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Detection and classification of cardiac murmurs using segmentation techniques and artificial neural networks

Detection and classification of cardiac murmurs using segmen...

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ieee symposium on computational intelligence and Data Mining

作者： Strunic, S. L. Rios-Gutierrez, F. Alba-Flores, R. Nordehn, G. Burns, S. Univ Minnesota 1023 Univ Dr Duluth MN 55812 USA

ISBN: (纸本)9781424407057

In this paper we present the implementation of a diagnostic system based on Artificial Neural Networks (ANN) that can be used in the detection and classification of heart murmurs. Segmentation and alignment algorithms serve as important pre-processing steps before heart sounds are applied to the ANN structure. The system enables users to create a classifier that can be trained to detect virtually any desired target set of heart sounds. The output of the system is the classification of the sound as either normal or a type of heart murmur. The ultimate goal of this research is to implement a heart sounds diagnostic system that can be used to help physicians in the auscultation of patients and to reduce the number of unnecessary echocardiograms-- those that are ordered for healthy patients. Testing has been conducted using both simulated and recorded patient heart sounds as input. Three sets of results for the tested system are included herein, corresponding to three different target sets of simulated heart sounds. The system is able to classify with up to 85 +/- 7.4% accuracy and 95 +/- 6.8% sensitivity. For each target set, the accuracy rate of the ANN system is compared to the accuracy rate of a group of 2(nd) year medical students who were asked to classify heart sounds from the same group of heart sounds classified by the ANN system. System test results are also explored using recorded patient heart sounds.

关键词： acoustic signal detection diseases echocardiography medical diagnostic computing neural nets patient care patient diagnosis signal classification alignment algorithms artificial neural networks cardiac murmur classification cardiac murmur detection diagn Heart Sounds medical diagnostic computing acoustic signal detection Heart Murmurs patient diagnosis signal classification Neural network accuracy rate Echocardiography Patient Care diagnostic system maladjustment

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Designing a fuzzy rule based system to estimate depth of anesthesia

Designing a fuzzy rule based system to estimate depth of ane...

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ieee symposium on computational intelligence and Data Mining

作者： Esmaeili, V. Assareh, A. Shamsollahi, M. B. Moradi, M. H. Arefian, N. M. Sharif Univ Technol Sch Elect Engn Biomed Signal & Image Proc Lab Tehran Iran Amirkabir Univ Technol Fac Biol Engn Tehran Iran Shahid Beheshti Univ Dept Anesthesia Tehran Iran

ISBN: (纸本)9781424407057

Estimating the depth of anesthesia (DOA) is still a challenging area in anesthesia research. The objective of this study was to design a fuzzy rule based system which integrates electroencephalogram (EEG) features to quantitatively estimate the DOA. The proposed method is based on the analysis of single-channel EEG using frequency and time domain features as well as Shannon entropy measure. The fuzzy classifier is trained with features obtained from four subsets of data comprising well-defined anesthesia states: awake, moderate, general anesthesia, and isoelectric. The classifier extracts efficient fuzzy if-then rules and the DOA index is derived between 100 (full awake) to 0 (isoelectric) using fuzzy inference engine. To validate the proposed method, a clinical study has conducted on 22 patients to construct 4 subsets of reference states and also to compare the results with CSM monitor (Danmeter, Denmark), which has revealed satisfactory correlation with clinical assessments.

关键词： electroencephalography fuzzy systems inference mechanisms medical computing Shannon entropy measure anesthesia research depth-of-anesthesia electroencephalogram fuzzy classifier fuzzy inference engine fuzzy rule based system single-channel EEG

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Information enhancement method for image retrieval and object recognition

Information enhancement method for image retrieval and objec...

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3rd International symposium on computational Intellegence and Intelligent Informatics

作者： Rovid, A. Hashimoto, T. Varkonyi-Koczy, A. R. Shimodaira, Y. Shizuoka Univ Dept Elect & Elect Engn Oya Shizuoka 422 Japan Tech Univ Budapest & Econom Dept Measurement & Informat Syst Hungary Integrated Intelligent Syst Japanese Hungarian Lan H-1521 Budapest Hungary

ISBN: (纸本)9781424411573

Recently, the importance of information enhancement methods in digital image processing has increased significantly. A large amount of research has been focused on information retrieval and image understanding. Typical examples are searching for similar objects/images in large databases and understanding the objects in images. The main point of these tasks is to extract the most characteristic features of the objects in the images, like edges, corners, characteristic textures, etc. Another.,cry important aspect can be the separation of the "significant" and "unimportant" parts of these features, i.e. the enhancement of those features which carry primary information and to filter out the part which represents information of minor importance. By this, the complexity of the searching and/or interpreting algorithms can be decreased while the performance increased. This paper describes a new edge processing method which is able to extract the "primary", i.e. those edges which can advantageously be used in sketch based image retrieval algorithms.

关键词： information enhancement image retrieval image understanding features extraction image processing image smoothing edge extraction image contours

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An image-based measurement system for quality control in civil engineering

An image-based measurement system for quality control in civ...

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5th International symposium on image and signal processing and Analysis

作者： Reiterer, Alexander Lehmann, Martin Vienna Univ Technol Res Grp Engn Geodesy Inst Geodesy & Geophys A-1040 Vienna Austria

ISBN: (纸本)9789531841160

In the past, high-precision on-line 3D measuring required artificial targets defining the points on the objects to be monitored. For many tasks like monitoring of displacements of buildings, artificial targets are not desired. Today's image assisted measurement systems can perform their measurements even without targeting. Such systems use the texture on the surface of the object to find "interesting points" which can replace the artificial targets. However well-trained "measurement experts" are required to operate such a measurement system. In order to make such systems easy to use even for non-experts, it can be extended by an appropriate decision system which supports the operator. At the Institute of Geodesy and Geophysics of Vienna University of Technology a new kind of image-based measurement system will be developed (research project "Multi-Sensor Deformation Measurement System Supported by Knowledge-Based. and Cognitive Vision Techniques"). This system can be used for measuring, analysing and interpreting deformations for the task of quality control in civil engineering. The workflow is based on new techniques originally developed in the area of Artificial intelligence.

关键词： Quality control

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Super-resolution imaging using grid computing 07

Super-resolution imaging using grid computing

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7th ieee International symposium on Cluster Computing and the Grid

作者： Tian, Jing Ma, Kai-Kuang Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9780769528335

The super-resolution (SR) imaging is to overcome the inherent limitations of the image acquisition systems to produce high-resolution images from their low-resolution counterparts. In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully developed and shown as a promising stochastic approach for addressing the SR problem. However the MCMC SR approach requires substantial amounts of computational resources, for it not only needs to generate a huge number of samples, but also requires an exhaustive search for obtaining an optimal prior image model. To tackle the above computation challenge, Grid computing is introduced for tackling the SR problem in this paper The computationally-intensive MCMC SR task is broke down into a set of independent and small sub-tasks, which are further distributed and implemented in the Grid computing environment. Their respective results are finally assembled to produce a high-resolution image as the final result of the entire MCMC SR task. Experiments are conducted to show that Grid computing can effectively accelerating the execution time of the MCMC SR algorithm.

关键词： image resolution Grid computing Strontium High-resolution imaging Distributed computing signal processing algorithms Monte Carlo methods Assembly Biomedical imaging Medical diagnostic imaging

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Optimal decomposition level of discrete wavelet transform for pixel based fusion of multi-focused images

Optimal decomposition level of discrete wavelet transform fo...

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International Conference on computational intelligence and Multimedia Applications, ICCIMA 2007

作者： Kannan, K. Arumuga Perumal, S. Dept. of ECE Kamaraj College of Engg. and Tech. Virudhunagar Tamilnadu India Dept. of Computer Science St. Hindu College Nagarcoil Tamilnadu India

ISBN: (纸本)0769530508

The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of image fusion. image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Direct Fusion and Multi resolution fusion. In Direct fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Multi resolution fusion uses transform for representing the source image at multi scale. The most common widely used transform for image fusion at multi scale is Wavelet Transform. This paper describes the optimal level of decomposition of discrete wavelet transform required for better pixel based fusion of multi focused images in terms of root mean square error, Peak signal to Noise Ratio, Quality Index and image fidelity © 2007 ieee.

关键词： image fusion

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Automated Major Depressive Disorder Classification using Deep Convolutional Neural Networks and Choquet Fuzzy Integral Fusion

Automated Major Depressive Disorder Classification using Dee...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Rafiei, Alireza Wang, Yu-Kai Univ Tehran Fac New Sci & Technol Tehran Iran Univ Technol Sydney Sch Comp Sci Australian Artificial Intelligence Inst Ultimo NSW 2007 Australia

ISBN: (纸本)9781665487689

Major depressive disorder (MDD) is a common and severe ailment impacting functional frailty, while its concrete manifestations have been shrouded in mystery. Hence, manual diagnosis of MDD is an arduous and subjective task. Despite the aid of electroencephalogram (EEG) signals in the detection, developing intelligent systems are required to improve clinical utility, performance, and efficiency. In this study, we focus on the automated detection of MDD via raw EEG data using convolutional neural networks (CNN). For this objective, we first extracted the short-time Fourier transform (STFT) of EEG records for five distinct band powers and created an image representing the frequency oscillation of every channel during a resting state. Afterward, we applied three approaches to determine whether a subject is MDD or a Healthy individual. In the first approach, a 2D-CNN model was developed for each band power to detect MDD separately. Second, the outcomes of the developed models were used to establish a Choquet fuzzy integral fusion to classify subjects using all of the previous models. The third approach was dedicated to introducing a 3D-CNN architecture. This model received three-dimensional data by putting different band powers' images together. The two last approaches achieved a 95.65% accuracy and 100% sensitivity to detect MDD. The proposed approaches can help clinicians as straightforward, efficient, and intelligent diagnostic tools for detecting MDD.

关键词： Major depressive disorder (MDD) Deep learning Choquet fuzzy integral fusion EEG signal processing EEG band powers Convolutional neural network (CNN)

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A road traffic signal recognition system based on template matching employing tree classifier

A road traffic signal recognition system based on template m...

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International Conference on computational intelligence and Multimedia Applications, ICCIMA 2007

作者： Varun, S. Singh, Surendra Sanjeev Kunte, R. Sudhaker Samuel, R.D. Philip, Bindu Department of Electronics and Communication S J College of Engineering Mysore - 570006 Karnataka India

ISBN: (纸本)0769530508

The traffic sign detection and recognition system is an essential module of the driver warning and assistance system. Smart vehicles are the order of the day. Such vehicles have the ability to warn drivers of pending situations, remind them of speed limits and even automatically take evasive action. Due to the visual nature of existing infrastructure, signs and line markings, image processing will play a large part in these systems. In this paper we present an automated Traffic sign recognition system allowing an invariance localization to changes in position, scale, rotation, weather conditions, partial occlusion, and the presence of other objects of the same color. The reliability demonstrated by the proposed method suggests that this system could be a part of an integrated driver warning and assistance system based on computer vision technology. © 2007 ieee.

关键词： image recognition

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A layered segmentation algorithm of fingerprint

A layered segmentation algorithm of fingerprint

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International Conference on Communications, Circuits and Systems Proceedings

作者： Gao, Jingjing Xie, Mei Univ Elect Sci & Technol China Sch Elect Engn Chengdu 610054 Peoples R China

ISBN: (纸本)9781424408849

Fingerprint image segmentation heavily influences the performance of fingerprint verification systems. The task of generic segmentation algorithm is only to find the background and the foreground. But the layered segmentation method in this paper is not only to distinguish the background and the foreground but also to be helpful for the matching of fingerprint by endowing different weight to the minutiae in different area. The traditional methods that usually use only one feature to segment diversified images are broadly introduced. In this paper, we introduce a new approach to segment the fingerprint image that uses both the information of gray level of fingerprint image and the direction information from fingerprint image. This approach can segment the fingerprint image to three kinds of area according to the definition of every block of image. The segmentation method described above has been tested on the fingerprint images from the FVC2004 database. The fingerprint is segmented to the background, the clear area of the foreground and the dim area which can be renewed in the foreground using different criteria of estimation in different steps. The effect of this segmentation achieves the anticipative aim that we want to get.

关键词： image segmentation

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A Pilot Study of Automatic Lung Tumor Segmentation from Positron Emission Tomography images using Standard Uptake Values

A Pilot Study of Automatic Lung Tumor Segmentation from Posi...

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ieee symposium on computational intelligence in image and signal processing, ciisp

作者： Aparna Kanakatte Jayavardhana Gubbi Nallasamy Mani Tomas Kron David Binns Department of Electrical and Computer Systems Monash University Clayton VIC Australia Department of Electrical and Electronics Engineering University of Melbourne Parville VIC Australia Peter MacCallum Cancer Centre East Melbourne VIC Australia

Positron emission tomography (PET) is a medical imaging procedure that shows the physiological function of an organ or tissue. The role of PET during the past decade has evolved rapidly in the detection of lung tumors but the research on quantitative evaluation of PET images is still in its infancy. PET commonly involves scanning the patient after administration of a radioactive analogue of glucose called fluorodeoxy-glucose (FDG). Tumor cells metabolise more glucose than most normal cells. In PET lung images the heart is often visible and because of its constant pumping of blood it requires more glucose and hence both the tumor and the heart appear brighter than the rest in the PET image. In this paper we present a novel segmentation scheme for detecting the tumor alone in lung PET images using standard uptake values (SUV) and connected component analysis. We perform the segmentation in two steps. In coarse segmentation, a non linear scaling of SUV values is performed and then a threshold is chosen adaptively to convert the gray image into the binary image. Fine segmentation is performed on the coarse segmented data in order to narrow down the region of interest using connected component labeling. To our knowledge no one has used connected component analysis for segmenting PET images. We compare our proposed scheme with several commonly used medical image segmentation techniques like threshold, Sobel edge detector, Laplacian of Gaussian (LoG) edge detector, region growing and SUV based segmentation (applied only to PET as SUV is specific to PET). One of the problems in lung tumor detection is the presence of the heart in the image which accumulates activity and often gets recognized as a hot spot (a probable tumor). All the other segmentation schemes detected both the heart and the tumor as hot spots while our segmentation scheme detected the tumor alone as the hot spot. The preliminary study of the proposed scheme has yielded very promising results and will be stu

关键词： Lung neoplasms image segmentation Positron emission tomography Heart image edge detection Sugar Tumors Biomedical imaging image analysis Detectors

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