Computer aided diagnosis
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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.
ISIE focuses on advancements in knowledge, new methods, and technologies relevant to industrial electronics, along with their applications and future developments.
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.
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 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.
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.
Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.
IEEE Design & Test of Computers offers original works describing the methods used to design and test electronic product hardware and supportive software. The magazine focuses on current and near-future practice, and includes tutorials, how-to articles, and real-world case studies. Topics include IC/module design, low-power design, electronic design automation, design/test verification, practical technology, and standards. IEEE Design & Test of ...
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.
Proceedings Sixth International Conference on Information Visualisation, 2002
The use of virtual reality (VR) techniques for computer-aided diagnosis is revolutionizing the medical routing in various medical disciplines. Especially in the field of dentistry VR in combination with CAD/CAM technologies offers a high potential for implant planning/design resulting in a higher accuracy and a shorter therapy time period. The geometry of the teeth is recognisable and digitisable using an ...
19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006
In this paper, a liver disease diagnosis based on Gabor filters is proposed. Three kinds of liver diseases are identified: cyst, hepatoma and cavernous hemangioma. The diagnosis scheme includes two steps: features extraction and classification. The features derived from Gabor filters are obtained from the ROIs among the normal and abnormal CT images. In the classification step the SVM classifier ...
7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002., 2002
Extracting features from the colonoscopic images is essential for getting the quantitative parameters, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. Present methods mostly use manual approaches. A novel scheme is developed to extract new texture-based quantitative features from the texture ...
Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2000
We have reported that a prototype computer-aided diagnosis (CAD) system to automatically detect suspicious regions from chest CT images had been presented by our group, and the CT screening system used was a TCT-900 super helix of the Toshiba Corporation. In this paper, we present a new and automatic method for an early diagnosis of lung cancer based on a ...
2009 2nd IEEE International Conference on Computer Science and Information Technology, 2009
In the last decades, medical exams became a regular act; thus, the amount of mammograms interpreted by a radiologist increases dramatically. A focused effort is under way to develop a Computer-Aided Diagnosis of Mammograms (CADM) able to store, process and interpret data related to a mammogram. An important component is dedicated to reduce the haziness of interpretation; this will be ...
Renco Electronics: Renco Everywhere
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems
Photo Verification Technology for Radiology Images - Srini Tridandapani - IEEE EMBS at NIH, 2019
Honors 2020: Joseph R. Guerci Wins the IEEE Dennis J. Picard Medal for Radar Technologies and Applications
VisualDx Augmented Intelligence Project - Arthur Papier - IEEE EMBS at NIH, 2019
ASC-2014 SQUIDs 50th Anniversary: 4 of 6 - Keiji Enpuku
Engineering in Medicine and Biology: Segment 3
Panel: Unmet Needs in HIV/AIDS & TB Diagnosis & Management - IEEE EMBS at NIH, 2019
Learning Method of the SIC Fuzzy Inference Model - Genki Ohashi - ICRC San Mateo, 2019
Computer-Assisted Audiovisual Language Learning
LPIRC: Developing Mobile Computer Vision Models
Niklaus Wirth on Teaching Computer Science
Molecular Diagnostics for STIs - Gary Schoolnik - IEEE EMBS at NIH, 2019
Computing Conversations: Mitchell Baker on the Mozilla Foundation
Machine Ethics - Proceedings of the IEEE Webinar
Unmet Needs in the Diagnosis and Management of TB - Richard Chaisson - IEEE EMBS at NIH, 2019
Implantable Wireless Medical Devices and Systems
Computing Conversations: Van Jacobson on Content-Centric Networking
Computing Conversations: Bertrand Meyer: Eiffel Programming Language
The use of virtual reality (VR) techniques for computer-aided diagnosis is revolutionizing the medical routing in various medical disciplines. Especially in the field of dentistry VR in combination with CAD/CAM technologies offers a high potential for implant planning/design resulting in a higher accuracy and a shorter therapy time period. The geometry of the teeth is recognisable and digitisable using an intraoral camera, the implant models can be designed with specialized CAD systems and the final implants are generated using a CNC machine. In this context the "Virtual Articulator" (VA) completes the digital manufacturing pipeline and offers the possibility to consider patient-specific jaw biomechanics in the implant generation process. The objective of this project is the development of a system for dental occlusion diagnosis. For this important diagnosis in today's dental routine the mechanical articulator is used. The VA simulates and augments the functionalities of the mechanical articulator. Therefore it takes a digital 3-D representation of the jaws as input data, generates a simulation of the jaw movements, and delivers a dynamical visualization of the occlusion points. The high technical demands to the system are given through the high accuracy necessary for dental applications and the large models that have to be handled in the system.
In this paper, a liver disease diagnosis based on Gabor filters is proposed. Three kinds of liver diseases are identified: cyst, hepatoma and cavernous hemangioma. The diagnosis scheme includes two steps: features extraction and classification. The features derived from Gabor filters are obtained from the ROIs among the normal and abnormal CT images. In the classification step the SVM classifier is used to discriminate the different liver disease types. Finally the receiver operating characteristic curve is employed to evaluate the performance of the diagnosis system. The effectiveness of the proposed method is demonstrated through experimental results on CT images including 76 liver cysts, 30 hepatomas, and 40 cavernous hemangiomas. From the results, we can observe that the discrimination rate of cyst is higher than the other diseases, and the classification accuracy decreases slightly between cavernous hemangiomas and hepatomas. However, a normal region can be discriminated from all of these diseases entirely
Extracting features from the colonoscopic images is essential for getting the quantitative parameters, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. Present methods mostly use manual approaches. A novel scheme is developed to extract new texture-based quantitative features from the texture spectra in the chromatic and achromatic domains of colonoscopic images. The texture spectra are obtained from the texture unit numbers, which contain local and global texture information of the image. These features are evaluated using supervisory Backpropagation Neural Network (BPNN) with various training algorithms, viz., resilient propagation (RPROP), scaled conjugate gradient (SCG), and Marquardt algorithms. The evaluation is based on training time, training epoch, and accuracy on classifying the colon status. The preliminary results obtained by the proposed approach support the feasibility of the technique.
We have reported that a prototype computer-aided diagnosis (CAD) system to automatically detect suspicious regions from chest CT images had been presented by our group, and the CT screening system used was a TCT-900 super helix of the Toshiba Corporation. In this paper, we present a new and automatic method for an early diagnosis of lung cancer based on a CAD system in which all the CT images are read. Furthermore, the CAD system is equipped with functions to automatically detect suspicious regions from chest CT images, and to assist the comparative reading in retrospect. The key processes of the CAD system are a slice matching algorithm for comparison of each slice image of the present and past CT scans, and an interface to display some features of the suspicious regions. The experimental results indicate that our CAD system can work effectively.
In the last decades, medical exams became a regular act; thus, the amount of mammograms interpreted by a radiologist increases dramatically. A focused effort is under way to develop a Computer-Aided Diagnosis of Mammograms (CADM) able to store, process and interpret data related to a mammogram. An important component is dedicated to reduce the haziness of interpretation; this will be done by tracing Focus of Attention Regions (FARs), which can have tumors, abnormalities, so. The goal is to underline these FAR zones, instead of paying attention to the whole image, accomplishing two major objectives: on one side, reducing the volume of processed data, and on the other side, reducing the possibility of false detection. In this paper two way of detecting FARs will be investigated. Both are provided by fractal geometry, a field which has already proved its utility in medical imaging. Natural shapes can be characterized through fractal characteristics; therefore fractal analysis techniques represent an efficiently working instrument which allows automation of analysis procedures of medical images. Experiments were done on 20 cases of mammograms; the results show that fractal techniques may be used to reduce the haziness in medical imaging.
This paper presents a content-based medical image retrieval (CBIR) method that used in medical CT images of liver lesions with a computer-assisted diagnosis. According to medical CT images characteristics of blurred boundaries and the unconspicuous region, the liver region of interest is extracted by using semi- automatic method. We extract local co-occurrence matrix texture features and intensity features, and use improved non-tensor product wavelet filter to extract the image global features. Experimental results show that this method can improve the detection rate of lesions. It obtains good results in hepatic hemangioma and HCC which are difficult differential diagnosis both of rich blood supply to tumors.
Various statistical parameters have been tried for the computer-aided diagnosis of the liver fibrosis. The region of interest (ROI) for the liver and spleen parenchymas have been chosen, and the hepatolienal textural contrast for each ultrasound (US) image has been examined. The selectively chosen textural parameters are linearly combined with the pre-determined coefficients to give the computer-aided diagnostic parameter for the liver fibrosis, whose final stage is named as cirrhosis. From the comparison with the clinical diagnosis it is suggested that the proposed calculation scheme using the textural parameters show the quite promising classification performance for the computer-aided diagnosis of the liver cirrhosis.
The fast detection of pneumoconiosis-affected parts using neural network is presented. The rounded opacities on the pneumoconiosis X-ray photo are picked up quickly through a backpropagation (BP) neural network with several typical training patterns. Training patterns from 0.6 mmO to 4.0 mmO are made as simple circles. The neck problem for automatic pneumoconiosis diagnosis is to reject unnecessary parts like ribs and blood vessel shadows. In this paper, such unnecessary parts are rejected well by a special technique called "moving normalization". The input of the neural network is a 30/spl times/30 pixel image which is quarried successively from a bi-level ROI (region of interest) image with a size of 500/spl times/500 pixels. The moving normalization technique has been developed in order to make an appropriate bi-level ROI image. A complete evaluation is carried out using a size and figure categorization. Many simulation examples show that the proposed method gives much more reliable results than traditional methods do.
The computerized and sophisticated version of medical atlases aims at constituting iconographic bases and at developing "intelligent" tools, thus allowing image retrieval according to several modalities. Such atlases are consequently used as assistance tools for physicians, at the same time for medical diagnosis, formation and research. Therefore, the study described is directed at upper digestive endoscopy and its imagery with the aim of conceiving a computer-assisted diagnosis system. It will be articulated around a base of images on which will be supported case-based reasoning), adapted to elucidate a case-in fact, to find the similar ones to it. Moreover, a knowledge base describing endoscopic pathologies is destined to take place of a control unit of coherence for similarities search.
Describes a computer assisted diagnosis system of lung cancer that detects early stage candidate tumors from helical X-ray CT images with precise measurement conditions. Recently, helical X-ray CT images have been used for the mass screening process as a tool for diagnosis, but considering the number of images to be checked, the checking time is too long. Here the authors develop a diagnosis system that can detect the candidate area for the lung cancer's tumor in its early stage. The authors' diagnostic algorithm is based on image processing techniques and medical knowledge. From results of the application to patients of lung cancer, the authors present the effectiveness of their algorithm.<<ETX>>
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