Conferences related to Tumors

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2020 IEEE International Power Modulator and High Voltage Conference (IPMHVC)

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.


2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)

The world's premiere conference in MEMS sensors, actuators and integrated micro and nano systems welcomes you to attend this four-day event showcasing major technological, scientific and commercial breakthroughs in mechanical, optical, chemical and biological devices and systems using micro and nanotechnology.The major areas of activity in the development of Transducers solicited and expected at this conference include but are not limited to: Bio, Medical, Chemical, and Micro Total Analysis Systems Fabrication and Packaging Mechanical and Physical Sensors Materials and Characterization Design, Simulation and Theory Actuators Optical MEMS RF MEMS Nanotechnology Energy and Power


2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

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


2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)

Science, technology and applications spanning the millimeter-waves, terahertz and infrared spectral regions


2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

The conference is the primary forum for cross-industry and multidisciplinary research in automation. Its goal is to provide a broad coverage and dissemination of foundational research in automation among researchers, academics, and practitioners.


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Periodicals related to Tumors

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Antennas and Propagation, IEEE Transactions on

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.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Biomedical Engineering, IEEE Transactions on

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.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

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; ...


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Most published Xplore authors for Tumors

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Xplore Articles related to Tumors

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Evaluation of surface roughness of tumor using neural network

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

Since surface roughness of a malignant tumor is more remarkable than that of a benign tumor, it is possible to classify pathological states of the tumor by computing the degree of surface roughness. We have proposed a method for the segmentation of the tumor in ultrasonic echography and confirmed the feasibility of the technique. This paper describes a neural network ...


Shape based classification of breast tumors using fractal analysis

2009 International Multimedia, Signal Processing and Communication Technologies, 2009

In this paper we adopt FFT based fractal analysis method. With the help FFT based method breast tumors (benign and malignant) are classified based on their shapes. In general malignant tumors contour have rough and irregular shapes whereas benign contour have smooth and macrolobulated shapes. In this paper we present a fractal based Fourier transform method to classify the contours. ...


Shape and Boundary Analysis for Classification of Breast Masses

2008 International Symposium on Computational Intelligence and Design, 2008

Malignant breast tumors appear spiculate or microlobulate in the boundary and irregular in shape. But benign breast masses appear smooth in the boundary and round in shape. We used polygonal modeling to draw Index of spiculation(SI), index of lobule(IF), measure of fractal dimension (FD) and measure of circularity (C) to represent the characteristic of the boundary and the shape of ...


Spiculation-preserving Polygonal Modeling of Contours of Breast Tumors

2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

Malignant breast tumors typically appear in mammograms with rough, spiculated, or microlobulated contours, whereas most benign masses have smooth, round, oval, or macrolobulated contours. Several studies have shown that shape factors that incorporate differences as above can provide high accuracies in distinguishing between malignant tumors and benign masses based upon their contours only. However, global measures of roughness, such as ...


Mammogram tumor classification using multimodal features and Genetic Algorithm

2009 International Conference on Control, Automation, Communication and Energy Conservation, 2009

This paper proposes a computer aided decision support system for an automated diagnosis and classification of breast tumor using mammogram. The proposed method differentiates two breast diseases namely benign masses and malignant tumors. From the preprocessed mammogram image, texture and shape features are extracted. The optimal features can be extracted by using a feature selection scheme based on the multi ...


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Educational Resources on Tumors

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IEEE-USA E-Books

  • Evaluation of surface roughness of tumor using neural network

    Since surface roughness of a malignant tumor is more remarkable than that of a benign tumor, it is possible to classify pathological states of the tumor by computing the degree of surface roughness. We have proposed a method for the segmentation of the tumor in ultrasonic echography and confirmed the feasibility of the technique. This paper describes a neural network based classifier using the surface roughness of a breast tumor which is extracted from ultrasonic echography. We define nine parameters for evaluation of the surface roughness, which form an artificial neural network (ANN) input vector. The ANN output sequence displays two types of pathological states; malignant and benign. We use twenty seven benign tumors and twenty four malignant tumors for the feasibility study. Twenty four tumors are used for learning data and the other tumors are used for the trial. As a result, successful classification is obtained.

  • Shape based classification of breast tumors using fractal analysis

    In this paper we adopt FFT based fractal analysis method. With the help FFT based method breast tumors (benign and malignant) are classified based on their shapes. In general malignant tumors contour have rough and irregular shapes whereas benign contour have smooth and macrolobulated shapes. In this paper we present a fractal based Fourier transform method to classify the contours. The log -log magnitude frequency response plot and rose plot (for average radial distance and angle variations in terms of average slops and slope of intercept) are used in this study in order to extract the features for classifications.

  • Shape and Boundary Analysis for Classification of Breast Masses

    Malignant breast tumors appear spiculate or microlobulate in the boundary and irregular in shape. But benign breast masses appear smooth in the boundary and round in shape. We used polygonal modeling to draw Index of spiculation(SI), index of lobule(IF), measure of fractal dimension (FD) and measure of circularity (C) to represent the characteristic of the boundary and the shape of breast masses. The boundary of the mass is divided into three type: 1. spiculate; 2. microlobulate; 3.smooth.The shape of the mass is divided into two types: 1. irregular; 2. sub-circular. Considering the boundary and the shape style the masses can be divided into malignant ones and benign ones. The test is based on a dataset of 93 images from MIAS with 54 benign masses and 39 malignant tumors. The accuracy of the classification reach 0.9265 in terms of the area(A<sub>z</sub>) under the ROC curve.

  • Spiculation-preserving Polygonal Modeling of Contours of Breast Tumors

    Malignant breast tumors typically appear in mammograms with rough, spiculated, or microlobulated contours, whereas most benign masses have smooth, round, oval, or macrolobulated contours. Several studies have shown that shape factors that incorporate differences as above can provide high accuracies in distinguishing between malignant tumors and benign masses based upon their contours only. However, global measures of roughness, such as compactness, are less effective than specially designed features based upon spicularity and concavity. We propose a method to derive polygonal models of contours that preserve spicules and details of diagnostic importance. We show that an index of spiculation derived from the turning functions of the polygonal models obtained by the proposed method yields better classification accuracy than a similar measure derived using a previously published method. The methods were tested with a set of 111 contours of 65 benign masses and 46 malignant tumors. A high classification accuracy of 0.93 in terms of the area under the receiver operating characteristics curve was obtained

  • Mammogram tumor classification using multimodal features and Genetic Algorithm

    This paper proposes a computer aided decision support system for an automated diagnosis and classification of breast tumor using mammogram. The proposed method differentiates two breast diseases namely benign masses and malignant tumors. From the preprocessed mammogram image, texture and shape features are extracted. The optimal features can be extracted by using a feature selection scheme based on the multi objectives genetic algorithm (MOGA).The performance of the proposed method is compared with the previous methods in terms of classification accuracy, training and testing time. Simulation results show that, this approach is an efficient, easy to use and can achieve high sensitivity.

  • Classification of skin tumors based on shape features of nuclei

    Summary form only given. In this paper, an automated system to segment cell nuclei of dermatofibroma (DF) and dermatofibrosarcoma protuberans (DFSP) is proposed. It is difficult to segment nuclear regions accurately, because there exists a lot of ambiguous nuclei. Objective of the system is to segment nuclear regions objectively and in some accuracy. In the system, regions to be segmented are defined as regions that are surrounded by edges of certain strength. Under this restriction, arbitrary shaped nuclear regions and weakly stained nuclear regions are possible to be extracted. At first, contrast emphasis using hue is done as preprocessing. Image binarization is done by a dynamic thresholding method with implementation of Laplacian-histogram method and Otsu/spl square/fs thresholding method. At last, separation of the overlapping nuclei is carried out by watershed algorithm. To evaluate availability of this system, segmentation test was done using real tissue cell images of DF and DFSP. From some features computed from the segmented nuclear regions, an automated classification of benign tumor or malignant tumor is finally performed.

  • Polygonal Modeling of Contours of Breast Tumors With the Preservation of Spicules

    Malignant breast tumors typically appear in mammograms with rough, spiculated, or microlobulated contours, whereas most benign masses have smooth, round, oval, or macrolobulated contours. Several studies have shown that shape factors that incorporate differences as above can provide high accuracies in distinguishing between malignant tumors and benign masses based upon their contours only. However, global measures of roughness, such as compactness, are less effective than specially designed features based upon spicularity and concavity. We propose a method to derive polygonal models of contours that preserve spicules and details of diagnostic importance. We show that an index of spiculation derived from the turning functions of the polygonal models obtained by the proposed method yields better classification accuracy than a similar measure derived using a previously published method. The methods were tested with a set of 111 contours of 65 benign masses and 46 malignant tumors. A high classification accuracy of 0.94 in terms of the area under the receiver operating characteristics curve was obtained.

  • Breast tumor diagnosis system using three dimensional ultrasonic echography

    This paper describes a breast tumor diagnosis system using three dimensional ultrasonic echographic images. Since a malignant tumor is characterized by the morphological surface roughness, it is significant for the diagnosis to extract the exact boundaries of the tumor and show the three dimensional structure of the tumors. The proposed system consists of three dimensional image capturing system and a fuzzy reasoning based algorithm for the extraction of breast tumors. The method the authors proposed in this paper classifies all the voxels as one of "tumor", "normal tissue" or "boundary" by employing fuzzy reasoning and relaxation techniques. In order to evaluate the surface roughness, the authors define a parameter of a ratio of the surface area over the volume of the extracted tumor. The results for the clinical cases of three malignant and two benign tumors are successfully obtained by the system using an ultrasonic mechanical sector scanning probe of 10 MHz. It is found that the differences in the surface roughness between both types of tumors are clearly evident using a rendered surface image of the extracted tumor, and the average value of the defined parameter for the above three malignant tumors is 9.6, and 3.8 for the benign tumours.

  • Classification of breast tumors via parabolic modeling of their contours

    The discrimination between malignant tumors and benign masses in mammograms can be difficult because of the diversity of tumor shape. In order to facilitate this discrimination, we propose a method of shape analysis based only on characterization of the boundaries of the tumors via parabolic modeling. The contours used were drawn (using XPAINT) on digitized mammograms by an expert radiologist (JELD). The classification of 54 tumors as benign or malignant was achieved with an accuracy of 76 percent.

  • Breast Cancer Detection Based on Multi-Frequency EIS Measurement

    As a convenient, un-injurious and low cost method for women breast tumor diagnosis, Electrical Impedance Scanning (EIS) is being paid increasing attention. But EIS still has some problems. In theory the malignant tumor has higher electrical conductivity than normal tissue and benign tumor, so the cancer will be recognized in the EIS image as a bright spot. However, in practical the difference is not so obvious. In many cases it is difficult to judge whether a suspicious bright spot tells a malignant or benign tumor. This paper concerns a new method based on multi-frequency EIS measurement to solve this problem. A group of scanning results with various stimulating frequencies will be performed during an EIS measurement, which is automatically controlled by specially designed device and the location and pressure of the sensor will keep stable, so we will get a series of data at different stimulating frequencies through a measurement. Because the malignant and the benign tumor have different curves in electrical conductivity-frequency diagram, we can analyze the data at every frequency and find out the attribute of the tumor. Clinical trails have been carried out. Subjects were suspicious sufferers that were to receive histopathology examinations, and all analyses have been compared with histopathology results.



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