Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435699
Kalpana Naruka, Dr.O.P. Sahu
This paper reviews some existing Speech Enhancement techniques and also proposes a new method for enhancing the speech by combining Compressed Sensing and Kalman filter approaches. This approach is based on reconstruction of noisy speech signal using Compressive Sampling Matching Pursuit (CoSaMP) algorithm and further enhanced by Kalman filter. The performance of the proposed method is evaluated and compared with that of the existing techniques in terms of intelligibility and quality measure parameters of speech. The proposed algorithm shows an improved performance compared to Spectral Subtraction, MMSE, Wiener filter, Signal Subspace, Kalman filter in terms of WSS, LLR, SegSNR, SNRloss, PESQ and overall quality.
{"title":"An improved speech enhancement approach based on combination of compressed sensing and Kalman filter","authors":"Kalpana Naruka, Dr.O.P. Sahu","doi":"10.1109/ICCIC.2015.7435699","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435699","url":null,"abstract":"This paper reviews some existing Speech Enhancement techniques and also proposes a new method for enhancing the speech by combining Compressed Sensing and Kalman filter approaches. This approach is based on reconstruction of noisy speech signal using Compressive Sampling Matching Pursuit (CoSaMP) algorithm and further enhanced by Kalman filter. The performance of the proposed method is evaluated and compared with that of the existing techniques in terms of intelligibility and quality measure parameters of speech. The proposed algorithm shows an improved performance compared to Spectral Subtraction, MMSE, Wiener filter, Signal Subspace, Kalman filter in terms of WSS, LLR, SegSNR, SNRloss, PESQ and overall quality.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130931947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435795
A. Rao, Swathi Agarwal, K. Srinivas, B. Rani
The fascinations of Internet of Things (IoT) necessitate a large number of devices are to be integrated with the existing IoT. These devices are very difficult to manage in a large distributed environment without a careful management design. These location based devices generate data at fixed intervals of time and need configure these devices to software platform to analyze data and understand environment in better way. So, learning capability should incorporate within the system as the environment of system changes dynamically. As the Internet of Things continues to develop, further potential is estimated by a combination with related technology approaches and concepts such as Cloud Computing, Future Internet, Big Data, Robotics and Semantic Technologies. The idea is becomes now evident as those related concepts have started to reveal synergies by combining them.
{"title":"Learning mechanism for RT task scheduling","authors":"A. Rao, Swathi Agarwal, K. Srinivas, B. Rani","doi":"10.1109/ICCIC.2015.7435795","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435795","url":null,"abstract":"The fascinations of Internet of Things (IoT) necessitate a large number of devices are to be integrated with the existing IoT. These devices are very difficult to manage in a large distributed environment without a careful management design. These location based devices generate data at fixed intervals of time and need configure these devices to software platform to analyze data and understand environment in better way. So, learning capability should incorporate within the system as the environment of system changes dynamically. As the Internet of Things continues to develop, further potential is estimated by a combination with related technology approaches and concepts such as Cloud Computing, Future Internet, Big Data, Robotics and Semantic Technologies. The idea is becomes now evident as those related concepts have started to reveal synergies by combining them.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131001501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435724
Avijit Mondal, Subrata Paul, A. Mitra, Biswajit Gope
Internet Security system has been largely threatened due to increase in Internet Worms at an alarming rate. Intrusion Detection System signature has been manually generated by security experts during their study on the network status after the release of a new worm. But it can take place after a significant loss of assets. In this research work, we are proposing an automatic method which will generate signature for detection of polymorphic worms. We will be applying Principal Component Analysis (PCA) for determining the important substrings that appears mostly and are pooled amongst the instances of polymorphic worms for using them as signatures. The results generated show the successful detection of polymorphic worms using zero false positives and low false negatives by the PCA.
{"title":"Automated signature generation for polymorphic worms using substrings extraction and principal component analysis","authors":"Avijit Mondal, Subrata Paul, A. Mitra, Biswajit Gope","doi":"10.1109/ICCIC.2015.7435724","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435724","url":null,"abstract":"Internet Security system has been largely threatened due to increase in Internet Worms at an alarming rate. Intrusion Detection System signature has been manually generated by security experts during their study on the network status after the release of a new worm. But it can take place after a significant loss of assets. In this research work, we are proposing an automatic method which will generate signature for detection of polymorphic worms. We will be applying Principal Component Analysis (PCA) for determining the important substrings that appears mostly and are pooled amongst the instances of polymorphic worms for using them as signatures. The results generated show the successful detection of polymorphic worms using zero false positives and low false negatives by the PCA.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124626138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435664
Vivek P. Patkar, Lekha Das, Prakruti J. Joshi
EEG (Electroencephalography) is a recording of electrical activities of brain measured from scalp. Brain is a control center for almost all functions of body. As EEG originates from brain, it contains various components related to cognitive activities of brain. Hence, it also contains information regarding the motor functions associated with movement of the body. EEG is commonly recorded for purposes of diagnosis and research associated with diseases like epilepsy, seizures, sleep disorders etc. But apart from these applications it can also be used to map various motor movements being thought of. This may lead to development of landmark devices in the field of rehabilitation of physically challenged individuals. Here we intend to extract the features and classify the directions using EEG. At initial stage it is desired to classify two movements i.e. left and right, but the method can be extended for the classification of other directions as well. In present scenario the most suitable methods for classification problems can be developed using machine learning algorithms. In this work the features like probability co efficient, PSE (power spectral entropy) and STFT (Short Time Fourier Transform) are extracted and evaluated for their efficiency in classification. Radial Basis Function is used for classifying these features. The study shows probability co efficient and STFT have yielded about 60% accuracy in classifying raw EEG signals proving them advantageous over power spectral entropy.
{"title":"Evaluation of PSE, STFT and probability coefficients for classifying two directions from EEG using radial basis function","authors":"Vivek P. Patkar, Lekha Das, Prakruti J. Joshi","doi":"10.1109/ICCIC.2015.7435664","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435664","url":null,"abstract":"EEG (Electroencephalography) is a recording of electrical activities of brain measured from scalp. Brain is a control center for almost all functions of body. As EEG originates from brain, it contains various components related to cognitive activities of brain. Hence, it also contains information regarding the motor functions associated with movement of the body. EEG is commonly recorded for purposes of diagnosis and research associated with diseases like epilepsy, seizures, sleep disorders etc. But apart from these applications it can also be used to map various motor movements being thought of. This may lead to development of landmark devices in the field of rehabilitation of physically challenged individuals. Here we intend to extract the features and classify the directions using EEG. At initial stage it is desired to classify two movements i.e. left and right, but the method can be extended for the classification of other directions as well. In present scenario the most suitable methods for classification problems can be developed using machine learning algorithms. In this work the features like probability co efficient, PSE (power spectral entropy) and STFT (Short Time Fourier Transform) are extracted and evaluated for their efficiency in classification. Radial Basis Function is used for classifying these features. The study shows probability co efficient and STFT have yielded about 60% accuracy in classifying raw EEG signals proving them advantageous over power spectral entropy.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114386284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435718
J. Rizwana, R. Jeevitha, R. Venkatesh, K. Parthiban
Under normal operating conditions, the generation capacity is more than the total load demand and losses. The objective is to find the real power scheduling of each generator for an interconnected power system under testing condition to minimize the operating cost of the power plant. Hence the generators power are allowed to vary within the given limits to meet the particular load with minimum fuel cost which is called as optimal power flow problem. The objective function of this paper is to minimize the fuel cost of the power system for the various loads under consideration by solving the economic dispatch problem (EDP) of real power generation by using MPSO optimization algorithm. This paper compares the optimization techniques such as Particle Swarm Optimization, Modified Particle Swarm Optimization (MPSO) in a 3-unit generating system to show the effectiveness of the MPSO algorithm. Also by using the optimization technique the power losses of the considered power system were reduced.
{"title":"Minimization of fuel cost in solving the power economic dispatch problem including transmission losses by using modified Particle Swarm Optimization","authors":"J. Rizwana, R. Jeevitha, R. Venkatesh, K. Parthiban","doi":"10.1109/ICCIC.2015.7435718","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435718","url":null,"abstract":"Under normal operating conditions, the generation capacity is more than the total load demand and losses. The objective is to find the real power scheduling of each generator for an interconnected power system under testing condition to minimize the operating cost of the power plant. Hence the generators power are allowed to vary within the given limits to meet the particular load with minimum fuel cost which is called as optimal power flow problem. The objective function of this paper is to minimize the fuel cost of the power system for the various loads under consideration by solving the economic dispatch problem (EDP) of real power generation by using MPSO optimization algorithm. This paper compares the optimization techniques such as Particle Swarm Optimization, Modified Particle Swarm Optimization (MPSO) in a 3-unit generating system to show the effectiveness of the MPSO algorithm. Also by using the optimization technique the power losses of the considered power system were reduced.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122056838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435722
A. Savitha, Rajiv R. Chetwani, Y. R. Bhanumathy, M. Ravindra
ISRO programmes are realizing 8 to 10 spacecrafts a year. The complexity of the spacecraft is increasing in each of geostationary, IRS and interplanetary missions, leading to increase in computational load of the onboard computer. Hence there is a need for quick testing and analysis of various spacecrafts. The attitude and orbit control electronics system receives data from various sensors and does the processing and maintains the spacecraft in station by generating control signals for actuators. It has various subsystems which interact with it. In addition to this package there is data handling remote terminal package which is implemented to provide other interfaces like star sensor, satellite positioning system, solid state recorder, baseband data handling and many more. These systems communicate with attitude and orbit control electronics through MIL-STD-1553 Bus. This paper explains the methodology developed for the analysis of this spacecraft baseband data handling auxiliary data. This analysis helps for both internal logic verification and external component interface. The actual results are recorded for further analysis and reports are generated.
{"title":"Automated verification of spacecraft auxiliary data","authors":"A. Savitha, Rajiv R. Chetwani, Y. R. Bhanumathy, M. Ravindra","doi":"10.1109/ICCIC.2015.7435722","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435722","url":null,"abstract":"ISRO programmes are realizing 8 to 10 spacecrafts a year. The complexity of the spacecraft is increasing in each of geostationary, IRS and interplanetary missions, leading to increase in computational load of the onboard computer. Hence there is a need for quick testing and analysis of various spacecrafts. The attitude and orbit control electronics system receives data from various sensors and does the processing and maintains the spacecraft in station by generating control signals for actuators. It has various subsystems which interact with it. In addition to this package there is data handling remote terminal package which is implemented to provide other interfaces like star sensor, satellite positioning system, solid state recorder, baseband data handling and many more. These systems communicate with attitude and orbit control electronics through MIL-STD-1553 Bus. This paper explains the methodology developed for the analysis of this spacecraft baseband data handling auxiliary data. This analysis helps for both internal logic verification and external component interface. The actual results are recorded for further analysis and reports are generated.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122175714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435737
B. Deepa, M. Sumithra
Noise removal techniques have become an essential exercise in medical imaging applications, for the study of anatomical structures. To address this issue many denoising algorithm has been proposed both in spatial and frequency domain. Among them, few techniques in spatial domain are hybrid median filter, Weiner filter, bilateral filter, histogram equalization and in frequency domain are wavelet transform, independent component analysis were successfully used in medical imaging. The most commonly affected noises in medical image are salt and pepper, Gaussian, Speckle and Brownian noise. In this paper, the medical images taken for comparison include MRI brain images, in gray scale and RGB. The performances of these algorithms are analyzed for various noise types at different noise levels ranging from 0 dB to 30 dB. The evaluation of these algorithms is done by measures like peak signal to noise ratio (PSNR), root mean square error value (RMSE), universal quality index (UQI) and picture quality scale(PQS). Experimental results suggest that, independent component analysis performs better for removing salt and pepper noise in RGB and gray scale and Gaussian noise for images in RGB. Wavelet transform gives superior performance for removing speckle and Brownian noise for images in RGB and grayscale, irrespective of the noise level considered. Whereas histogram equalization gives better quality results while removing Gaussian noise at all noise levels for the images in gray scale only. On the other hand all spatial filtering techniques give comparative results at all dB levels in gray scale, which is inferior to frequency domain techniques.
{"title":"Comparative analysis of noise removal techniques in MRI brain images","authors":"B. Deepa, M. Sumithra","doi":"10.1109/ICCIC.2015.7435737","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435737","url":null,"abstract":"Noise removal techniques have become an essential exercise in medical imaging applications, for the study of anatomical structures. To address this issue many denoising algorithm has been proposed both in spatial and frequency domain. Among them, few techniques in spatial domain are hybrid median filter, Weiner filter, bilateral filter, histogram equalization and in frequency domain are wavelet transform, independent component analysis were successfully used in medical imaging. The most commonly affected noises in medical image are salt and pepper, Gaussian, Speckle and Brownian noise. In this paper, the medical images taken for comparison include MRI brain images, in gray scale and RGB. The performances of these algorithms are analyzed for various noise types at different noise levels ranging from 0 dB to 30 dB. The evaluation of these algorithms is done by measures like peak signal to noise ratio (PSNR), root mean square error value (RMSE), universal quality index (UQI) and picture quality scale(PQS). Experimental results suggest that, independent component analysis performs better for removing salt and pepper noise in RGB and gray scale and Gaussian noise for images in RGB. Wavelet transform gives superior performance for removing speckle and Brownian noise for images in RGB and grayscale, irrespective of the noise level considered. Whereas histogram equalization gives better quality results while removing Gaussian noise at all noise levels for the images in gray scale only. On the other hand all spatial filtering techniques give comparative results at all dB levels in gray scale, which is inferior to frequency domain techniques.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117012776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435805
Sharad Wagh, Avinash More, Aditya Khavnekar
Cognitive radio is the one of the technique which used to solve the problems of spectrum inefficiency and limited spectrum availability in wireless networks. However, while designing the availability and efficiency of spectrum in wireless network the security in cognitive radio is one of the key challenge. The selfish attack is one of major security issue found in cognitive radio. The selfish attack can be describe, where the selfish node tries to occupy maximum available channels in the network without interfering with the existing channels. Due to this, the overall performance of the network degrades which effects the quality of service (QoS) of the network. However, in order to improve the performance of the networks we have identified the Selfish Attack in cognitive radio by using channel pre occupation scheme with the help of Cooperative Neighboring Cognitive Radio Nodes (COOPON) technique.
{"title":"Identification of selfish attack in cognitive radio ad-hoc networks","authors":"Sharad Wagh, Avinash More, Aditya Khavnekar","doi":"10.1109/ICCIC.2015.7435805","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435805","url":null,"abstract":"Cognitive radio is the one of the technique which used to solve the problems of spectrum inefficiency and limited spectrum availability in wireless networks. However, while designing the availability and efficiency of spectrum in wireless network the security in cognitive radio is one of the key challenge. The selfish attack is one of major security issue found in cognitive radio. The selfish attack can be describe, where the selfish node tries to occupy maximum available channels in the network without interfering with the existing channels. Due to this, the overall performance of the network degrades which effects the quality of service (QoS) of the network. However, in order to improve the performance of the networks we have identified the Selfish Attack in cognitive radio by using channel pre occupation scheme with the help of Cooperative Neighboring Cognitive Radio Nodes (COOPON) technique.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115184203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435700
G. Patkar, G. Anjaneyulu, P. Mouli
This paper intends to solve palm fruit grading using the proposed prototype and Palm fruit ripeness Unified Modeling Language diagrams. In this paper the issues of correct harvesting prediction, categorizing palm fruits and distinguishing Ripe and Overripe fruit is solved suing Unified Modeling Language (UML) diagrams. UML diagrams designed and shown in the paper helped to model and implement the palm fruit harvester algorithm. The proposed algorithm is implemented in Visual C++ and results are tested with the live data collected from field and farmers. This research will resolve the issue of manual grading thereby helping farmers in producing good quality oil production and also will help researchers to use the prototype for implementation.
{"title":"Palm fruit harvester algorithm for elaeis guineensis oil palm fruit grading using UML","authors":"G. Patkar, G. Anjaneyulu, P. Mouli","doi":"10.1109/ICCIC.2015.7435700","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435700","url":null,"abstract":"This paper intends to solve palm fruit grading using the proposed prototype and Palm fruit ripeness Unified Modeling Language diagrams. In this paper the issues of correct harvesting prediction, categorizing palm fruits and distinguishing Ripe and Overripe fruit is solved suing Unified Modeling Language (UML) diagrams. UML diagrams designed and shown in the paper helped to model and implement the palm fruit harvester algorithm. The proposed algorithm is implemented in Visual C++ and results are tested with the live data collected from field and farmers. This research will resolve the issue of manual grading thereby helping farmers in producing good quality oil production and also will help researchers to use the prototype for implementation.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123431366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435715
Parineeta Suman, D. Parasar, V. Rathod
Ultrasound imaging is one of the most popular and cheapest noninvasive medical scans. At the time of image acquisition, there may be degradation in the quality of image in the form of speckle noise. In recent times, many researches have made various experiments to enhance the quality of medical imaging. However, there is scope to further enhance it. In the proposed method, finding out the seed pixel randomly is the basic problem, which is treated as an optimization problem. This problem can be solved by Particle Swarm Optimization. Using Particle Swarm Optimization algorithm, the fitness function can give us the appropriate seed pixel for the desired ultrasound imaging. In this paper, a novel method is proposed, wherein segmentation will be applied on a fuzzy filtered image. The fuzzy filter applies fuzzy rules to detect regions in the image viz. edge region, homogeneous region, and noisy region by using different gradients, and then filters the noisy region using fuzzy membership rules. The proposed method has been tested on different ultrasound images, and the experimental results demonstrate its effectiveness.
{"title":"Seeded region growing segmentation on ultrasound image using particle swarm optimization","authors":"Parineeta Suman, D. Parasar, V. Rathod","doi":"10.1109/ICCIC.2015.7435715","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435715","url":null,"abstract":"Ultrasound imaging is one of the most popular and cheapest noninvasive medical scans. At the time of image acquisition, there may be degradation in the quality of image in the form of speckle noise. In recent times, many researches have made various experiments to enhance the quality of medical imaging. However, there is scope to further enhance it. In the proposed method, finding out the seed pixel randomly is the basic problem, which is treated as an optimization problem. This problem can be solved by Particle Swarm Optimization. Using Particle Swarm Optimization algorithm, the fitness function can give us the appropriate seed pixel for the desired ultrasound imaging. In this paper, a novel method is proposed, wherein segmentation will be applied on a fuzzy filtered image. The fuzzy filter applies fuzzy rules to detect regions in the image viz. edge region, homogeneous region, and noisy region by using different gradients, and then filters the noisy region using fuzzy membership rules. The proposed method has been tested on different ultrasound images, and the experimental results demonstrate its effectiveness.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129609629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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