853 resources related to Malignant tumors
- Topics related to Malignant tumors
- IEEE Organizations related to Malignant tumors
- Conferences related to Malignant tumors
- Periodicals related to Malignant tumors
- Most published Xplore authors for Malignant tumors
2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting
The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science
The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
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 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 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.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
This conference provides an exchange of technical topics in the fields of Solid State Modulators and Switches, Breakdown and Insulation, Compact Pulsed Power Systems, High Voltage Design, High Power Microwaves, Biological Applications, Analytical Methods and Modeling, and Accelerators.
IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.
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; ...
Electrical insulation common to the design and construction of components and equipment for use in electric and electronic circuits and distribution systems at all frequencies.
The Canadian Journal of Electrical and Computer Engineering, issued quarterly, has been publishing high-quality refereed scientific papers in all areas of electrical and computer engineering since 1976. Sponsored by IEEE Canada (The Institute of Electrical and Electronics Engineers, Inc., Canada) as a part of its role to provide scientific and professional activity for its members in Canada, the CJECE complements ...
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
Digital Dermoscopy is a tool commonly used by dermatologists for assisting the diagnosis of skin lesions. The presence of hair in such dermoscopic images frequently occludes significant diagnostic information and reduces their value. In this work we propose algorithms that successfully identify and remove hair from the dermoscopic images. The proposed algorithms consist of two parts; the first deals with ...
2016 International Conference on Computer Communication and Informatics (ICCCI), 2016
This paper focuses on the finding, segmentation, categorization and removal of skin lesion as a literature survey. Melanoma is a category of cancer that develop from the pigment-network cells renowned as melanocytes. Melanomas usually develop in the skin other than may arise in the maw, backbone or ogle. This paper addresses two different systems for finding of fur evil in ...
IEEE Spectrum, 2002
Breast cancer accounts for nearly one of every three cancers diagnosed in US women. While great strides have been made in early detection, the conventional method of mammography is not failproof it has trouble imaging dense tissue, it may show suspicious areas where no malignancy exists, and radiologists interpreting the images can miss up to 15 percent of cancers. It's ...
Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ, 1993
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. ...
2011 IEEE Medal for Innovations in Healthcare Technology - Harrison H. Barrett
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 4 of 7 - MRI at 130 Microtesla
IEEE Magnetics 2014 Distinguished Lectures - Tim St Pierre
ASC-2014 SQUIDs 50th Anniversary: 2 of 6 - John Clarke - The Ubiquitous SQUID
Digital Dermoscopy is a tool commonly used by dermatologists for assisting the diagnosis of skin lesions. The presence of hair in such dermoscopic images frequently occludes significant diagnostic information and reduces their value. In this work we propose algorithms that successfully identify and remove hair from the dermoscopic images. The proposed algorithms consist of two parts; the first deals with the identification of hair, while the second part concerns the image restoration using interpolation. For the evaluation of the algorithms we used ground truth images with synthetic hair and compared the results with the commonly used in the literature DullRazor tool. According to the experimental results the proposed hair removal algorithms can be used successfully in the detection and removal of both dark and light colored hair.
This paper focuses on the finding, segmentation, categorization and removal of skin lesion as a literature survey. Melanoma is a category of cancer that develop from the pigment-network cells renowned as melanocytes. Melanomas usually develop in the skin other than may arise in the maw, backbone or ogle. This paper addresses two different systems for finding of fur evil in dermoscopy images. The first system uses global features and the second system uses local methods and the classifier. Therefore, melanoma is simply to identify with help of global features and local methods.
Breast cancer accounts for nearly one of every three cancers diagnosed in US women. While great strides have been made in early detection, the conventional method of mammography is not failproof it has trouble imaging dense tissue, it may show suspicious areas where no malignancy exists, and radiologists interpreting the images can miss up to 15 percent of cancers. It's also uncomfortable, requiring each breast to be compressed between plastic plates, which can lead to bruising. Susan Hagness wants to change all that. An assistant professor of electrical engineering at the University of Wisconsin- Madison, she is pioneering a novel detection technique that uses ultrawideband microwaves to image even the tiniest malignant tumors in the breast. Breast tumors and normal tissue show much more contrast at microwave frequencies than at the X-ray frequencies used for mammograms. Microwaves are also nonionizing, and the technique requires no breast compression. In Susan Hagness' search for a better way to detect breast cancer, she gets her students involved, too. Such efforts have paid off: her courses consistently receive high marks on student evaluations.
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.
Dermoscopy is one of the major imaging techniques used in diagnoses of Melanoma and other skin diseases. Because of difficulties and subjectivity of human interpretation, automatic and computerized analysis of dermoscopic images has opened an important research area. Skin lesion detection is as the first step in this analysis. Finding an optimal threshold for segmenting the lesion is a severe task in image processing. Different methods for thresholding already exist. In this work, we use a combination of well-known thresholding methods and fuse them by Sarsa Reinforcement algorithm which leads to a reinforced threshold. The reinforced agent learns optimal weights for different thresholding methods and finally segments the dermoscopic image with optimal threshold. A reward function is designed for achieving the similarity ratio between the binary output image and original gray level image and calculating reward/punish signal which should be exerted to reinforced agent. We use three thresholding methods for combination in the reinforced agent and the detected lesions are compared with the ground-truth which is determined by three different dermatologists.
We formulate a new multi-task graph classification (MTG) problem, where multiple graph classification tasks are jointly regularized to find discriminative subgraphs shared by all tasks for learning. More details can be found in .
This paper presents a segmentation approach of melanoma by color morphology. The images of melanoma are filtered by morphological tools using a lexicographic order onto HSI color space. A thresholding technique is applied to segment the melanoma Region of Interest (ROI). Binary morphological techniques are used to filter the ROI. The approach was tested on two benign and malignant image databases, both containing 100 images, and the results were compared to ground-truth segmentation and Fuzzy CMeans one. By performing twelve metrics, the results have shown the promising aspects of this approach to segment benign and malignant lesions.
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%.
X-ray projection is not effective for representing complex overlapping objects. This paper presents a novel computational framework to decompose X-ray projections into multiple images with non-overlapping objects that are differentiated by their own material compositions. Based on energy-dependent X-ray attenuation characteristics for each material, multiple energy X-ray images are analyzed to obtain material-selective images, which correspond to projections of basis materials that constitute objects. We show that material- selective images can be considered as linear mixtures of independent components that are associated with object-selective images. As a result, multiple objects can be decomposed by independent component analysis (ICA) of material-selective images or ICA of multiple monochromatic energy X-ray images. To demonstrate the concept of the proposed method, we apply it to simulated images based on a 3-D human model.
No standards are currently tagged "Malignant tumors"