Pub Date : 2015-07-09DOI: 10.1109/ReTIS.2015.7232863
Parneeta Sidhu, M. Bhatia, Abhishek Ravi, Kirti Jherwal
Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual distributions. Many ensemble learning approaches were developed to handle these changes in the dataset, which proved to be better than a single classifier system. In our work, we will discuss the framework of our new approach, Double Weighted Methodology and empirically prove it to be better than the existing single classifier approaches and the online ensemble approaches. Empirical results would prove that our approach is highly competitive, giving good accuracy and speed in handling and identifying drifts in data, irrespective of noise present in the dataset.
{"title":"Double weighted methodology: A weighted ensemble approach to handle concept drift in data streams","authors":"Parneeta Sidhu, M. Bhatia, Abhishek Ravi, Kirti Jherwal","doi":"10.1109/ReTIS.2015.7232863","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232863","url":null,"abstract":"Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual distributions. Many ensemble learning approaches were developed to handle these changes in the dataset, which proved to be better than a single classifier system. In our work, we will discuss the framework of our new approach, Double Weighted Methodology and empirically prove it to be better than the existing single classifier approaches and the online ensemble approaches. Empirical results would prove that our approach is highly competitive, giving good accuracy and speed in handling and identifying drifts in data, irrespective of noise present in the dataset.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126998903","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232927
Sanjay Singh, S. Karumuri
Adiabatic Logic is the most effective technique which is used for implementing of low power digital logic circuits. In this research paper to designed low power Dissipation carry select adder using DFAL 2X1 mux and Diode free adiabatic logic (DFAL) which compare proposed adder circuit with CMOS Technology Designed Adder for low power VLSI Application. In digital electronics, adder is a play important role that performs addition of binary numbers. Now a days The Propagation Delay of Each adder is major problem overcomes by using Carry select Adder. Its area is slightly increasing as compared with normal adder. In this research paper we have used T_SPICE simulator at 0.18μm technology with Mosis Modal and 1.8V standard CMOS for simulation. We have observed that Diode free adiabatic technique saves 55% more power in comparison of CMOS logic with the transition frequency range of 10-80MHZ.
{"title":"Implementation of 4-bit carry select adder using Diode free adiabatic logic (DFAL)","authors":"Sanjay Singh, S. Karumuri","doi":"10.1109/ReTIS.2015.7232927","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232927","url":null,"abstract":"Adiabatic Logic is the most effective technique which is used for implementing of low power digital logic circuits. In this research paper to designed low power Dissipation carry select adder using DFAL 2X1 mux and Diode free adiabatic logic (DFAL) which compare proposed adder circuit with CMOS Technology Designed Adder for low power VLSI Application. In digital electronics, adder is a play important role that performs addition of binary numbers. Now a days The Propagation Delay of Each adder is major problem overcomes by using Carry select Adder. Its area is slightly increasing as compared with normal adder. In this research paper we have used T_SPICE simulator at 0.18μm technology with Mosis Modal and 1.8V standard CMOS for simulation. We have observed that Diode free adiabatic technique saves 55% more power in comparison of CMOS logic with the transition frequency range of 10-80MHZ.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130374182","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232918
B. Halder, S. Mitra
In Telemedicine applications, Digital watermarking is a technique to improve the security and authenticity of ECG signals transmitted to the doctor's end through GSM network. Keeping this in mind, the authors of this paper propose a new watermarking technique which embeds patient's identification inside the ECG signals that will enhance the security and authenticity of ECG signals. In this paper ECG signals are watermarked with patient identity using Adaptive Normalization Factor (ANF) and Least Significant Bit (LSB) watermarking technique to avoid confusion between the ECG signals and patient's identity. The entire technique has been found to be useful to a variety of ECG records for all the 12 leads taken from CSE Multi-lead ECG diagnostic record and the maximum 15 character string that is used to implant watermark embodies for patient's recognition. The novelty of the projected watermarking technique is that the implanted watermark can be completely detached from any altered form of actual signal. It has been observed that the projected method gives a marginal quantity of signal distortion (0.018%), which does not have an effect on any vital features of the ECG signals and it also does not cause any changes in the diagnosis.
{"title":"Modified watermarked ECG signals by using adaptive normalization factor","authors":"B. Halder, S. Mitra","doi":"10.1109/ReTIS.2015.7232918","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232918","url":null,"abstract":"In Telemedicine applications, Digital watermarking is a technique to improve the security and authenticity of ECG signals transmitted to the doctor's end through GSM network. Keeping this in mind, the authors of this paper propose a new watermarking technique which embeds patient's identification inside the ECG signals that will enhance the security and authenticity of ECG signals. In this paper ECG signals are watermarked with patient identity using Adaptive Normalization Factor (ANF) and Least Significant Bit (LSB) watermarking technique to avoid confusion between the ECG signals and patient's identity. The entire technique has been found to be useful to a variety of ECG records for all the 12 leads taken from CSE Multi-lead ECG diagnostic record and the maximum 15 character string that is used to implant watermark embodies for patient's recognition. The novelty of the projected watermarking technique is that the implanted watermark can be completely detached from any altered form of actual signal. It has been observed that the projected method gives a marginal quantity of signal distortion (0.018%), which does not have an effect on any vital features of the ECG signals and it also does not cause any changes in the diagnosis.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125648097","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232851
M. Gangopadhyaya, P. Mukherjee, Udit Sharma, B. Gupta, Suvrajit Manna
Microstrip Antenna (MSA) found widespread applications in satellite communications, wireless and microwave systems, direct broadcast systems; due to their reduced cost and compact-planar structure. Differential Evolution (DE) algorithm is a recent evolutionary computing technique. In this paper, we have implemented DE algorithm to optimize the resonant frequencies of the inset line fed rectangular microstrip patch antenna considering their geometrical design parameters like patch length, patch width and the length of the inset fed as an unknown variables. Simulations have been done for different microwave frequencies (3 to 18 GHz) for the optimized antennas.
{"title":"Design optimization of microstrip fed rectangular microstrip antenna using differential evolution algorithm","authors":"M. Gangopadhyaya, P. Mukherjee, Udit Sharma, B. Gupta, Suvrajit Manna","doi":"10.1109/ReTIS.2015.7232851","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232851","url":null,"abstract":"Microstrip Antenna (MSA) found widespread applications in satellite communications, wireless and microwave systems, direct broadcast systems; due to their reduced cost and compact-planar structure. Differential Evolution (DE) algorithm is a recent evolutionary computing technique. In this paper, we have implemented DE algorithm to optimize the resonant frequencies of the inset line fed rectangular microstrip patch antenna considering their geometrical design parameters like patch length, patch width and the length of the inset fed as an unknown variables. Simulations have been done for different microwave frequencies (3 to 18 GHz) for the optimized antennas.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132779245","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232865
D. Adhikary, Swarup Roy
Association rule mining (ARM) techniques are effective in extracting frequent patterns and hidden associations among data items in various databases. These techniques are widely used for learning behavior, predicting events and making decisions at various levels. The conventional ARM techniques are however limited to databases comprising categorical data only whereas the real-world databases mostly in business and scientific domains have attributes containing quantitative data. Therefore, an improvised methodology called Quantitative Association Rule Mining (QARM) is used that helps discovering hidden associations from the real-world quantitative databases. In this paper, we present an exhaustive discussion on the trends in QARM research and further make a systematic classification of the available techniques into different categories based on the type of computational methods they adopted. We perform a critical analysis of various methods proposed so far and present a theoretical comparative study among them. We also enumerate some of the issues that needs to be addressed in future research.
{"title":"Trends in quantitative association rule mining techniques","authors":"D. Adhikary, Swarup Roy","doi":"10.1109/ReTIS.2015.7232865","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232865","url":null,"abstract":"Association rule mining (ARM) techniques are effective in extracting frequent patterns and hidden associations among data items in various databases. These techniques are widely used for learning behavior, predicting events and making decisions at various levels. The conventional ARM techniques are however limited to databases comprising categorical data only whereas the real-world databases mostly in business and scientific domains have attributes containing quantitative data. Therefore, an improvised methodology called Quantitative Association Rule Mining (QARM) is used that helps discovering hidden associations from the real-world quantitative databases. In this paper, we present an exhaustive discussion on the trends in QARM research and further make a systematic classification of the available techniques into different categories based on the type of computational methods they adopted. We perform a critical analysis of various methods proposed so far and present a theoretical comparative study among them. We also enumerate some of the issues that needs to be addressed in future research.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131953828","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232886
Paramita Das, Trijit Chatterjee, S. Chakraborty, Debasree Mondal, N. Das
Cancer is one of the most destructive diseases which if not detected in time, will surely lead to death. About 12 million people will be died due to cancer by 2030 as per the statistics, provided by the World Health Organization (WHO). Thus a big challenge and area of research emerges in front of both the medical practitioner and scientific researcher to fight against cancers. When a patient is suspected for the presence of malignant tumor they are advised for FNAC (Fine Needle Aspiration Cytology) test where specimens of cells can be taken in minimally invasive way with, e.g., tiny needles, with or without syringes. One of the main drawbacks of cytopathological diagnosis is the time required for an expert to visually inspect a specimen under a microscope, in search of malignant or suspicious cells and manually select them for further analysis. The present work tried to device an automated computer-aided diagnostic system specifically to reduce time and provide `second opinion' for pathologists in making diagnosis. A database of 100 FNAC images were taken on which k-fold cross-validation was performed, where k varied, for the diagnosis of malignancy. Initially, elimination of cytoplasm from the images consisting of multiple cells was done by performing saturation threshold segmentation and from the segmented nucleus boundary, meaningful texture and shape describing features are calculated using GLCM and LBP algorithms. The outcome of segmentation followed by feature extraction was tested by using the Logistic classifier which is a machine learning algorithm. The achieved diagnostic accuracy is 86%, when features obtained by combining GLCM and LBP methods, are used for classification.
{"title":"A texture based approach for automatic identification of benign and malignant tumor from FNAC images","authors":"Paramita Das, Trijit Chatterjee, S. Chakraborty, Debasree Mondal, N. Das","doi":"10.1109/ReTIS.2015.7232886","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232886","url":null,"abstract":"Cancer is one of the most destructive diseases which if not detected in time, will surely lead to death. About 12 million people will be died due to cancer by 2030 as per the statistics, provided by the World Health Organization (WHO). Thus a big challenge and area of research emerges in front of both the medical practitioner and scientific researcher to fight against cancers. When a patient is suspected for the presence of malignant tumor they are advised for FNAC (Fine Needle Aspiration Cytology) test where specimens of cells can be taken in minimally invasive way with, e.g., tiny needles, with or without syringes. One of the main drawbacks of cytopathological diagnosis is the time required for an expert to visually inspect a specimen under a microscope, in search of malignant or suspicious cells and manually select them for further analysis. The present work tried to device an automated computer-aided diagnostic system specifically to reduce time and provide `second opinion' for pathologists in making diagnosis. A database of 100 FNAC images were taken on which k-fold cross-validation was performed, where k varied, for the diagnosis of malignancy. Initially, elimination of cytoplasm from the images consisting of multiple cells was done by performing saturation threshold segmentation and from the segmented nucleus boundary, meaningful texture and shape describing features are calculated using GLCM and LBP algorithms. The outcome of segmentation followed by feature extraction was tested by using the Logistic classifier which is a machine learning algorithm. The achieved diagnostic accuracy is 86%, when features obtained by combining GLCM and LBP methods, are used for classification.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133658601","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232915
A. Saha, A. Konar, Mainak Dan, Sudipta Ghosh
This paper presents a novel feature selection and fuzzy-neural classification scheme to decode motor imagery signals during driving. To perform this, we would consider the fuzziness involved in sudden left bent, where the driver is supposed to take sudden 90o left turn during acceleration. This requires classification of motor imagery signals during acceleration and steering left control. The fuzzy-recurrent neural network classifier offers better performance using proposed differential evolution-induced feature selection technique, when compared with principal component analysis in such situation and provides the highest classification accuracy of 98.472%. In addition, false classification rate/misclassification rate is also found much higher when using principal component analysis instead of proposed differential evolution-induced feature selection algorithm. The performance of the proposed differential evolution-induced fuzzy recurrent neural network classifier has been compared with a list of standard classifiers including linear support vector machines, k-nearest neighbor and support vector machines with radial basis function kernel, where fuzzy-recurrent neural network classifier outperforms its competitors with an average classification accuracy of 95.472% and 95.647 for steering left and acceleration motor intensions respectively.
{"title":"Decoding of motor imagery potentials in driving using DE-induced fuzzy-neural classifier","authors":"A. Saha, A. Konar, Mainak Dan, Sudipta Ghosh","doi":"10.1109/ReTIS.2015.7232915","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232915","url":null,"abstract":"This paper presents a novel feature selection and fuzzy-neural classification scheme to decode motor imagery signals during driving. To perform this, we would consider the fuzziness involved in sudden left bent, where the driver is supposed to take sudden 90o left turn during acceleration. This requires classification of motor imagery signals during acceleration and steering left control. The fuzzy-recurrent neural network classifier offers better performance using proposed differential evolution-induced feature selection technique, when compared with principal component analysis in such situation and provides the highest classification accuracy of 98.472%. In addition, false classification rate/misclassification rate is also found much higher when using principal component analysis instead of proposed differential evolution-induced feature selection algorithm. The performance of the proposed differential evolution-induced fuzzy recurrent neural network classifier has been compared with a list of standard classifiers including linear support vector machines, k-nearest neighbor and support vector machines with radial basis function kernel, where fuzzy-recurrent neural network classifier outperforms its competitors with an average classification accuracy of 95.472% and 95.647 for steering left and acceleration motor intensions respectively.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130404260","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232854
R. Madhu, G. Rao
Capacity is one of the significant factors in determining the performance of any cellular system. However, the capacity is limited by the interference. In cellular systems, the major source of interference is the Co-Channel Interference(CCI). In this paper, an analytical method is proposed to evaluate the erlang capacity of 3G WCDMA systems under CCI and the performance analysis with the use of adaptive antenna system. In this the probability of CCI is considered for capacity determination. Erlang Capacity results are evaluated using MATLABR2013b software with different number of co-channel interferes, adaptive antenna system and power control. The capacity of a WCDMA system can be improved with the use of adaptive antenna for a given probability of CCI. It is observed that the capacity of a WCDMA system is increased from 202.82 Erlangs to 278.22 Erlangs per cell with the proposed method.
{"title":"Erlang capacity evaluation of WCDMA systems under co-channel interference with adaptive antenna","authors":"R. Madhu, G. Rao","doi":"10.1109/ReTIS.2015.7232854","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232854","url":null,"abstract":"Capacity is one of the significant factors in determining the performance of any cellular system. However, the capacity is limited by the interference. In cellular systems, the major source of interference is the Co-Channel Interference(CCI). In this paper, an analytical method is proposed to evaluate the erlang capacity of 3G WCDMA systems under CCI and the performance analysis with the use of adaptive antenna system. In this the probability of CCI is considered for capacity determination. Erlang Capacity results are evaluated using MATLABR2013b software with different number of co-channel interferes, adaptive antenna system and power control. The capacity of a WCDMA system can be improved with the use of adaptive antenna for a given probability of CCI. It is observed that the capacity of a WCDMA system is increased from 202.82 Erlangs to 278.22 Erlangs per cell with the proposed method.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124147301","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232937
Arka Saha, P. Sadhukhan
Location estimation is essential to the success of location based services. Since GPS does not work well in indoor and the urban areas, several indoor localization systems have been proposed in the literature. Among these, the fingerprinting-based localization systems involving two phases: training phase and positioning phase, are used mostly. In the training phase, a radio map is constructed by collecting the received signal strength (RSS) measurements at a set of known training locations. In the positioning phase, the training location whose corresponding RSS pattern matches best with the currently observed RSS pattern is selected as the estimated location of the object. The positioning accuracy of such systems depends on the grain size of the training locations, i.e., better localization accuracy can be achieved with increasing number of training locations, which in turn, increases the comparison cost as well as the searching time in the positioning phase. Several clustering strategies have been proposed in the literature to reduce the searching time by grouping several training locations into a cluster and selecting the right cluster in the positioning phase followed by searching within the selected cluster to localize an object. However, selection of some false cluster degrades the positioning accuracy of the localization system. Thus, this paper aims at devising some novel clustering strategy that would reduce the searching time without compromising the positioning accuracy.
{"title":"A novel clustering strategy for fingerprinting-based localization system to reduce the searching time","authors":"Arka Saha, P. Sadhukhan","doi":"10.1109/ReTIS.2015.7232937","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232937","url":null,"abstract":"Location estimation is essential to the success of location based services. Since GPS does not work well in indoor and the urban areas, several indoor localization systems have been proposed in the literature. Among these, the fingerprinting-based localization systems involving two phases: training phase and positioning phase, are used mostly. In the training phase, a radio map is constructed by collecting the received signal strength (RSS) measurements at a set of known training locations. In the positioning phase, the training location whose corresponding RSS pattern matches best with the currently observed RSS pattern is selected as the estimated location of the object. The positioning accuracy of such systems depends on the grain size of the training locations, i.e., better localization accuracy can be achieved with increasing number of training locations, which in turn, increases the comparison cost as well as the searching time in the positioning phase. Several clustering strategies have been proposed in the literature to reduce the searching time by grouping several training locations into a cluster and selecting the right cluster in the positioning phase followed by searching within the selected cluster to localize an object. However, selection of some false cluster degrades the positioning accuracy of the localization system. Thus, this paper aims at devising some novel clustering strategy that would reduce the searching time without compromising the positioning accuracy.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122122783","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 : 2015-07-09DOI: 10.1109/ReTIS.2015.7232910
Biswajit Biswas, A. Chakrabarti, K. Dey
Medical image fusion combines both functional and anatomical structures in different imaging modalities such as Computer Tomography (CT) and Magnetic Resonance Image (MRI). In spine medical image fusion, CT and MR of the spine provides complementary information that assist to diagnostic and therapeutic decisions. Thus, spine medical image fusion is an essential technique that integrate the anatomical details of CT image and the functional information of MR image to a fused image with high functional and anatomical structures. This paper proposes a spine medical image fusion using wiener filter (WF) in shearlet domain. Shearlet transform (ST) obtains the shearlet subbands from CT and MR source images. A unique fusion strategy is devised for lowpass ST subbands. The processing of highpass ST subbands are considered in detail. Finally, the fused image achieved by inverse shearlet transform (IST). By evaluating with mainly some familiar techniques with regard to some quality assessment indexes, simulation and experimental results on spine images are presented the excellence of proposed technique.
{"title":"Spine medical image fusion using wiener filter in shearlet domain","authors":"Biswajit Biswas, A. Chakrabarti, K. Dey","doi":"10.1109/ReTIS.2015.7232910","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232910","url":null,"abstract":"Medical image fusion combines both functional and anatomical structures in different imaging modalities such as Computer Tomography (CT) and Magnetic Resonance Image (MRI). In spine medical image fusion, CT and MR of the spine provides complementary information that assist to diagnostic and therapeutic decisions. Thus, spine medical image fusion is an essential technique that integrate the anatomical details of CT image and the functional information of MR image to a fused image with high functional and anatomical structures. This paper proposes a spine medical image fusion using wiener filter (WF) in shearlet domain. Shearlet transform (ST) obtains the shearlet subbands from CT and MR source images. A unique fusion strategy is devised for lowpass ST subbands. The processing of highpass ST subbands are considered in detail. Finally, the fused image achieved by inverse shearlet transform (IST). By evaluating with mainly some familiar techniques with regard to some quality assessment indexes, simulation and experimental results on spine images are presented the excellence of proposed technique.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114444956","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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