This paper explains new imaging techniques that show promising results in breast cancer detection. The
presented techniques use microwave-based methods, wavelet analyses, and neural networks to get a
suitable resolution for the breast image. One of the presented techniques (hybrid method) uses a
combination of microwaves and acoustic signals to improve the detection capability. Some promising
results are shown and explained.
This work was presented at the first Annual IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS) held as part of the IEEE Radio and Wireless Symposium 2011, in Phoenix, AZ.
Breast cancer detecting device using micro strip antennaSubham Dhar
The document describes a proposed design for a flexible microstrip patch antenna for breast cancer detection. It would work in the 2.2-2.45 GHz ISM band with a return loss of -30 dB or better. The design focuses on low skin heating, avoiding antenna arrays, and maximizing gain. It would be implanted on an F4F substrate to address issues with existing antenna designs. The document also provides background on breast cancer and discusses different detection techniques like mammography and MRI, as well as applications of antenna arrays in medical imaging and diagnosis.
This document provides an overview of a radiogenomic imaging prototype that utilizes data from The Cancer Imaging Archive (TCIA). It discusses how DICOM images contain valuable metadata and how radiomics aims to extract higher dimensional data from images for improved decision support. The prototype would allow segmentation and feature extraction of images from TCIA and correlate these with genomic and pathology data from The Cancer Genome Atlas. Within 6 months, the goal is to create a standalone Python application that demonstrates key imaging analysis capabilities and is compatible with TCIA data standards.
Modern imaging modalities with recent innovationGrinty Babu
This is a presentation on the modern diagnostic modalities used in the healthcare industry. Introduction to modality, Modalities of radiology. Hyperspectral Imaging, Electromagnetic Acoustic Imaging, Superconducting magnetic system, Waterscale mega microchip.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
1. IMRT allows delivering different dose levels to multiple tumor targets simultaneously.
2. Advanced MRI techniques like DCE and T2 mapping can help better identify diseased sites and define boost targets for IMRT planning.
3. The presenter is working to incorporate MRI data like functional MRI into the radiotherapy planning process to help optimize dose distribution and improve patient outcomes.
This document summarizes modern advances in radiation treatment for cancer. It discusses how radiation oncology aims to maximize radiation targeting of tumors while sparing surrounding healthy tissues. Key advances discussed include improved dose delivery techniques like IMRT and IMAT, more precise target definition using anatomical and functional imaging, and better target localization using image-guided radiation therapy and on-board imaging. The document also outlines potential future directions like dose escalation techniques like focal boosting, use of charged particle therapy, and stereotactic body radiotherapy.
This work was presented at the first Annual IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS) held as part of the IEEE Radio and Wireless Symposium 2011, in Phoenix, AZ.
Breast cancer detecting device using micro strip antennaSubham Dhar
The document describes a proposed design for a flexible microstrip patch antenna for breast cancer detection. It would work in the 2.2-2.45 GHz ISM band with a return loss of -30 dB or better. The design focuses on low skin heating, avoiding antenna arrays, and maximizing gain. It would be implanted on an F4F substrate to address issues with existing antenna designs. The document also provides background on breast cancer and discusses different detection techniques like mammography and MRI, as well as applications of antenna arrays in medical imaging and diagnosis.
This document provides an overview of a radiogenomic imaging prototype that utilizes data from The Cancer Imaging Archive (TCIA). It discusses how DICOM images contain valuable metadata and how radiomics aims to extract higher dimensional data from images for improved decision support. The prototype would allow segmentation and feature extraction of images from TCIA and correlate these with genomic and pathology data from The Cancer Genome Atlas. Within 6 months, the goal is to create a standalone Python application that demonstrates key imaging analysis capabilities and is compatible with TCIA data standards.
Modern imaging modalities with recent innovationGrinty Babu
This is a presentation on the modern diagnostic modalities used in the healthcare industry. Introduction to modality, Modalities of radiology. Hyperspectral Imaging, Electromagnetic Acoustic Imaging, Superconducting magnetic system, Waterscale mega microchip.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
1. IMRT allows delivering different dose levels to multiple tumor targets simultaneously.
2. Advanced MRI techniques like DCE and T2 mapping can help better identify diseased sites and define boost targets for IMRT planning.
3. The presenter is working to incorporate MRI data like functional MRI into the radiotherapy planning process to help optimize dose distribution and improve patient outcomes.
This document summarizes modern advances in radiation treatment for cancer. It discusses how radiation oncology aims to maximize radiation targeting of tumors while sparing surrounding healthy tissues. Key advances discussed include improved dose delivery techniques like IMRT and IMAT, more precise target definition using anatomical and functional imaging, and better target localization using image-guided radiation therapy and on-board imaging. The document also outlines potential future directions like dose escalation techniques like focal boosting, use of charged particle therapy, and stereotactic body radiotherapy.
IRJET- Comparison of Breast Cancer Detection using Probabilistic Neural Netwo...IRJET Journal
1) The document compares two machine learning algorithms, probabilistic neural network (PNN) and support vector machine (SVM), for detecting breast cancer in mammogram images.
2) It evaluates the performance of PNN and SVM on a dataset of 322 mammogram images containing both benign and malignant tumors.
3) The proposed methodology applies techniques like image enhancement, segmentation, and feature extraction before classifying the images using PNN and SVM to detect tumors and determine if they are benign or malignant.
Segmentation of thermograms breast cancer tarek-to-slid shareTarek Gaber
This document presents a new method for segmenting regions of interest (ROIs) in breast thermograms to detect breast abnormalities. The method uses features extracted from the ROIs, like statistical and texture features, and supports vector machines for classification. It was tested on a database of 149 patients, achieving 100% accuracy in detecting normal vs. abnormal breasts. The method provides an automatic and low-cost approach to segmenting thermograms for breast cancer detection.
This document summarizes radiotherapy techniques for treating tumors. It discusses the goals of maximizing dose to the tumor while minimizing dose to normal tissues. It then describes 3D conformal radiotherapy/IMRT which uses CT scans to delineate the tumor and organs at risk, plan the treatment using a treatment planning system, and implement the approved plan. IMRT is described as an advanced form of 3DCRT that allows for a higher conformity of dose to the tumor while better sparing critical structures. IGRT is also summarized as using image guidance to verify treatment setup and correct any errors before beginning the treatment.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
IRJET- Lung Cancer Detection using Digital Image Processing and Artificia...IRJET Journal
This document discusses a proposed system to detect lung cancer at early stages using digital image processing and artificial neural networks. The system consists of several steps: image acquisition, preprocessing using histogram equalization, segmentation using thresholding, dilation, image filling, feature extraction from CT images, and classification of images using an artificial neural network. The goal is to develop an automated diagnostic system that can maximize the detection of true positive lung cancer cases while minimizing false negatives to improve early detection rates and patient outcomes.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Recent advances in steriotactic radiosurgeryNabil Khalil
The document discusses advances in radiosurgery technology that have allowed for more precise targeting of lesions in or near the brain and spine. It describes new radiosurgery systems like the Leksell Gamma Knife Perfexion and Cyberknife that can perform stereotactic radiosurgery with enhanced features like dynamic shaping and treatment of suboccipital and extracranial targets. While the gamma knife is limited by its use of a stereotactic frame and inability to treat lesions far from the cranium, new technologies overcome limitations of standard radiation techniques for spinal tumors which require large treatment fields and cannot exclude the spinal cord from high radiation doses.
The document discusses patient safety and image quality in x-ray imaging. It notes that ionizing radiation carries risks like carcinogenesis and outlines radiation doses from common medical imaging procedures. Maintaining adequate image quality while avoiding unnecessary radiation exposure requires justification of exams, optimization of protocols, and limiting patient doses. Key principles of radiation protection aim to balance image quality needs with radiation risks.
The document discusses computer assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. It begins with an introduction to breast cancer and computer aided detection and diagnosis systems. It then provides background on areas of interest including improvement of pictorial information and machine vision. Next, it discusses microcalcifications, mammography, and mammograms. The document reviews literature on various preprocessing, feature extraction, and detection techniques. It identifies challenges in microcalcification detection including their small size and variable clusters. Finally, it outlines the plan of action for the thesis including use of the mini-MIAS mammogram database and a range of techniques to remove pectoral muscle and x-ray labels.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
The document describes the CyberKnife robotic radiosurgery system. It provides sub-millimeter accuracy for treating tumors throughout the body with precise radiation beams. Key features include its robotic ability to track and correct for tumor movement during treatment in real-time without needing invasive head/body frames. It has treated over 16,000 patients worldwide for conditions like brain, lung, prostate and spine tumors.
This document provides an overview of Intensity Modulated Radiotherapy (IMRT). It discusses the shift from conventional to conformal radiotherapy using improved imaging and planning techniques. IMRT allows customization of radiation dose distributions through non-uniform beam intensities achieved using dynamic multileaf collimators or compensators. The clinical implementation of IMRT requires treatment planning and delivery systems. IMRT offers advantages over conventional radiotherapy for many cancer types and its use has increased substantially in recent decades.
Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using convolutional neural networks to detect breast cancer from images. It begins with an abstract stating that breast cancer starts as uncontrolled growth of breast cells that can form tumors. Early detection at the first stage allows for curing. The proposed approach uses a convolutional neural network to take input images, perform preprocessing, compare to a database of cancer images, and detect cancer along with its stage to recommend treatment. It discusses using CNN algorithms inspired by the visual cortex to perform image recognition like humans. The document provides definitions of CNNs and deep learning, technologies used like image processing, and concludes that detecting and treating cancer early at its first stage is preferable.
Artificial neural network based cancer cell classificationAlexander Decker
This document summarizes an artificial neural network (ANN) based system called ANN-C3 for cancer cell classification using medical images. The system performs image pre-processing, segmentation using Harris corner detection and region growing, feature extraction of Tamura texture features, and classification using a neural network ensemble. Segmentation detects threshold points using Harris corner detection and performs region growing from these seed points. Feature extraction converts the image data into numerical form using Tamura texture features that capture variations in illumination and surfaces that human vision and surgeons use to differentiate cancerous and non-cancerous cells. The neural network is trained on a large set of labeled data to accurately classify cells.
Basic Evaluation of Antennas Used in Microwave Imaging for Breast Cancer Dete...csandit
Microwave imaging is one of the most promising techniques in diagnosis and screening of
breast cancer and in the medical field that currently under development. It is nonionizing,
noninvasive, sensitive to tumors, specific to cancers, and low-cost. Microwave measurements
can be carried out either in frequency domain or in time domain. In order to develop a
clinically viable medical imaging system, it is important to understand the characteristics of the
microwave antenna. In this paper we investigate some antenna characteristics and discuss
limitations of existing and proposed systems.
This document discusses the need for improved imaging systems for breast cancer research using animal models. It introduces a new micro-ultrasound system that allows researchers to non-invasively collect longitudinal data on tumor size, volume, vascularity and perfusion over an animal's lifespan. This significantly reduces the number of animals needed for research. The system has been adopted by leading cancer research institutions and numerous studies have been published demonstrating its ability to accurately track tumor growth and response to therapies.
IRJET- Comparison of Breast Cancer Detection using Probabilistic Neural Netwo...IRJET Journal
1) The document compares two machine learning algorithms, probabilistic neural network (PNN) and support vector machine (SVM), for detecting breast cancer in mammogram images.
2) It evaluates the performance of PNN and SVM on a dataset of 322 mammogram images containing both benign and malignant tumors.
3) The proposed methodology applies techniques like image enhancement, segmentation, and feature extraction before classifying the images using PNN and SVM to detect tumors and determine if they are benign or malignant.
Segmentation of thermograms breast cancer tarek-to-slid shareTarek Gaber
This document presents a new method for segmenting regions of interest (ROIs) in breast thermograms to detect breast abnormalities. The method uses features extracted from the ROIs, like statistical and texture features, and supports vector machines for classification. It was tested on a database of 149 patients, achieving 100% accuracy in detecting normal vs. abnormal breasts. The method provides an automatic and low-cost approach to segmenting thermograms for breast cancer detection.
This document summarizes radiotherapy techniques for treating tumors. It discusses the goals of maximizing dose to the tumor while minimizing dose to normal tissues. It then describes 3D conformal radiotherapy/IMRT which uses CT scans to delineate the tumor and organs at risk, plan the treatment using a treatment planning system, and implement the approved plan. IMRT is described as an advanced form of 3DCRT that allows for a higher conformity of dose to the tumor while better sparing critical structures. IGRT is also summarized as using image guidance to verify treatment setup and correct any errors before beginning the treatment.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
IRJET- Lung Cancer Detection using Digital Image Processing and Artificia...IRJET Journal
This document discusses a proposed system to detect lung cancer at early stages using digital image processing and artificial neural networks. The system consists of several steps: image acquisition, preprocessing using histogram equalization, segmentation using thresholding, dilation, image filling, feature extraction from CT images, and classification of images using an artificial neural network. The goal is to develop an automated diagnostic system that can maximize the detection of true positive lung cancer cases while minimizing false negatives to improve early detection rates and patient outcomes.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Recent advances in steriotactic radiosurgeryNabil Khalil
The document discusses advances in radiosurgery technology that have allowed for more precise targeting of lesions in or near the brain and spine. It describes new radiosurgery systems like the Leksell Gamma Knife Perfexion and Cyberknife that can perform stereotactic radiosurgery with enhanced features like dynamic shaping and treatment of suboccipital and extracranial targets. While the gamma knife is limited by its use of a stereotactic frame and inability to treat lesions far from the cranium, new technologies overcome limitations of standard radiation techniques for spinal tumors which require large treatment fields and cannot exclude the spinal cord from high radiation doses.
The document discusses patient safety and image quality in x-ray imaging. It notes that ionizing radiation carries risks like carcinogenesis and outlines radiation doses from common medical imaging procedures. Maintaining adequate image quality while avoiding unnecessary radiation exposure requires justification of exams, optimization of protocols, and limiting patient doses. Key principles of radiation protection aim to balance image quality needs with radiation risks.
The document discusses computer assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. It begins with an introduction to breast cancer and computer aided detection and diagnosis systems. It then provides background on areas of interest including improvement of pictorial information and machine vision. Next, it discusses microcalcifications, mammography, and mammograms. The document reviews literature on various preprocessing, feature extraction, and detection techniques. It identifies challenges in microcalcification detection including their small size and variable clusters. Finally, it outlines the plan of action for the thesis including use of the mini-MIAS mammogram database and a range of techniques to remove pectoral muscle and x-ray labels.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
The document describes the CyberKnife robotic radiosurgery system. It provides sub-millimeter accuracy for treating tumors throughout the body with precise radiation beams. Key features include its robotic ability to track and correct for tumor movement during treatment in real-time without needing invasive head/body frames. It has treated over 16,000 patients worldwide for conditions like brain, lung, prostate and spine tumors.
This document provides an overview of Intensity Modulated Radiotherapy (IMRT). It discusses the shift from conventional to conformal radiotherapy using improved imaging and planning techniques. IMRT allows customization of radiation dose distributions through non-uniform beam intensities achieved using dynamic multileaf collimators or compensators. The clinical implementation of IMRT requires treatment planning and delivery systems. IMRT offers advantages over conventional radiotherapy for many cancer types and its use has increased substantially in recent decades.
Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using convolutional neural networks to detect breast cancer from images. It begins with an abstract stating that breast cancer starts as uncontrolled growth of breast cells that can form tumors. Early detection at the first stage allows for curing. The proposed approach uses a convolutional neural network to take input images, perform preprocessing, compare to a database of cancer images, and detect cancer along with its stage to recommend treatment. It discusses using CNN algorithms inspired by the visual cortex to perform image recognition like humans. The document provides definitions of CNNs and deep learning, technologies used like image processing, and concludes that detecting and treating cancer early at its first stage is preferable.
Artificial neural network based cancer cell classificationAlexander Decker
This document summarizes an artificial neural network (ANN) based system called ANN-C3 for cancer cell classification using medical images. The system performs image pre-processing, segmentation using Harris corner detection and region growing, feature extraction of Tamura texture features, and classification using a neural network ensemble. Segmentation detects threshold points using Harris corner detection and performs region growing from these seed points. Feature extraction converts the image data into numerical form using Tamura texture features that capture variations in illumination and surfaces that human vision and surgeons use to differentiate cancerous and non-cancerous cells. The neural network is trained on a large set of labeled data to accurately classify cells.
Basic Evaluation of Antennas Used in Microwave Imaging for Breast Cancer Dete...csandit
Microwave imaging is one of the most promising techniques in diagnosis and screening of
breast cancer and in the medical field that currently under development. It is nonionizing,
noninvasive, sensitive to tumors, specific to cancers, and low-cost. Microwave measurements
can be carried out either in frequency domain or in time domain. In order to develop a
clinically viable medical imaging system, it is important to understand the characteristics of the
microwave antenna. In this paper we investigate some antenna characteristics and discuss
limitations of existing and proposed systems.
This document discusses the need for improved imaging systems for breast cancer research using animal models. It introduces a new micro-ultrasound system that allows researchers to non-invasively collect longitudinal data on tumor size, volume, vascularity and perfusion over an animal's lifespan. This significantly reduces the number of animals needed for research. The system has been adopted by leading cancer research institutions and numerous studies have been published demonstrating its ability to accurately track tumor growth and response to therapies.
A low cost and portable microwave imaging system for breast tumor detection u...rsfdtd
This document summarizes a research article that presents a new low-cost and portable microwave imaging system using an ultra-wideband directional antenna array for detecting breast tumors. Key points:
1) A compact side slotted tapered slot antenna was designed for the system with 9 slots added to enhance gain and directivity while reducing size.
2) An experimental validation was conducted using a breast phantom developed to mimic dielectric properties of real breast tissues and containing tumor inclusions.
3) Scattered signals were collected and processed using an iterative delay-and-sum algorithm to reconstruct tumor images within the breast phantom.
This document describes a new microwave imaging system for breast cancer detection that produces 3D tomographic images much faster than previous systems. The system uses an array of antennas to illuminate the breast and collects data in under 2 minutes. It then uses a discrete dipole approximation algorithm to reconstruct the 3D images in less than 20 minutes, overcoming the enormous time burdens of prior algorithms. The document presents the first clinical 3D microwave tomographic images of the breast from over 400 patient exams. Two clinical examples are shown, one demonstrating potential for breast cancer screening and another focusing on monitoring therapy response.
1. A new low-cost and portable microwave imaging system is proposed for detecting breast tumors using an ultra-wideband directional antenna array.
2. A compact tapered slot antenna is designed with side slots to enhance gain and directivity while reducing size.
3. An experimental system is developed using a breast phantom containing tumors to validate the antenna and imaging algorithm. Scattered signals are processed to reconstruct tumor images within the breast phantom.
4. Initial results demonstrate this ultra-wideband antenna-based system can successfully detect tumor clusters in breast phantoms, showing potential for clinical use.
UWB antenna with circular patch for early breast cancer detectionTELKOMNIKA JOURNAL
Breast cancer is the most common cancer in women. It has the highest incidence rate and
the highest mortality rate. In recent years, the incidence of breast cancer has become more and
more important, it is becoming the first tumor killer for women around the world. Early diagnosis is
the most important parameter for detecting cancerous tissue to prevent serious consequences. In this
electronic paper, wepresent a new design of an ultra-wide-band circular microstrip patch antenna operating
in the recommended FCC band ([3.1 GHz - 10.6 GHz]) for the detection of breast tumors. The antenna is
printed on an FR4 epoxy substrate with a dielectric permittivity r = 4.4 and loss tangent tan = 0.02.
The results obtained are largely satisfying and prove that the proposed antenna is a candidate for
biomedical applications.
Breast cancer research in animal models has long been hindered by the lack of a fast, portable, high resolution, research and animal focused imaging system that can visualize 2D tumor size, 3D tumor volume, neoangiogenesis and blood perfusion in vivo, in real-time and most importantly, non-invasively.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
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.
Skin cure an innovative smart phone based application to assist in melanoma e...sipij
This document proposes a smart phone application called SKINcure that aims to assist with melanoma early detection and prevention. The application has two main components: 1) a UV alert module that notifies users of sunburn risk and calculates time to burn, and 2) an image analysis module that allows users to take skin images and classifies them as normal, atypical, or melanoma with 96.3-97.5% accuracy by analyzing features like hair detection, lesion segmentation, and classification algorithms. The proposed system utilizes a dermoscopy image database containing 200 images for development and testing, achieving high accuracy in detecting different lesion types automatically.
SkinCure: An Innovative Smart Phone Based Application to Assist in Melanoma E...sipij
Melanoma spreads through metastasis, and therefore it has been proven to be very fatal. Statistical
evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma.
Further investigations have shown that the survival rates in patients depend on the stage of the infection;
early detection and intervention of melanoma implicates higher chances of cure. Clinical diagnosis and
prognosis of melanoma is challenging since the processes are prone to misdiagnosis and inaccuracies due
to doctors’ subjectivity. This paper proposes an innovative and fully functional smart-phone based
application to assist in melanoma early detection and prevention. The application has two major
components; the first component is a real-time alert to help users prevent skin burn caused by sunlight; a
novel equation to compute the time for skin to burn is thereby introduced. The second component is an
automated image analysis module which contains image acquisition, hair detection and exclusion, lesion
segmentation, feature extraction, and classification. The proposed system exploits PH2 Dermoscopy image
database from Pedro Hispano Hospital for development and testing purposes. The image database
contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases.
The experimental results show that the proposed system is efficient, achieving classification of the normal,
atypical and melanoma images with accuracy of 96.3%, 95.7% and 97.5%, respectively.
This document summarizes recent developments in digital analysis techniques for breast imaging, including mammography, MRI, and computer-assisted diagnosis systems. It discusses how digital mammography and computer algorithms can help detect cancers and reduce false positives compared to analog mammography. It also describes how MRI provides additional functional information and computer-assisted diagnosis can help standardize assessments of breast density and lesion characteristics. Emerging techniques like radiomics and deep learning extract large amounts of imaging features and use machine learning to correlate imaging phenotypes with cancer outcomes or risk factors.
AUTOMATIC BREAST CANCER DETECTION WITH OPTIMIZED ENSEMBLE OF CLASSIFIERSIAEME Publication
Many people are being affected by breast cancer. When numerous procedures are used to diagnose breast cancer, such as clump thickness, cell size uniformity, cell shape homogeneity, and so on, the end outcome might be challenging to get, even for medical professionals. Therefore, an automatic breast cancer detection model is developed in this research work. This research utilizes four key steps to construct an intelligent breast cancer detection approach: "(a) pre-processing, (b) segmentation, (c) feature extraction, and (d) classification". The provided input image is first pre-processed using the median filtering approach and “Contrast Limited Adaptive Histogram Equalization (CLAHE)”. Then, Chebyshev Distanced- Fuzzy C-Means Clustering (CD-FCM) is used to segment the pre-processed image for ROI recognition. The Augumented Local Vector Pattern (ALVP), Shape features, and “Gray-level Co-occurrence Matrix (GLCM)” are then extracted from the recognized ROI regions. The Improved information gain is used to choose the most optimum features from the retrieved features. Finally, an ensemble classification approach is used to complete the classification process. The “CNN-GRU [Gated Recurrent Units (GRU)-Convolutional Neural Networks (CNN)], Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbours (KNN)” are all included in this ensemble classification approach. With the specified relevant characteristics, SVM, RF, and KNN are trained. The last decision maker is the CNNGRU, which is trained using the results of SVM, RF, and KNN. The weight function of CNN-GRU is improved utilizing a newly created hybrid algorithm-Slimemould Updated Wildbeast Optimization (SUWO) formulated by integrating the principles of both Slime mould algorithm (SMA) and Wildebeest herd optimization (WHO), respectively, in order to improve the detection accuracy of CNN-GRU. Finally, a comparative evaluation is undergone to validate the efficiency of the projected model.
A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction fo...CSCJournals
Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. We concentrate on a special case of super resolution reconstruction for early detection of cancer from low resolution mammogram images. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. This paper describes a novel approach for enhancing the resolution of mammographic images. We are proposing an efficient lifting wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the digitized low resolution mammographic images are decomposed into many levels to obtain different frequency bands. We use Daubechies (D4) lifting schemes to decompose low resolution mammogram images into multilevel scale and wavelet coefficients. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution adaptive interpolation is applied. Our proposed lifting wavelet transform based restoration and adaptive interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative method, reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution mammogram image with a high PSNR, ISNR ratio and a good visual quality.
This document discusses the use of artificial intelligence in breast imaging, specifically for the early detection of breast cancer. It provides background on common breast imaging techniques like mammography, tomosynthesis, ultrasound and MRI. It then discusses traditional CAD (computer-aided detection) systems and their limitations in detecting cancers. The document introduces artificial intelligence and how techniques like machine learning and deep learning can improve upon traditional CAD systems. It reviews several studies that have found AI-based systems can help radiologists achieve higher accuracy and reduce false-positive rates compared to unaided diagnosis. Finally, it mentions several companies developing AI solutions for applications in mammography, tomosynthesis and breast MRI.
Breast Cancer Detection through Deep Learning: A ReviewIRJET Journal
Three sentences:
Convolutional neural networks show promise for automating breast cancer detection from medical images to address increasing caseloads. Previous research has developed CNN approaches for segmenting breast tissue and detecting cancer from mammograms and ultrasound images with strong accuracy. This literature review evaluates past works applying deep learning to breast cancer detection and segmentation tasks to inform the development of a new CNN-based approach detailed in future work.
A deep learning framework for accurate diagnosis of colorectal cancer using h...IJECEIAES
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
This document discusses radioimmunoscintigraphy and radioimmunotherapy for breast cancer diagnosis and treatment. It begins by introducing breast cancer as a major public health problem, and discusses various conventional imaging modalities for diagnosis like mammography and their limitations. It then highlights nuclear medicine techniques like radioimmunoscintigraphy which can help overcome these limitations by exploiting differences in tumor biology. The document also reviews current treatment methods for breast cancer including surgery, radiation therapy, chemotherapy and immunotherapy. It concludes by introducing radioimmunotherapy as a targeted molecular therapy method being researched to more efficiently treat cancer cells.
Breast conserving surgery followed by adjuvant radiotherapy is adopted in the early detected cases and mastectomy followed by radiotherapy or chemotherapy in the advanced cases are the general practices.
Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths worldwide.
Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is
preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late.
Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount
importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless,
conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not
very reliable. The microarray gene expression technology is a promising technology that couldpredict
cancer recurrence by analyzing the gene expression of sample cells. The microarray technology allows
researchers to examine the expression of thousands of genes simultaneously. This paper describes a stateof-
the-art machine learning based approach called averaged one-dependence estimators with subsumption
resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a
particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the
computational complexity, we employ an entropy-based geneselection approach to select relevant
prognosticgenes that are directly responsible for recurrence prediction. This proposed system has achieved
an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results
demonstrate the efficacy of our framework.
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1. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.3, June 2014
DOI : 10.5121/ijcses.2014.5304 41
BREAST CANCER DIAGNOSIS USING MICROWAVE
AND HYBRID IMAGING METHODS
Younis M. Abbosh
College of Electronics Engineering, Mosul University, Mosul, Iraq
ABSTRACT
This paper explains new imaging techniques that show promising results in breast cancer detection. The
presented techniques use microwave-based methods, wavelet analyses, and neural networks to get a
suitable resolution for the breast image. One of the presented techniques (hybrid method) uses a
combination of microwaves and acoustic signals to improve the detection capability. Some promising
results are shown and explained.
KEYWORDS
microwav imaging; breast cancer; hybrid imaging ; neural network; wavelet
1. INTRODUCTION
Breast cancer is one of the most common cancers diagnosed in women. In the western
countries, it is estimated that one in eleven women will develop breast cancer at some stage in
their life making it the highest life risk [1,2]. Early detection and timely medical treatment are key
factors affecting long-term survival of breast-cancer patients. Currently, the primary method for
breast screening is X-ray mammography and in some cases magnetic resonance imaging (MRI).
X-ray mammography is the golden diagnostic tool for breast cancer, but the technology has
several serious shortfalls. Firstly, it produces a relatively high false-negative rate, which can be as
large as 30% [3]. Secondly, screening mammography suffers from a high false-positive rate: on
average, 75% of breast biopsies prompted by a “suspicious” mammographic abnormality prove
benign [4]. Thirdly, screening mammography is less sensitive in women with radio-graphically
dense breast tissue prevalent in younger women, where it has been shown that X-ray
mammography has failed to detect up to 30% of cancers greater than 5 mm in diameter [5], due to
its relatively poor soft-tissue contrast.
Mammography’s other drawbacks include variability in radiological interpretation, and a
slight risk of inducing cancer due to the ionizing radiation exposure. Frequent monitoring is
difficult because of health concerns related to exposure to ionizing radiation [6]. Best prospects of
detecting malignant tissues using X-rays are when tumours reach the late stage of calcification,
which seriously jeopardises the success of treatment.
MRI is a highly sensitive imaging technique, but lacks information on specificity, and thus it
has high rate of false positive results [7]. The impact of misdiagnosis on the patient is
considerable, particularly when they are confronted with unnecessary surgeries. In general, MRI
can be considered to have a moderate success rate of correct diagnosis; however MRI has high
operational costs.
An ultimate diagnosis of all types of breast disease depends on a biopsy. A biopsy is an
invasive procedure to remove and examine tissue or cells for the presence of cancer. In most
2. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.3, June 2014
42
cases the decision for a biopsy is based on mammography findings. Unfortunately, biopsy results
indicate that 80% of breast lesions detected by mammography are benign. This situation calls for
alternative diagnostic tools to reduce physical and mental suffering of patients caused by this false
positive diagnosis. Of particular interest is the development of low-cost diagnosis methods, which
could be easily accessed by as much people as possible.
Given the deficits of the current diagnostic tools, development of imaging modalities that
enhance, complement, or replace X-ray mammography or MRI has been a priority in many
countries and by many research groups around the globe. Some of the alternative modalities
under investigation by research groups around the world are ultrasound-based and microwave-
based methods.
Ultrasound imaging of the breast is capable of distinguishing between solid tumours and
fluid-filled cysts. Also, it can be used to evaluate lumps that are hard to see on a mammogram. As
ultrasound does not harm biological tissue, thus it can be applied frequently. This is of
importance, especially with respect to younger women for whom the risks from X-ray radiation
are most significant. However, ultrasound lacks spatial resolution, cannot image calcifications and
is very operator dependent. Breast cancer is relatively iso-echoic [8] (that is, the echo similarity of
two or more tissues as measured by ultrasonography) and hence, difficult to detect by ultrasound.
Thus, unnecessary biopsies are needed on benign masses [8].
In the last years, intensive research has been conducted at many universities on different
microwave-based imaging techniques for breast cancer detection. Some of promising techniques
use suitable parameters extracted from microwave signal collected at different places around
breast as input to neural network. These parameters are extracted from time and frequency
domain using wavelet transforms [9-11]. The breast model including the probes that are used to
collect the scattered signals is analyzed using the full-wave electromagnetic simulator CST
Microwave Studio. This research has enjoyed notable success especially in the design, and
development of different devices used in those techniques [12-20]. It has been demonstrated that
the use of microwave technology, combined with acoustic signals, properly designed microwave
devices can improve the resolution needed for early breast cancer detection. Those systems are
still under continuous improvements aiming at reaching a standard required to make the system
clinically reliable.
2. MICROWAVE IMAGING
The potential for using microwaves for detecting breast tumours is based on the concept of
tissue-dependent microwave scattering and absorption in the breast to exploit the contrast in the
dielectric properties of malignant and normal breast tissues. There are many approaches in which
microwaves can be utilized in the imaging tools as shown in Fig. 1.
It has been widely assumed that normal breast tissue is largely transparent to microwaves
because they are featured with a low relative permittivity and conductivity at the microwave
frequency bands, whereas lesions, which contain more water and blood are characterized by a
high relative permittivity and conductivity at the microwave frequencies and hence they cause a
significant backscatter [21]. Upon this assumption, which is supported by some measurements,
microwave imaging systems are being designed to detect the presence of a small object inside a
breast causing a considerably larger backscatter than the surrounding medium.
Microwave techniques involve the propagation of very low levels (1000 times less than a
mobile phone) of microwave energy through the breast tissue. The basis for tumour detection and
location is the difference in the electrical properties between normal and malignant breast tissue.
3. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.3, June 2014
43
Normal breast tissue is largely transparent to microwave radiation, whereas malignant tissues,
which contain more water and blood, cause microwave signal backscattering. This scattered
signal can be picked by an array of microwave antennas and analysed using a computer.
Figure. 1 Microwave-based imaging methods
Figure. 2 Radar-based microwave imaging system of (a) monostatic, and (b) bistatic radars.
Figure. 3 Radar-based imaging of breast.
4. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.3, June 2014
44
Figure. 4 Configuration of the radar system.
Figure. 5 Imaging result of the wideband radar system.
The radar approach to microwave imaging (Fig. 2) employs generating and receiving
short pulses for various locations of probe antenna or alternatively by an array antenna. The
processed signals for various locations of a probe antenna or from array elements are combined to
form a two or three-dimensional image showing the location of a highly reflecting object
representing a cancerous tissue [12].
The configuration, shown in Fig. 2(a), is based on the principle of monostatic radar. In
this configuration, the same antenna is used for both transmitting and receiving of a microwave
signal. The configuration shown in Fig. 2(b) uses two antennas, which are displaced by a certain
distance. In this case, the microwave imaging system is based on the principle of bistatic radar.
When using the radar-based techniques to image human breast, the procedure is as shown
in Fig. 3. The configuration of a prototype radar system is shown in the Fig. 4 [16]. The system
consists of a circular cylindrical scanning platform with a resolution of 1° to support a breast
phantom, and a mechanical scanning platform with resolution of 0.1 mm in the vertical axis. The
scanning platform supports an array of wideband antennas [22]. The antenna is connected to a
microwave Vector Network Analyzer that measures the scattered signals. The collected scattered
signals are then processed in a personal computer to get an image of the breast.
The imaging capabilities of the aforementioned radar system are carried out for an artificial
breast phantom. Sample of the obtained images is shown in Fig. 5. The figure clearly shows the
boundaries of the layer representing the skin, size and shape of the object that represents the
tumor.
5. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.3, June 2014
45
The microwave-based imaging systems face two serious challenges before being ready for
clinical tests. First, the success of those systems depends on a substantial contrast in the dielectric
properties of malignant and normal breast tissues. A large scale study to experimentally determine
the dielectric properties of a variety of normal, malignant and benign breast tissues, measured at
the microwave range has recently been conducted in the USA [23, 24]. It has been demonstrated
that the dielectric contrast varies widely from individual to individual and with age and some
other biological factors. It has also been found that although the contrast between malignant and
normal adipose-dominated tissues in the breast is considerable, the contrast in the dielectric
properties is no more than about 10% between malignant and normal glandular/fibro-connective
tissues in the breast [24]. By using the scattering theory of electromagnetic fields [25], it is
possible to show that when the dielectric contrast between the normal and malignant tissue is low,
resolution of the microwave imaging system deteriorates rapidly resulting in a blurry image of the
breast.
The second important challenge that limits the success of the microwave-based imaging
systems originates from the nature of the microwave signals. The limited resolution comes from
the fact that the heterogeneous structure of the breast causes multiple scatterings and reflections
for the microwave signal while penetrating inside the tissues. This increases the uncertainty in
estimating the three-dimensional image of the breast. That uncertainty can only be removed by
additional information about the breast tissues.
3. HYBRID IMAGING
In order to remove the obstacles facing the success of the microwave-based imaging methods, the
hybrid technique is proposed and tested [17,19,20]. The hybrid technique utilizes the dielectric
(electrical properties) and elasticity (mechanical properties) contrast between tumors and healthy
tissue in order to produce a three dimensional image of the breast. The information from the
hybrid image significantly enhances early diagnostic accuracy.
The mechanical properties of biological tissues are important indicators for biomedical
diagnosis since they are generally correlated with tissue pathological changes [26]. For example,
different kinds of carcinomas of the breast have been found to be harder than its surrounding
normal tissues [27]. Although different in terms of their elasticity, some tumors are not readily
detectable by conventional imaging modalities, especially in the presence of complex background
alterations such as scar tissue, or other benign phenomena [28].
A schematic diagram of the system is shown in Fig. 6 [17-20]. Under operator guided
computer control, a very short frequency-modulated pulse is applied from an ultra wideband
(UWB) antenna beside the breast of a patient in the face-down (prone) position via a switching
unit. At the same time, an acoustic signal is applied from a transducer below the breast. The
proposed hybrid system utilizes the combined benefits of microwaves, with UWB spectrum, and
acoustic excitations to produce a three-dimensional image. In this method, microwaves are
employed to give a full view of the dielectric contrasts, while acoustics give the elasticity
distributions within the breast. The two distributions are then used to produce a final image with
high contrast and resolution.
The scattered signals, which are a consequence of the difference in the electrical and
mechanical properties of different types of breast tissues, are collected by a cylindrical array of
UWB antennas encircling the breast. Parameters of the scattered signals, i.e. amplitude, time
delay, frequency shift and their angular distribution, correlate with characteristics of the breast
tissue. For example, shift in frequency, which is called Doppler frequency, is due to elasticity
contrast, whereas time delay is due to dielectric contrast. The detection unit shown in Fig. 6
6. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.3, June 2014
46
captures those parameters, and the signal conditioning unit is designed to digitize and improve the
quality of the data for storage on a high speed computer. Operation of the different modules of
the system are controlled from a computer, which would also be used for calibration, data
collection, processing, and to display the images. Certain processing algorithms are used to
produce a clear three-dimensional image of the breast. The operator is able to distinguish tumors
from the healthy tissues of the breast by identifying the different breast regions characterized by
high backscatter and high stiffness.
The switching unit depicted in Fig. 6 includes a matrix of directional couplers and power
dividers. The unit is used to direct the generated microwave pulse towards the transmitting
antenna and the scattered pulses towards the detection unit and achieve a high level of isolation
between them. The detection unit includes a matrix of correlators, which have the ability to detect
the amplitude and phase variations of the received signals.
In order to remove undesired reflections due to the mismatch between the antenna, the skin
layer and the space separating them, the breast under test is immersed in a coupling liquid which
has material properties that reduce the backward scattering at the skin layer, and thus increasing
the dynamic range of the system [29].
Snapshots from the results of imaging using the hybrid technique are shown in Fig. 7. The
results in that figure reveal the possibility of detection as small as 1 mm tumors. This is a
promising result. However, further work needs to be done so that the imaging can be performed in
more realistic environments using heterogeneous breast phantoms.
Figure. 6 A schematic diagram showing the hybrid imaging system [14], [16], [17].
7. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.3, June 2014
47
Figure. 7 The successful detection of small tumors using the hybrid techniques on artificial breast phantom.
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using radiation force: system development and performance evaluation,” Ultrasound in Medicine and
Biology, vol. 32, no. 3, pp. 387–396, 2006.
[9] Younis M.Abbosh, Ammar F. Yahya, , and Amin Abbosh, “Neural Networks for the Detection and
Localization of Breast Cancer,” International Conference on Communications and Information
Technology (ICCIT), pp 156-159, March 2011
[10] Ammar F.Yahya, Younis M. Abbosh, and Amin Abbosh, “Microwave Imaging Method Employing
Wavelet Transform and Neural Networks for Breast Cancer Detection,” Proceedings of the Asia-
Pacific Microwave Conference 2011, pp 1418-1421
[11] A.Abbosh, Y. Abbosh, and A. Yahya “Modeling and Imaging of Breast Using the Combined Effect of
Microwave and Acoustic Pulses,” Proceedings of the Asia-Pacific Microwave Conference 2011, pp
1790-1793
[12] W.Khor, M. Bialkowski, A. Abbosh, N. Seman, and S. Crozier, “An ultra wideband microwave
imaging system for breast cancer detection,” IEICE Trans.Communications, vol. E-90B, no. 9, pp.
2376-2381, 2007.
[13] A.Abbosh, K. Kan, and M. Bialkowski, “A compact UWB planar tapered slot antenna for use in a
microwave imaging system,” Microwave and Optical Technology Letters, vol. 48, no. 11, pp. 2212-
2216, 2006.
[14] W.Khor, H. Wang, M. Bialkowski, A. Abbosh, and N. Seman, “An experimental and theoretical
investigation into capabilities of a UWB microwave imaging radar system to detect breast cancer,”
European Microwave Conference, 2007.
[15] A.Abbosh, M. Bialkowski, and S. Crozier, “A simple model for electromagnetic scattering due to
breast tumour,” IEEE-Antennas and Propagation Symposium, 2008.
8. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.3, June 2014
48
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Microwave Imaging System for Breast Cancer Detection” Proc. IEEE Antennas and Propagation
Society International Symposium, pp. 263-266, Albuquerque, New Mexico, USA, Jul 2006.
[17] A.Abbosh, S. Crozier, "Strain Imaging of the Breast by Compression Microwave Imaging," Antennas
and Wireless Propagation Letters, IEEE , vol.9, no., pp.1229-1232, 2010
[18] A.Abbosh, and S. Crozier, "Hybrid Imaging Method for Early Breast Cancer Detection," Biomedical
Engineering Conference, 2008. CIBEC 2008. Cairo International , vol., no., pp.1-4, 18-20 Dec. 2008
[19] A.Abbosh, "Strain imaging of breast using ultra-wideband pulse," Microwave Conference
Proceedings (APMC), 2010 Asia-Pacific , vol.,
[20] A.Abbosh, "Early breast cancer detection using hybrid imaging modality," Antennas and Propagation
Society International Symposium, 2009. APSURSI '09. IEEE , vol., no., pp.1-4, 1-5 June 2009
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cancer detection,” IEEE Trans. Biomed. Eng., vol. 53, pp. 1647–57, 2006.
[22] A.Abbosh, H.K. Kan and M.E. Bialkowski “Design of compact directive ultra wideband antipodal
antenna” Microwave and Optical Techn. Letters. vol. 48, No. 12, pp. 2448-2450, 2006.
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Symposium, USA, 2008.
Author
Younis M. Abbosh born in 1957 in Mosul-IRAQ. He awarded BSc in Electronic and
Communication Engineering from University of Mosul, Mosul- IRAQ in 1979.Next, he
awarded the MSc in Electronic and Communication from University of Mosul in 1985,
and PhD in Electronic and Communication in 2006 from same University. From 2007-
2010, Dr Younis worked as assistance dean of College of Electronics Engineering at
Mosul University. From 2013-now he is working as Head of Computer and Information
Department in College of Electronics Engineering.
Through his academic life he published over 10 papers in field of Electronics, Communications, and Digital
Signal Processing.