This document proposes a non-invasive skin lesion analysis system for early detection of malignant melanoma using image processing in MATLAB. The system has two main parts: 1) a sunburn monitoring app to track sun exposure and 2) an automatic image analysis module. The image analysis module segments skin lesions, extracts features like shape, color and texture, and classifies lesions as benign, atypical or skin cancer with over 95% accuracy. It was tested on 200 dermoscopy images from a Portuguese hospital and achieved high classification performance. The proposed system provides an affordable, effective tool for early melanoma detection using a mobile phone platform.
IRJET- Three-Dimensional Analysis on Dermoscopic Images with RSA Encrypted Di...IRJET Journal
This document presents a proposed method for three-dimensional analysis of dermoscopic images with encrypted diagnosis using RSA encryption. The method involves converting 2D dermoscopic skin lesion images into 3D images after estimating depth. 3D shape, texture, and color features are extracted from the 3D reconstructed images. Two classifiers, AdaBoost and multi-SVM, are used to classify lesions and diagnose conditions like melanoma, blue nevus, and seborrheic keratosis. The results are encrypted using RSA encryption before being output for security and privacy. The method aims to help detect skin cancer at early stages through noninvasive 3D analysis and encrypted diagnosis of dermoscopic images.
Digital Breast Tomosynthesis, MicrocalcificationsNaglaa Mahmoud
This study compared the characterization of microcalcification clusters using 2D digital mammography (FFDM) and digital breast tomosynthesis (DBT) in 107 cases. There were 11 discordant results where DBT classified clusters lower than FFDM. DBT incorrectly underclassified 3 clusters as benign that were malignant. However, DBT correctly classified 8 clusters as benign that FFDM misclassified as suspicious. While diagnostic performance between the two modalities was similar, the authors conclude DBT has the potential to underestimate a small portion of malignant lesions, so 2D plus 3D imaging is recommended for breast screening to avoid missing microcalcification clusters.
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.
IRJET- Survey on Face Detection MethodsIRJET Journal
The document reviews 15 papers on various face detection methods published between 2013 and 2018. It finds that the most popular feature extraction method is skin color segmentation, which achieves detection rates of 88-98%. The Viola-Jones method typically detects face regions as well as other body parts at a rate of 80-90%. Common face detection methods reviewed include skin color segmentation, Viola-Jones, Haar features, 3D mean shift, and Cascaded Head and Shoulder Detection. OpenCV, Python or MATLAB are typically used to implement real-time face detection systems.
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
Microwave imaging shows promise for breast cancer screening by taking advantage of the dielectric property differences between normal and malignant breast tissues. A microwave imaging system was developed at Dartmouth that incorporates patient anatomical information from MRI to improve the spatial resolution of the reconstructed microwave images. Initial phantom and clinical studies demonstrate that including structural information enhances the ability to detect and characterize abnormalities. Further research is still needed including bilateral breast imaging and customized MRI coils to enable viable 3D microwave imaging.
The document discusses digital breast tomosynthesis (DBT), a new 3D mammography technique. It provides 3 key benefits over traditional digital mammography: 1) improved cancer detection rates, especially for women with dense breasts, 2) better characterization of lesions by reducing tissue overlap, and 3) more precise lesion localization to guide biopsies. While offering advantages, DBT imaging requires optimization of technical parameters like scan angle and number of projections to maximize image quality within dose constraints. Advanced iterative reconstruction algorithms have also been developed specifically for DBT's geometry. The document examines several DBT systems and applications, demonstrating improved visualization for screening, diagnosis and interventional guidance.
IRJET- Three-Dimensional Analysis on Dermoscopic Images with RSA Encrypted Di...IRJET Journal
This document presents a proposed method for three-dimensional analysis of dermoscopic images with encrypted diagnosis using RSA encryption. The method involves converting 2D dermoscopic skin lesion images into 3D images after estimating depth. 3D shape, texture, and color features are extracted from the 3D reconstructed images. Two classifiers, AdaBoost and multi-SVM, are used to classify lesions and diagnose conditions like melanoma, blue nevus, and seborrheic keratosis. The results are encrypted using RSA encryption before being output for security and privacy. The method aims to help detect skin cancer at early stages through noninvasive 3D analysis and encrypted diagnosis of dermoscopic images.
Digital Breast Tomosynthesis, MicrocalcificationsNaglaa Mahmoud
This study compared the characterization of microcalcification clusters using 2D digital mammography (FFDM) and digital breast tomosynthesis (DBT) in 107 cases. There were 11 discordant results where DBT classified clusters lower than FFDM. DBT incorrectly underclassified 3 clusters as benign that were malignant. However, DBT correctly classified 8 clusters as benign that FFDM misclassified as suspicious. While diagnostic performance between the two modalities was similar, the authors conclude DBT has the potential to underestimate a small portion of malignant lesions, so 2D plus 3D imaging is recommended for breast screening to avoid missing microcalcification clusters.
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.
IRJET- Survey on Face Detection MethodsIRJET Journal
The document reviews 15 papers on various face detection methods published between 2013 and 2018. It finds that the most popular feature extraction method is skin color segmentation, which achieves detection rates of 88-98%. The Viola-Jones method typically detects face regions as well as other body parts at a rate of 80-90%. Common face detection methods reviewed include skin color segmentation, Viola-Jones, Haar features, 3D mean shift, and Cascaded Head and Shoulder Detection. OpenCV, Python or MATLAB are typically used to implement real-time face detection systems.
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
Microwave imaging shows promise for breast cancer screening by taking advantage of the dielectric property differences between normal and malignant breast tissues. A microwave imaging system was developed at Dartmouth that incorporates patient anatomical information from MRI to improve the spatial resolution of the reconstructed microwave images. Initial phantom and clinical studies demonstrate that including structural information enhances the ability to detect and characterize abnormalities. Further research is still needed including bilateral breast imaging and customized MRI coils to enable viable 3D microwave imaging.
The document discusses digital breast tomosynthesis (DBT), a new 3D mammography technique. It provides 3 key benefits over traditional digital mammography: 1) improved cancer detection rates, especially for women with dense breasts, 2) better characterization of lesions by reducing tissue overlap, and 3) more precise lesion localization to guide biopsies. While offering advantages, DBT imaging requires optimization of technical parameters like scan angle and number of projections to maximize image quality within dose constraints. Advanced iterative reconstruction algorithms have also been developed specifically for DBT's geometry. The document examines several DBT systems and applications, demonstrating improved visualization for screening, diagnosis and interventional guidance.
Digital Breast Tomosynthesis with Minimal CompressionDavid Scaduto
Breast compression is utilized in mammography to improve image quality and reduce radiation dose. Lesion conspicuity is improved by reducing scatter effects on contrast and by reducing the superposition of tissue structures. However, patient discomfort due to breast compression has been cited as a potential cause of noncompliance with recommended screening practices. Further, compression may also occlude blood flow in the breast, complicating imaging with intravenous contrast agents and preventing accurate quantification of contrast enhancement and kinetics. Previous studies have investigated reducing breast compression in planar mammography and digital breast tomosynthesis (DBT), though this typically comes at the expense of degradation in image quality or increase in mean glandular dose (MGD). We propose to optimize the image acquisition technique for reduced compression in DBT without compromising image quality or increasing MGD. A zero-frequency signal-difference-to-noise ratio model is employed to investigate the relationship between tube potential, SDNR and MGD. Phantom and patient images are acquired on a prototype DBT system using the optimized imaging parameters and are assessed for image quality and lesion conspicuity. A preliminary assessment of patient motion during DBT with minimal compression is presented.
1) The document presents a new face parts detection algorithm that combines the Viola-Jones object detection framework with geometric information of facial features.
2) It detects faces, then isolates regions of interest for the eyes, nose, and mouth. Eye pupils are located using iris recognition techniques.
3) The algorithm was tested on hundreds of images and showed promising results for automated facial feature detection.
MICROCALCIFICATION IDENTIFICATION IN DIGITAL MAMMOGRAM FOR EARLY DETECTION OF...Nashid Alam
Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. These texture features will be used to classify the microcalcifications as either malignant or benign.
Digital breast tomosynthesis (DBT) is an emerging 3D breast imaging technique that involves acquiring low-dose X-ray images of the stationary breast from multiple angles to create tomographic slices. The first DBT system was introduced in 2011. DBT provides improved visualization of lesions and reduced call back rates compared to 2D digital mammography. While DBT exposes patients to a higher radiation dose than 2D mammography alone, combining DBT with a synthesized 2D mammogram can achieve a radiation dose similar to standard mammography alone. DBT is being widely adopted for breast cancer screening due to its superior performance over conventional digital mammography.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.
Table of Contents - June 2021, Volume 12, Number 3sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
Digital breast tomosynthesis (DBT) provides 3D imaging of the breast and has been shown to improve cancer detection rates compared to digital mammography alone. DBT aims to overcome limitations of 2D mammography in dense breasts by acquiring multiple low-dose projection images over an angular range and using advanced reconstruction algorithms to generate 1mm thickness slices. Studies have demonstrated DBT improves cancer detection sensitivity by 15-25% and reduces recall rates by around 15% compared to digital mammography. Additionally, synthesizing 2D images from DBT data can provide diagnostic accuracy comparable to full field digital mammography with a reduced radiation dose. DBT is becoming widely adopted for breast cancer screening due to its superior performance over digital mammography.
3D mammography provides improved detection of breast cancer compared to traditional 2D mammography. The 3D mammogram process takes only a few seconds longer than a standard mammogram and works by capturing millimeter-thick images from different angles as the arm moves slightly during breast compression. These images are then reassembled with advanced software to create a 3D image, allowing for clearer visualization and a 35% improvement in cancer detection rates compared to 2D mammograms. Radiation exposure is the same as a standard mammogram and insurance and payment options are available to make 3D mammograms affordable.
This document discusses the advancement of mammographic equipment. It begins by introducing the components and purpose of mammography equipment. Key components discussed in detail include the x-ray tube, compressor, anti-scatter grid, cassette holder, and digital detectors. The document then covers recent advancements, such as digital mammography technologies like computed radiography, full-field digital mammography, and digital breast tomosynthesis, which uses 3D imaging to improve cancer detection rates.
Mammography is the cornerstone of breast imaging and offers the necessary reliability to diagnose curable breast cancers. It involves using low-dose x-rays of the breast to detect tumors that are too small to feel. Digital mammography offers superior contrast resolution in dense breasts compared to conventional mammography but has lower spatial resolution, potentially missing some lesions. Mammography equipment includes an x-ray tube, compression device, and digital detectors to capture and process images, allowing diagnosis according to the BI-RADS assessment categories.
This document discusses treatment of cancer of the maxilla bone. It begins by describing the anatomy and symptoms of maxillary sinus cancer. Risk factors include occupational exposures, smoking, and chronic sinusitis. Investigations may include imaging and biopsies. Management involves surgery, radiation therapy, or a combination. Radiation techniques have advanced from conventional to 3D conformal and IMRT to better spare nearby organs at risk like the eyes and brain. Doses above 60Gy generally improve outcomes but can cause side effects like dry mouth and optic neuropathy if not carefully planned. Patient immobilization and monitoring during treatment are important.
This document provides an overview of a medical device called the NIRvana Sensing Glove that uses near-infrared sensors and haptic feedback. The glove is being developed to detect changes in breast tissue properties associated with cancer by analyzing NIR light absorption and translating it into vibrational feedback. Currently the prototype acts as a pulse oximeter that detects the pulse and replicates the feeling through vibration motors. The document describes the device components, operating principles using NIR light absorption analysis, functional requirements, constraints of the design, competitive products, and provides context on the goal of developing an affordable breast cancer screening tool.
CBCT has become an important tool in clinical orthodontics for providing 3D information. It was developed due to increasing demand for 3D data from conventional CT scans. This article discusses CBCT technology and its various uses in orthodontics such as detection of facial asymmetry, assessment of mandibular shape and growth, localization of impacted teeth, evaluation of root resorption and airway changes. CBCT allows more accurate diagnosis and treatment planning compared to 2D radiographs and has largely replaced conventional records in digital orthodontic records.
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.
This document provides an overview of mammography and the specialized equipment needed to perform it. Mammography requires high resolution and contrast to detect small breast lesions. Dedicated mammography equipment has a molybdenum target and rhodium filter to produce low-energy x-rays best suited for soft breast tissue. It also uses a small focal spot, grid, and compression to reduce noise and improve image quality while minimizing radiation dose. Stereotactic biopsy systems allow targeted needle biopsies of lesions using two x-ray angles to calculate the three-dimensional location.
This document describes a proposed method for automated object detection and suspicious behavior alert in ATMs using an embedded system. The method uses facial recognition with occlusion handling to identify users at an ATM and detect suspicious behaviors in real-time video. It trains a model on sample input images and recognizes faces in video sequences despite occlusions like eyeglasses or masks. When an unknown face or suspicious behaviors like fighting are detected, an alert is triggered. The method was implemented on an ARM 11 embedded system connected to a camera and tested on a database of ATM user images with results showing it can reasonably perform recognition in practical ATM environments.
Dr Patrick Treacy shares some of his most challenging
cases. This month he talks about treating intradermal
naevi with Ellman radiosurgery. This is the classical skin-coloured ‘mole’, elevated from the skin’s surface. They are
not pigmented because the tumours originate deep within the dermis, rather than at the junction of the epidermis and dermis where the melanocyte layer is situated.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
Digital Breast Tomosynthesis with Minimal CompressionDavid Scaduto
Breast compression is utilized in mammography to improve image quality and reduce radiation dose. Lesion conspicuity is improved by reducing scatter effects on contrast and by reducing the superposition of tissue structures. However, patient discomfort due to breast compression has been cited as a potential cause of noncompliance with recommended screening practices. Further, compression may also occlude blood flow in the breast, complicating imaging with intravenous contrast agents and preventing accurate quantification of contrast enhancement and kinetics. Previous studies have investigated reducing breast compression in planar mammography and digital breast tomosynthesis (DBT), though this typically comes at the expense of degradation in image quality or increase in mean glandular dose (MGD). We propose to optimize the image acquisition technique for reduced compression in DBT without compromising image quality or increasing MGD. A zero-frequency signal-difference-to-noise ratio model is employed to investigate the relationship between tube potential, SDNR and MGD. Phantom and patient images are acquired on a prototype DBT system using the optimized imaging parameters and are assessed for image quality and lesion conspicuity. A preliminary assessment of patient motion during DBT with minimal compression is presented.
1) The document presents a new face parts detection algorithm that combines the Viola-Jones object detection framework with geometric information of facial features.
2) It detects faces, then isolates regions of interest for the eyes, nose, and mouth. Eye pupils are located using iris recognition techniques.
3) The algorithm was tested on hundreds of images and showed promising results for automated facial feature detection.
MICROCALCIFICATION IDENTIFICATION IN DIGITAL MAMMOGRAM FOR EARLY DETECTION OF...Nashid Alam
Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. These texture features will be used to classify the microcalcifications as either malignant or benign.
Digital breast tomosynthesis (DBT) is an emerging 3D breast imaging technique that involves acquiring low-dose X-ray images of the stationary breast from multiple angles to create tomographic slices. The first DBT system was introduced in 2011. DBT provides improved visualization of lesions and reduced call back rates compared to 2D digital mammography. While DBT exposes patients to a higher radiation dose than 2D mammography alone, combining DBT with a synthesized 2D mammogram can achieve a radiation dose similar to standard mammography alone. DBT is being widely adopted for breast cancer screening due to its superior performance over conventional digital mammography.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.
Table of Contents - June 2021, Volume 12, Number 3sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
Digital breast tomosynthesis (DBT) provides 3D imaging of the breast and has been shown to improve cancer detection rates compared to digital mammography alone. DBT aims to overcome limitations of 2D mammography in dense breasts by acquiring multiple low-dose projection images over an angular range and using advanced reconstruction algorithms to generate 1mm thickness slices. Studies have demonstrated DBT improves cancer detection sensitivity by 15-25% and reduces recall rates by around 15% compared to digital mammography. Additionally, synthesizing 2D images from DBT data can provide diagnostic accuracy comparable to full field digital mammography with a reduced radiation dose. DBT is becoming widely adopted for breast cancer screening due to its superior performance over digital mammography.
3D mammography provides improved detection of breast cancer compared to traditional 2D mammography. The 3D mammogram process takes only a few seconds longer than a standard mammogram and works by capturing millimeter-thick images from different angles as the arm moves slightly during breast compression. These images are then reassembled with advanced software to create a 3D image, allowing for clearer visualization and a 35% improvement in cancer detection rates compared to 2D mammograms. Radiation exposure is the same as a standard mammogram and insurance and payment options are available to make 3D mammograms affordable.
This document discusses the advancement of mammographic equipment. It begins by introducing the components and purpose of mammography equipment. Key components discussed in detail include the x-ray tube, compressor, anti-scatter grid, cassette holder, and digital detectors. The document then covers recent advancements, such as digital mammography technologies like computed radiography, full-field digital mammography, and digital breast tomosynthesis, which uses 3D imaging to improve cancer detection rates.
Mammography is the cornerstone of breast imaging and offers the necessary reliability to diagnose curable breast cancers. It involves using low-dose x-rays of the breast to detect tumors that are too small to feel. Digital mammography offers superior contrast resolution in dense breasts compared to conventional mammography but has lower spatial resolution, potentially missing some lesions. Mammography equipment includes an x-ray tube, compression device, and digital detectors to capture and process images, allowing diagnosis according to the BI-RADS assessment categories.
This document discusses treatment of cancer of the maxilla bone. It begins by describing the anatomy and symptoms of maxillary sinus cancer. Risk factors include occupational exposures, smoking, and chronic sinusitis. Investigations may include imaging and biopsies. Management involves surgery, radiation therapy, or a combination. Radiation techniques have advanced from conventional to 3D conformal and IMRT to better spare nearby organs at risk like the eyes and brain. Doses above 60Gy generally improve outcomes but can cause side effects like dry mouth and optic neuropathy if not carefully planned. Patient immobilization and monitoring during treatment are important.
This document provides an overview of a medical device called the NIRvana Sensing Glove that uses near-infrared sensors and haptic feedback. The glove is being developed to detect changes in breast tissue properties associated with cancer by analyzing NIR light absorption and translating it into vibrational feedback. Currently the prototype acts as a pulse oximeter that detects the pulse and replicates the feeling through vibration motors. The document describes the device components, operating principles using NIR light absorption analysis, functional requirements, constraints of the design, competitive products, and provides context on the goal of developing an affordable breast cancer screening tool.
CBCT has become an important tool in clinical orthodontics for providing 3D information. It was developed due to increasing demand for 3D data from conventional CT scans. This article discusses CBCT technology and its various uses in orthodontics such as detection of facial asymmetry, assessment of mandibular shape and growth, localization of impacted teeth, evaluation of root resorption and airway changes. CBCT allows more accurate diagnosis and treatment planning compared to 2D radiographs and has largely replaced conventional records in digital orthodontic records.
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.
This document provides an overview of mammography and the specialized equipment needed to perform it. Mammography requires high resolution and contrast to detect small breast lesions. Dedicated mammography equipment has a molybdenum target and rhodium filter to produce low-energy x-rays best suited for soft breast tissue. It also uses a small focal spot, grid, and compression to reduce noise and improve image quality while minimizing radiation dose. Stereotactic biopsy systems allow targeted needle biopsies of lesions using two x-ray angles to calculate the three-dimensional location.
This document describes a proposed method for automated object detection and suspicious behavior alert in ATMs using an embedded system. The method uses facial recognition with occlusion handling to identify users at an ATM and detect suspicious behaviors in real-time video. It trains a model on sample input images and recognizes faces in video sequences despite occlusions like eyeglasses or masks. When an unknown face or suspicious behaviors like fighting are detected, an alert is triggered. The method was implemented on an ARM 11 embedded system connected to a camera and tested on a database of ATM user images with results showing it can reasonably perform recognition in practical ATM environments.
Dr Patrick Treacy shares some of his most challenging
cases. This month he talks about treating intradermal
naevi with Ellman radiosurgery. This is the classical skin-coloured ‘mole’, elevated from the skin’s surface. They are
not pigmented because the tumours originate deep within the dermis, rather than at the junction of the epidermis and dermis where the melanocyte layer is situated.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
Automated Screening System for Acute Skin Cancer Detection Using Neural Netwo...IRJET Journal
The document describes an automated screening system for detecting acute skin cancer using neural networks and texture analysis. The proposed system aims to automatically detect melanoma in skin lesion images captured by smartphones with higher accuracy than existing methods. It uses techniques like texture segmentation, Gray Level Co-occurrence Matrix (GLCM) for feature extraction, and a neural network for classification. The results show the proposed system can detect melanoma in images with 97% accuracy, an increase over prior methods.
Skin Cancer Detection Using Deep Learning TechniquesIRJET Journal
This document proposes a method to detect skin cancer using deep learning techniques. The method uses a dataset of 3000 skin cancer images to train models like YOLOR and EfficientNet B0. It involves pre-processing images by resizing, removing hair, and augmenting data. Features are extracted using YOLOR and images are classified into 9 classes of skin conditions using a CNN with EfficientNet B0 architecture. The models are trained and tested on the dataset, with results and discussion to follow in the next section.
IRJET- Skin Cancer Prediction using Image Processing and Deep LearningIRJET Journal
This document discusses using deep learning and image processing to develop a model for skin cancer detection. It begins with an introduction to the rising problem of skin cancer cases and importance of early detection. Next, it describes the process of visual inspection and dermoscopy images currently used by dermatologists. The document then reviews literature on existing methods for skin cancer detection using machine learning approaches like convolutional neural networks (CNNs). Deeper CNN models that can learn from limited data are highlighted. Finally, the document outlines the fundamentals of different types of skin cancer and concludes by acknowledging guidance received to complete the project.
IRJET- Skin Cancer Detection using Digital Image ProcessingIRJET Journal
This document describes research on developing a system for detecting skin cancer through digital image processing. The system uses dermoscopic images of skin lesions that are preprocessed to remove noise. Texture features are then extracted from the images using Gray Level Co-occurrence Matrices and Gabor filtering. These features are input into a support vector machine for classification of images into cancerous or non-cancerous categories. The researchers achieved an accuracy of 77% and discuss potential improvements and applications of the system to help dermatologists detect melanoma and other skin cancers at early stages.
The document describes a skin cancer detection mobile application that uses image processing and machine learning. The application analyzes skin images for characteristics of melanoma like asymmetry, border, color, diameter and texture. It trains a model using the MobileNet-v2 architecture on datasets containing thousands of images. The trained model achieves 70% accuracy in detecting melanoma and differentiating normal and abnormal skin lesions when tested on new images. The application has potential to help identify skin cancer in early stages and assist medical practitioners.
Melanoma Skin Cancer Detection using Deep LearningIRJET Journal
This document presents research on developing a deep learning model to detect melanoma skin cancer. The researchers created a convolutional neural network called Xception to analyze images of skin lesions and classify them as benign or malignant. They developed a web application using Flask that allows users to upload images for analysis. The Xception model achieved 97% accuracy on a test dataset. The web app was also able to accurately classify images, demonstrating its potential to assist dermatologists in early detection of melanoma skin cancer. However, further improvements are still needed before the model and web app can be fully relied upon for clinical diagnosis.
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.
Computer Vision for Skin Cancer Diagnosis and Recognition using RBF and SOMCSCJournals
Human skin is the largest organ in our body which provides protection against heat, light, infections and injury. It also stores water, fat, and vitamin. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Skin cancer is the most commonly diagnosed type of cancer among men and women. Exposure to UV rays, modernize diets, smoking, alcohol and nicotine are the main cause. Cancer is increasingly recognized as a critical public health problem in Ethiopia. There are three type of skin cancer and they are recognized based on their own properties. In view of this, a digital image processing technique is proposed to recognize and predict the different types of skin cancers using digital image processing techniques. Sample skin cancer image were taken from American cancer society research center and DERMOFIT which are popular and widely focuses on skin cancer research. The classification system was supervised corresponding to the predefined classes of the type of skin cancer. Combining Self organizing map (SOM) and radial basis function (RBF) for recognition and diagnosis of skin cancer is by far better than KNN, Naïve Bayes and ANN classifier. It was also showed that the discrimination power of morphology and color features was better than texture features but when morphology, texture and color features were used together the classification accuracy was increased. The best classification accuracy (88%, 96.15% and 95.45% for Basal cell carcinoma, Melanoma and Squamous cell carcinoma respectively) were obtained using combining SOM and RBF. The overall classification accuracy was 93.15%.
Skin Cancer Detection using Digital Image Processing and Implementation using...ijtsrd
Melanoma is a serious type of skin cancer. It starts in skin cells called melanocytes. There are 3 main types of skin cancer, Melanoma, Basal and Squamous cell carcinoma. Melanoma is more likely to spread to other parts of the body. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. In this work, a computerised method has been developed to make use of Neural Networks in the field of medical image processing. The ultimate aim of this paper is to implement cost-effective emergency support systems to process the medical images. It is more advantageous to patients. The dermoscopy image of suspect area of skin cancer is taken and it goes under various pre-processing technique for noise removal and image enhancement. Then the image is undergone to segmentation using Thresholding method. Some features of image have to be extracted using ABCD rules. In this work, Asymmetry index and Geometric features are extracted from the segmented image. These features are given as the input to classifier. Artificial Neural Network ANN with feed forward architecture is used for classification purpose. It classifies the given image into cancerous or non-cancerous. The proposed algorithm has been tested on the ISIC International Skin Imaging Collaboration 2017 training and test datasets. The ground truth data of each image is available as well, so performance of this work can evaluate quantitatively. Khaing Thazin Oo | Dr. Moe Mon Myint | Dr. Khin Thuzar Win "Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18751.pdf
The document discusses melanoma skin cancer detection using a computer-aided diagnosis system based on dermoscopic images. It begins with an introduction to skin cancer and melanoma. It then reviews existing literature on automated melanoma detection systems that use techniques like image preprocessing, segmentation, feature extraction and classification. Features extracted in other studies include asymmetry, border irregularity, color, diameter and texture-based features. The proposed system collects dermoscopic images and performs preprocessing, segmentation, extracts 9 features based on the ABCD rule, and classifies images using a neural network classifier to detect melanoma. It aims to develop an automated diagnosis system to eliminate invasive biopsy procedures.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
IRJET - Histogram Analysis for Melanoma Discrimination in Real Time ImageIRJET Journal
The document presents a novel framework for recognizing melanoma in real-time skin images using k-means clustering and support vector machine algorithms. It discusses the challenges of automated melanoma recognition due to variations in melanoma appearance and similarities to non-melanoma lesions. A two-stage approach is proposed involving lesion segmentation followed by classification using deep learning networks to extract discriminative features for accurate melanoma recognition.
IRJET- Detection & Classification of Melanoma Skin CancerIRJET Journal
This document discusses methods for detecting and classifying melanoma skin cancer. It begins with an introduction to skin cancer and the importance of detecting melanoma early. It then reviews literature on existing techniques for melanoma detection using image processing and machine learning. The proposed system uses image segmentation, feature extraction using the ABCD criteria, principal component analysis to select key features, and support vector machine classification to determine whether images contain cancerous or non-cancerous lesions. The system aims to provide an accurate and fast evaluation of skin lesions to help in melanoma diagnosis.
IRJET -Malignancy Detection using Pattern Recognition and ANNSIRJET Journal
This document discusses using pattern recognition and artificial neural networks (ANNs) to detect malignancy, specifically melanoma skin cancer. It describes preprocessing dermoscopy images to remove noise, then implementing an ANN with 12 neurons in each layer to classify images as cancerous or non-cancerous based on 12 selected features. After training the ANN, it is tested on new data for decision making. The method provides efficient classification compared to alternative gradient descent approaches that may result in incorrect predictions. Publicly available skin cancer data is used to train and validate the ANN model.
IRJET- Analysis of Skin Cancer using ABCD TechniqueIRJET Journal
This document describes a proposed method for analyzing skin cancer using the ABCD technique. It begins with an introduction to skin cancer and melanoma. The proposed method involves preprocessing the skin lesion image using filters to reduce noise, segmenting the lesion from the image, extracting features using the ABCD parameters of asymmetry, border, color, and diameter, and then identifying malignant melanoma based on the feature analysis. If melanoma is detected early using this technique, it could help reduce healthcare costs by lowering the need for biopsies. The method aims to accurately detect melanoma for early treatment when survival rates are highest.
Detection of Skin Cancer Based on Skin Lesion Images UsingDeep LearningIRJET Journal
This document discusses skin cancer detection using deep learning techniques. It begins with an introduction to skin cancer and the need for early detection. It then reviews the existing methods for skin cancer detection which rely on visual examination by dermatologists. The proposed method uses a deep learning model trained on skin lesion images to classify lesions as benign or malignant. The methodology section describes the image acquisition, preprocessing including enhancement, data augmentation, and preparation steps. It then discusses training a convolutional neural network for classification. Experimental results show the system can accurately detect different types of skin cancers like basal cell carcinoma and keratosis. The conclusion discusses benefits of developing such a system for integrated use on smartphones to enable low-cost cancer screening.
Segmentation and Classification of Skin Lesions Based on Texture FeaturesIJERA Editor
Skin cancer is the most common type of cancer and represents 50% all new cancers detected each year. The deadliest form of skin cancer is melanoma and its incidence has been rising at a rate of 3% per year. Due to the costs for dermatologists to monitor every patient, there is a need for an computerized system to evaluate a patient‘s risk of melanoma using images of their skin lesions captured using a standard digital camera. In Proposed method, a novel texture-based skin lesion segmentation algorithm is used and to classify the stages of skin cancer using probabilistic neural network. Probabilistic neural network will give better performance in this system to detect a lot of stages in skin lesion. To extract the characteristics from various skin lesions and its united features gives better classification with new approached probabilistic neural network. There are five different skin lesions commonly grouped as Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Melanocytic Nevus / Mole (ML), Squamous Cell Carcinoma (SCC), Seborrhoeic Keratosis (SK). The system will be used to classify the queried images automatically to decide the stages of abnormality. The lesion diagnosis system involves two stages of process such as training and classification. Feature selection is used in the classified framework that chooses the most relevant feature subsets at each node of the hierarchy. An automatic classifier will be used for classification based on learning with some training samples of each stage. The accuracy of the proposed neural scheme is higher in discriminating cancer and pre-malignant lesions from benign skin lesions, and it attains an total classification accuracy is high of skin lesions.
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Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
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