345 resources related to Skin cancer
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IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.
The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.
The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore
Science, technology and applications spanning the millimeter-waves, terahertz and infrared spectral regions
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.
Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.
Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N, 1999
Multi-spectral transillumination (MST) imaging facilitates the diagnosis and classification of melanoma by giving crucial information about the depth of invasion. The combined analysis of MST images can give information regarding the cell growth pattern within the lesion. In this study, an optical imaging device called the Nevoscope is used for the collection of MST images of skin and skin lesions. ...
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
Prostate cancer is the most common cancer among men, excluding skin cancer. It is diagnosed by histopathology interpretation of Hematoxylin and Eosin (H&E)-stained tissue sections. Gland and nuclei distributions vary with the disease grade, and the morphological features vary with the advance of cancer. A tissue microarray with known disease stages can be used to enable efficient pathology slide image ...
2016 1st India International Conference on Information Processing (IICIP), 2016
Skin cancers particularly malignant melanoma is lethal and difficult to identify in last stages. The variation of stages (Squamous Cell Carcinoma, Actinic Keratosis Cell, Basal cell and Malignant Melanoma) of Skin Cancer is highly ambiguous and difficult to recognize. To clearly recognize the stage of Skin Cancer is primarily important for effective treatment, which cause in increasing the survival rate ...
2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008
The identification of the state of human skin tissues is discussed here. The bio-optical signals recorded in vitro have been analyzed by extracting various statistical features. Using LAB VIEW 7.1 programs/tools, different statistical features are extracted from both normal and pathology spectra. Each spectrum is filttered and normalized. Then different features like skewness, summation, median residuals, power spectral density, etc. ...
2007 IEEE Ultrasonics Symposium Proceedings, 2007
We have developed novel ultrasound (US) and optical non-invasive methods suitable for use in combination, to determine whether a hybrid multivariate approach may eventually be able to provide an objective aid to skin cancer diagnosis in primary care. A high frequency 3D US scanner was modified for C-scan, surface echo tracking and reflex transmission imaging. The images were co-registered with ...
IEEE 125th Anniversary Media Event: Cancer Prediction
IEEE Highlight: Electronic Nose: Diagnosing Cancer Through Smell
Big Data and Machine Learning in Cancer Genomics
Implantable, Insertable and Wearable Micro-optical Devices for Early Detection of Cancer - Plenary Speaker, Christopher Contag - IPC 2018
Applying Control Theory to the Design of Cancer Therapy
IMS 2015: Inkjet-Printed Nanotechnology-Enabled Zero-Power Wireless Sensor Nodes for Smart Skin Applications
Perpendicular magnetic anisotropy: From ultralow power spintronics to cancer therapy
Dean Kamen's Artificial Arm
A Manhattan Project for the Prosthetic Arms Race
Surgeons Got Game
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 5 of 7 - SQUID Instrumentation for Early Cancer Diagnostics
EMBC 2011 -Keynote (Women in Engineering Program) Re-engineering the War on Cancer: A Call to Action for Personalized Medicine -Mara G. Aspinall
Feeding the Machine: The World's Most Sophisticated Artificial Stomach
Life Sciences Grand Challenge Conference - Phillip A. Sharp
Tapping the Computing Power of the Unconscious Brain
IMS 2015: Panel Session: Wearable Electronics - Fad or Future?
Q&A with Dr. May Wang: IEEE Big Data Podcast, Episode 9
Magnetic Nanowires: Revolutionizing Hard Drives, RAM, and Cancer Treatment
Playing Guitar Hero Without Hands
Multi-spectral transillumination (MST) imaging facilitates the diagnosis and classification of melanoma by giving crucial information about the depth of invasion. The combined analysis of MST images can give information regarding the cell growth pattern within the lesion. In this study, an optical imaging device called the Nevoscope is used for the collection of MST images of skin and skin lesions. Skin is an inhomogeneous, multilayer tissue. Due to its inhomogeneity on the microscopic level, light entering the skin can be absorbed or scattered or can be reflected at the layer boundaries. The light remitted from the skin is collected by the Nevoscope to produce the MST images.
Prostate cancer is the most common cancer among men, excluding skin cancer. It is diagnosed by histopathology interpretation of Hematoxylin and Eosin (H&E)-stained tissue sections. Gland and nuclei distributions vary with the disease grade, and the morphological features vary with the advance of cancer. A tissue microarray with known disease stages can be used to enable efficient pathology slide image analysis. We focus on an intuitive approach for segmenting such images, using the Hierarchical Self-Organizing Map (HSOM). Our approach introduces the use of unsupervised clustering using both color and texture features, and the use of unsupervised color merging outside of the HSOM framework. The HSOM was applied to segment 109 tissues composed of four tissue clusters: glands, epithelia, stroma, and nuclei. These segmentations were compared with the results of an EM Gaussian clustering algorithm. The proposed method confirms that the self-learning ability and adaptability of the HSOM, coupled with the information fusion mechanism of the hierarchical network, leads to superior segmentation results for tissue images.
Skin cancers particularly malignant melanoma is lethal and difficult to identify in last stages. The variation of stages (Squamous Cell Carcinoma, Actinic Keratosis Cell, Basal cell and Malignant Melanoma) of Skin Cancer is highly ambiguous and difficult to recognize. To clearly recognize the stage of Skin Cancer is primarily important for effective treatment, which cause in increasing the survival rate from Skin cancer. In this work, we propose a methodology to reduce the probability of false diagnosis. In the proposed methodology, the data set is first preprocessed using K-mean Clustering algorithm. This preprocessing helps to increase the rate of reorganization by removing all irrelevant texture. The preprocessed data is then used to extract the features. The classification results illustrate that the proposed method can considerably improve in classification of Skin cancer disease. The computed accuracy of classification for this algorithm is achieved up to 94.4%.
The identification of the state of human skin tissues is discussed here. The bio-optical signals recorded in vitro have been analyzed by extracting various statistical features. Using LAB VIEW 7.1 programs/tools, different statistical features are extracted from both normal and pathology spectra. Each spectrum is filttered and normalized. Then different features like skewness, summation, median residuals, power spectral density, etc. were extracted. The values of the feature vector reveal information regarding tissue state. The values of the feature vector reveal information regarding tissue state. These parameters have been analyzed for discrimination between normal and pathology conditions. For analysis, a specific data set has been considered. Further discrimination between normal and pathology spectra is also be achieved by using MATLAB @6.1 tool based classical multilayer feed forward neural network with back propagation algorithm.
We have developed novel ultrasound (US) and optical non-invasive methods suitable for use in combination, to determine whether a hybrid multivariate approach may eventually be able to provide an objective aid to skin cancer diagnosis in primary care. A high frequency 3D US scanner was modified for C-scan, surface echo tracking and reflex transmission imaging. The images were co-registered with colour photographs and with images from a purpose-built spectrophotometric camera. The US system was used to scan 87 suspicious pigmented skin lesions referred from primary care. Nine were also examined using the imaging spectrophotometer. US regions of interest were drawn using the pigmented lesion boundaries on co- registered photographs, and numerical US characteristics were extracted for each lesion, providing a relative measure of US surface reflectance, intra-lesional US reflectance, total US attenuation, and the relative homogeneity of each characteristics. Quantitative differences between melanoma, seborrheic keratosis and benign naevi provided discrimination that was sufficient to potentially reduce the referral of benign tumours by 65% without missing melanoma. The extent and nature of the visual correspondence between optical spectral images of various wavelength and US C-scans at various depths varied with pigmented lesion type so as to suggest that there may indeed be value in combining spectrophotometric and US imaging in pigmented lesion diagnosis.
We investigated Bayesian network structure learning and probability estimation from mammographic feature data in order to classify breast lesions into different pathological categories. We compared the learned networks to naive Bayes classifiers, which are similar to the expert systems previously investigated for breast lesion classification. The learned network structures reflect the difference in the classification of biopsy outcome and the invasiveness of malignant lesions for breast masses and microcalcifications. The difference between masses and microcalcifications should be taken into consideration when interpreting systems for automatic pathological classification of breast lesions. The difference may also affect use of these systems for tasks such as estimating the sampling error of biopsy.
Digital image processing is a combination of various algorithms and technique to process different types of images. It is applied in various types of image to process and get a valuable outcome from the image. The Digital image processing is the experimented on image to extract different features of the image. This paper provides the idea which is used to detect the affected area of the Vitiligo disease with help of image captured by camera and classified the affected area from non-affected area in image. Vitiligo is the deep rooted skin disease which is depigmentation of the skin in which human skin starts losing or loss of pigment from the skin. The certain portion of the skin of body became white patches. The Vitiligo is visible in dark skin persons because of some genetic problem or environmental issues. Here, the learning vector quantization neural network is used to classify Vitiligo image in affected vs. non-affected region to detect disease. The implementation of LVQ neural network gives very good accuracy of 92.22% and kappa value of 0.810 which is very good for proposed technique.
Summary form only given. The prolate-spheroidal impulse radiating antenna (PSIRA) is used to focus a fast (100 ps), high voltage (> 100 kV) pulse launched from the first focal point onto targets located at the second focal point. These fast electromagnetic pulses with high amplitude are used in various biological applications such as skin cancer treatment and gene insertion. For these fast, high voltage pulses a switch system is desired to radiate spherical TEM waves from the first focal point. The switch system consists of the switch cones, gas (hydrogen) chamber and the pressure vessel. We have investigated various switch cone geometries which deviate from previous designs used in the prototype IRA system. Impedance considerations play an important role in the design of such a switch system. The use of 100 ps pulses also presents the interesting possibility of reducing the feed arms from the PSIRA. The waves radiated from the switch/source and the focal impulse waveforms for each design are explored using numerical simulations. The hydrogen chamber and pressure vessel are required to avoid dielectric breakdown when the switch is subjected to input voltages of greater than 100 kV. The design of the pressure vessel involves optimizing the dimensions and determining suitable dielectric material so that a spherical TEM wave emanates from the first focal point. The design must also be easy to fabricate. Numerical simulations to investigate the pressure vessel design are presented in this paper. The possibility of using the pressure vessel as a lens is also explored.
We developed a biophysical Raman model of human skin and validated it using in vivo clinical screening data. Key biophysical changes were used for fast and accurate diagnosis of melanoma and nonmelanoma skin cancer.
A Bluetooth-enabled handheld device, which is the size of a cell phone, is being developed to assist medical practitioners in the analysis of skin lesions and accurate detection of early melanoma. The device can capture images of the lesion, process them in real-time, and indicate the probability that the lesion is malignant. A desktop-based system has been developed that implements skin-lesion analysis automatically with high degree of accuracy. In this paper, we present a simple prototype, based on the Texas Instruments TMS320DM642 Video/Imaging fixed-point digital signal processor, which implements a few basic functions of the desktop system, including image capture, segmentation, and display of the extracted lesion.
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