Three-dimensional microwave breast imaging using least electrical path method

The ability to discern malignant from benign tissue in excised human breast specimens in Breast Conservation Surgery (BCS) was evaluated using single frequency terahertz radiation. Terahertz (THz) images of the specimens in reflection mode were obtained by employing a gas laser source and mechanical scanning. The images were correlated with optical histological micrographs of the same specimens, and a mean discrimination of 73% was found for five out of six samples using Receiver Operating Characteristic (ROC) analysis. The system design and characterization is discussed in detail. The initial results are encouraging but further development of the technology and clinical evaluation is needed to evaluate its feasibility in the clinical environment.

Ultrawideband radar imaging is regarded as one of the most promising alternatives for breast cancer detection. A range of algorithms reported in literature shows satisfactory tumor detection capabilities. However, most of algorithms suffer significant deterioration or even fail when the early-stage artifact, including incident signals and skin-fat interface reflections, cannot be perfectly removed from received signals. Furthermore, fibro-glandular tissue poses another challenge for tumor detection, due to the small dielectric contrast between glandular and cancerous tissues. This paper introduces a novel Robust and Artifact Resistant (RAR) algorithm, in which a neighborhood pairwise correlation-based weighting is designed to overcome the adverse effects from both artifact and glandular tissues. In RAR, backscattered signals are time-shifted, summed, and weighted by the maximum combination of the neighboring pairwise correlation coefficients between shifted signals, forming the intensity of each point within an imaging area. The effectiveness was investigated using 3-D anatomically and dielectrically accurate finite-difference-time-domain numerical breast models. The use of neighborhood pairwise correlation provided robustness against artifact and enabled the detection of multiple scatterers. RAR is compared with four well-known algorithms: delay-and-sum, delay-multiply-and-sum, modified-weighted-delay-and-sum, and filtered-delay-and-sum. It has shown that RAR exhibits improved identification capability, robust artifact resistance, and high detectability over its counterparts in most scenarios considered, while maintaining computational efficiency. Simulated tumors in both homogeneous and heterogonous, from mildly to moderately dense breast phantoms, combining an entropy-based artifact removal algorithm, were successfully identified and localized. These results show the strong potential of RAR for breast cancer screening.

A time‐domain microwave imaging system with improved image quality is presented for early detection of breast cancer. The time‐domain system has the advantage of short scan time compared with the frequency‐domain system but suffers from low signal‐to‐noise ratio (SNR) due to high attenuation of the RF signal within the breast tissue. Signal averaging of repeated measurements can improve the SNR, but this method also smears the signal peaks due to the signal‐averaged jitter, which adversely affects the reconstructed image quality. An image enhancement method using Gaussian bandpass filtering (BPF) is proposed for a time‐domain imaging system. The results from the proposed method were compared with those from the deconvolution method, which is typically used to remove the effects of signal‐averaged timing jitter in short pulse measurements. Simulation results with a 3D hemispherical homogeneous breast model containing tumour cells revealed improvement of the reconstructed image quality with the proposed method in the presence of timing jitter. The signal‐to‐mean ratio was increased from 6.46 to 14.15 with BPF, whereas deconvolution resulted in an increase to 9.36. Simulation results with a non‐homogeneous realistic breast model also showed image enhancement with the proposed method.

Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

The innovative knowledgebase system enables the healthcare system to address patient’s customized requirements. Developing and implementing this system with context-aware modifications is a challenge for developing countries, where it can change the healthcare delivery landscape from diagnosis to treatment. Although many studies have been conducted on the development of a knowledge-based system for early breast cancer detection using a rule-based approach, they lack contextual factors such as cultural aspects and socioeconomic features. This paper proposes a contextually aware machine learning (ML)-based knowledge base system for early breast cancer detection in the Pakistani context. Established risk and preventive breast cancer factors were identified from an extensive literature review and validated by academic and industry experts. Contextual factors were investigated with the assistance of field experts, and predictions from the ML algorithm were duly validated. The nested if-then rule, based on established risk and preventive factors, was combined with ML prediction to form the foundation of the proposed knowledgebase system. This research will improve the performance of healthcare systems in terms of accuracy and cost-effectiveness.

The design of a compact directive antenna for use in microwave imaging systems for early breast cancer detection is presented. The proposed element uses a modified coplanar waveguide fed tapered slot antenna. To improve the directivity and fix the direction of the mainbeam, the proposed antenna uses curved slots located near the open end of the tapered aperture and a semi-rectangular slot at the back of one of the conducting flares. The presented performance of the antenna indicates directive properties with fixed direction of end-fire radiation and distortionless time domain operation across the band from 3.1 GHz to 10.6 GHz. The proposed antenna is used to construct a circular planar array. The imaging results for a cylindrical breast modal is developed using confocal radar image reconstruction algorithm.

Breast cancer is the second most common type of cancer among women in the USA, and it is very common to appear in its invasive form. Detecting its presence in the early stages can potentially aid in the mortality rate depletion since at that point large tumours are highly unlikely to have developed. Technological advances of the last decades have provided advanced tools that employ machine learning for early detection. Common techniques include tumour imaging using special equipment that in most cases is not widely accessible. In order to overcome this limitation, new techniques that employ blood-based biomarkers are being explored. In the current work machine learning algorithms are exploited for the development of a decision support system for breast cancer using easily obtainable user information, age, body mass index, glucose and resistin. The explored algorithms include Logistic Regression, Naive Bayes, Support Vector Machine and Gradient Boosting Classification, all of which are used for the classification of new patients based on a dataset that includes information from previous breast cancer incidents. The results depict that the optimal algorithm based on the current methodology and implementation is the Gradient Boosting Classification which exhibits the highest prediction scores. In order to ensure wide accessibility, a mobile application is developed. The user can easily provide the required information for the prediction to the application and obtain the results rapidly.

In screening X-ray mammography two different views are captured and analysed of both breasts. In the four X-ray images some special signs are looked for. First the individual images are analysed independently, but good result can be achieved only if joint analysis of the images is also done. This paper proposes a simple procedure for the joint analysis of the breast's two views. The procedure is based upon the experiences of radiologists: masses and calcifications should emerge on both views, so if no matching is found, the given object is a false positive hit. First a reference system is evolved to find corresponding regions on the two images. Calcification clusters obtained in individual images are matched in “2.5 D” provided by the reference system. Masses detected in individual images are further examined with texture segmentation. The proposed approach can significantly reduce the number of false positive hits both in calcification and in mass detection.

<i>Objective:</i> This paper assesses the diagnostic performance of deep learning methods for tumour detection in breast microwave sensing (BMS). <i>Methods:</i> A convolutional neural network (CNN) was used to predict the presence of a cancerous lesion in data from experimental scans of MRI-derived phantoms. An experimental dataset containing data from 1257 scans was used. The CNN was compared to a similarly sized dense neural network (DNN) and logistic regression classifier. <i>Results:</i> The CNN was able to exploit the sinogram data structure to achieve diagnostic performance significantly better than random classification, while neither the DNN nor logistic regression classifiers could generalize to unseen test data. The area under the curve of the receiver operating characteristic curve of the CNN classifier was estimated to be between (78 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 3)&#x0025; and (90 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 3)&#x0025;, where the upper estimate was obtained when the testing set was constrained to consist of phantoms with breast volumes that are within the volume bounds of the training set and when the tumour was located at the same vertical position as the system antennas. <i>Conclusion:</i> The results obtained in this investigation demonstrate the potential of combining deep learning and BMS systems for breast cancer detection. <i>Impact:</i> This paper provides an estimate of the diagnostic performance of an air-based BMS system using deep learning methods for automatic tumour detection on a sizable experimental dataset. The performance was found to be comparable to that of AI-assisted mammography and marks a first step toward larger-scale investigations in BMS.

A novel focused active microwave system is investigated for detecting tumors in the breast. In contrast to X-ray and ultrasound modalities, the method reviewed here exploits the breast-tissue physical properties unique to the microwave spectrum, namely, the translucent nature of normal breast tissues and the high dielectric contrast between malignant tumors and surrounding lesion-free normal breast tissues. The system uses a pulsed confocal technique and time-gating to enhance the detection of tumors while suppressing the effects of tissue heterogeneity and absorption. Using published data for the dielectric properties of normal breast tissues and malignant tumors, we have conducted a two-dimensional (2-D) finite-difference time-domain (FDTD) computational electromagnetics analysis of the system. The FDTD simulations showed that tumors as small as 2 mm in diameter could be robustly detected in the presence of the background clutter generated by the heterogeneity of the surrounding normal tissue. Lateral spatial resolution of the tumor location was found to be about 0.5 cm.

Recently, a new speckle noise reduction and contrast enhancement technique has been introduced that is motivated by the research in compressive sampling or sensing. Compressive sampling is based on the principle that a sparse signal such as ultrasound can be fully recovered when sampled below the Nyquist rate. This allows for a new noise reduction technique that preserves the high frequency and fine details while reducing the effects of speckle noise. This method improves the overall perceptual quality of the image for visualization and diagnosis by the radiologist. This paper examines how the improvement in SNR makes the method suitable as a preprocessor to improve a computer aided detection (CAD) system for breast cancer detection. Classical performance metrics such as false positive rates, false negative rates and receiver operator curves will be used to show the benefits of this approach. Initial experiments look promising for microcalcification detection, where the new method yields a false negative rate of 20 percent at a false positive rate of 0.5 percent while the traditional speckle reduction techniques yield a false negative rate of 60 percent at a false positive rate of 0.5 percent.

Antenna system for breast cancer detection

In this paper, a hardware advancement for a microwave multi-static system for breast cancer detection is presented. In particular, we propose a metasurface-enhanced antenna capable of enhancing relevant “weak” signals backscattered by a tumour-mimicking target inserted in a breast phantom. Furthermore, we simulate a radar-based approach in CST Microwave Studio and use our previously developed Huygens principle-based algorithm to produce images of the target. Our results indicate that the proposed metasurface-enhanced antenna can improve target localization and decrease image artefacts.

We have previously reported investigations of a novel pulsed confocal microwave system for detection of breast tumors (Hagness et al., 1997). In this paper, we extend this work by identifying a frequency window for optimum operation of the tumor detection system. The finite difference time-domain (FDTD) method is used to study a simplified 2-D breast-tissue geometry adjacent to an elliptical reflector antenna. First, power density results are presented to demonstrate the focusing abilities of the reflector antenna for sinusoid excitation at 3, 6 and 9 GHz. Second, a tumor located at the in-breast focus of the antenna is included in the geometry in order to observe the pulse response of the detector as a function of the tumor size at center frequencies of 3, G and 9 GHz.

Extrinsically evolved system for breast cancer detection