an important aspect of determining the vulnerability of detected masses based on the previous cases. It is used to retrieve the most similar image for a given mammographic query image from a collection of previously archived images. By analyzing these results, doctors and radiologists can more accurately diagnose early-stage breast cancer and determine the best treatment. The direct result is better prognoses for breast cancer patients. Similarity measurement in images has always been a challenging task in the field of pattern recognition. A widely-adopted strategy in Content-Based Image Retrieval (CBIR) is comparison of local shape-based features of images. Contours summarize the orientations and sizes images, allowing for heuristic approach in measuring similarity between images. Similarly, global features of an image have the ability to generalize the entire object with a single vector which is also an important aspect of CBIR. The main objective of this paper is to enhance the similarity measurement between query images and database images so that the best match is chosen from the database for a particular query image, thus decreasing the chance of false positives. In this paper, a method has been proposed which compares both local and global features of images to determine their similarity. Three image filters are applied to make this comparison. First, we filter using the mesh distance Fourier descriptor (MDFD), which is based on the calculation of local features of the mammographic image. After this filter is applied, we retrieve the five most similar images from the database. Two additional filters are applied to the resulting image set to determine the best match. Experiments show that this proposed method overcomes shortcomings of existing methods, increasing accuracy of matches from 68% to 88%