Pub Date : 2010-07-07DOI: 10.1109/IPTA.2010.5586776
S. Emeriau, Frédéric Blanchard, J. Poline, L. Pierot, E. Bittar
As fMRI data is high dimensional, applications like connectivity studies, normalization or multivariate analyses, need to reduce data dimension while minimizing the loss of functional information. In our study we use connectivity profiles as a new functional feature to aggregate voxels into clusters. This offers two major advantages in comparison with the current clustering methods. It allows the analyst to deal with the spatial correlation of noise problem, that can lead to bad mergings in the functional domain, and it is based on the whole data independently of a priori information like the General Linear Model (GLM) regressors. We validated that the resulting clusters form a partition of the data in homogeneous regions according to both spatial and functional criteria.
{"title":"Connectivity feature extraction for spatio-functional clustering of fMRI data","authors":"S. Emeriau, Frédéric Blanchard, J. Poline, L. Pierot, E. Bittar","doi":"10.1109/IPTA.2010.5586776","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586776","url":null,"abstract":"As fMRI data is high dimensional, applications like connectivity studies, normalization or multivariate analyses, need to reduce data dimension while minimizing the loss of functional information. In our study we use connectivity profiles as a new functional feature to aggregate voxels into clusters. This offers two major advantages in comparison with the current clustering methods. It allows the analyst to deal with the spatial correlation of noise problem, that can lead to bad mergings in the functional domain, and it is based on the whole data independently of a priori information like the General Linear Model (GLM) regressors. We validated that the resulting clusters form a partition of the data in homogeneous regions according to both spatial and functional criteria.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134363630","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586730
Victor Chen, S. Ruan
In this paper, we present a graph cut application dealing with MRI brain image segmentation. We here propose another emerging approach of region segmentation based on graph cut which supports on the eigenspace characteristics and the perceptual grouping properties to classify brain tumoral tissue. Image segmentation is considered as solving the partitioning clustering problem by extracting the global impression of image. In the aim of providing visual and quantitative information for the diagnosis help in brain diseases, tumor features observed in image sequences must be extracted efficiently. We lastly extend this approach to perform volume segmentation by matching 2D contours set. This 3D representation provides a precise quantitative measure for following up the tumor brain evolution in the case of patients under pharmaceutical treatments.
{"title":"Graph cut segmentation technique for MRI brain tumor extraction","authors":"Victor Chen, S. Ruan","doi":"10.1109/IPTA.2010.5586730","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586730","url":null,"abstract":"In this paper, we present a graph cut application dealing with MRI brain image segmentation. We here propose another emerging approach of region segmentation based on graph cut which supports on the eigenspace characteristics and the perceptual grouping properties to classify brain tumoral tissue. Image segmentation is considered as solving the partitioning clustering problem by extracting the global impression of image. In the aim of providing visual and quantitative information for the diagnosis help in brain diseases, tumor features observed in image sequences must be extracted efficiently. We lastly extend this approach to perform volume segmentation by matching 2D contours set. This 3D representation provides a precise quantitative measure for following up the tumor brain evolution in the case of patients under pharmaceutical treatments.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133419655","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586794
M. Bertrand, F. Bouchara, S. Ramdani
The aim of this paper is to analyze the statistical properties of the Harris corner detector. Usually, the noise effect is computed using a linear model of the corner response H. Our approach, is different and propagate the error through the unmodified expression of H. The experimental results compared to Monte-Carlo simulations show the interest of this method.
{"title":"Estimation of uncertainty for Harris corner detector","authors":"M. Bertrand, F. Bouchara, S. Ramdani","doi":"10.1109/IPTA.2010.5586794","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586794","url":null,"abstract":"The aim of this paper is to analyze the statistical properties of the Harris corner detector. Usually, the noise effect is computed using a linear model of the corner response H. Our approach, is different and propagate the error through the unmodified expression of H. The experimental results compared to Monte-Carlo simulations show the interest of this method.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131092917","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586765
Nizar Fakhfakh, L. Khoudour, E. El-Koursi, J. Bruyelle, Alain Dufaux, J. Jacot
This paper proposes an obstacle detection system for the purpose of preventing accidents at level crossings. In order to avoid the limits of already proposed technologies, this system uses stereo cameras to detect and localize multiple targets at the level crossing. In a first step, a background subtraction module is performed using the Color Independent Component Analysis (CICA) technique which allows to detect vehicles even if they are stopped (the main cause of accidents at Level Crossings). A novel robust stereo matching algorithm is then used to reliably localize in 3D each segmented object. Standard stereo datasets and real-world images are used to evaluate the performances of the proposed algorithm, showing the efficiency and robustness of the proposed video surveillance system.
{"title":"Background subtraction and 3D localization of moving and stationary obstacles at level crossings","authors":"Nizar Fakhfakh, L. Khoudour, E. El-Koursi, J. Bruyelle, Alain Dufaux, J. Jacot","doi":"10.1109/IPTA.2010.5586765","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586765","url":null,"abstract":"This paper proposes an obstacle detection system for the purpose of preventing accidents at level crossings. In order to avoid the limits of already proposed technologies, this system uses stereo cameras to detect and localize multiple targets at the level crossing. In a first step, a background subtraction module is performed using the Color Independent Component Analysis (CICA) technique which allows to detect vehicles even if they are stopped (the main cause of accidents at Level Crossings). A novel robust stereo matching algorithm is then used to reliably localize in 3D each segmented object. Standard stereo datasets and real-world images are used to evaluate the performances of the proposed algorithm, showing the efficiency and robustness of the proposed video surveillance system.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"265 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116190211","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586737
S. Ruan, N. Zhang, S. Lebonvallet, Q. Liao, Yuemin Zhu
The objective of this study concerns the classification of a scene observed by different types of images, which generates large amounts of data to be processed. We have therefore chosen to use the classification SVM (Support Vector Machines) who is known for treating high-dimensional data. Although different sources of information can provide additional information to address the ambiguities, they introduce, at the same time, some redundant information. Our idea for the fusion of these data is to extract the useful information from all data to obtain an effective classification. The selection of the most discriminating features is carried out in the SVM kernel space, because the selection can be done linearly in this space. This selection also helps to reduce the size of data to be classified. The selection criteria are based on class separability. We propose a system based on SVM classification with the selection of characteristics to classify a brain tumor using three types of 3D MRI images. Our system can follow-up the evolution of a tumor along a therapeutic treatment.
{"title":"Fusion and classification of multi-source images by SVM with selected features in a kernel space","authors":"S. Ruan, N. Zhang, S. Lebonvallet, Q. Liao, Yuemin Zhu","doi":"10.1109/IPTA.2010.5586737","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586737","url":null,"abstract":"The objective of this study concerns the classification of a scene observed by different types of images, which generates large amounts of data to be processed. We have therefore chosen to use the classification SVM (Support Vector Machines) who is known for treating high-dimensional data. Although different sources of information can provide additional information to address the ambiguities, they introduce, at the same time, some redundant information. Our idea for the fusion of these data is to extract the useful information from all data to obtain an effective classification. The selection of the most discriminating features is carried out in the SVM kernel space, because the selection can be done linearly in this space. This selection also helps to reduce the size of data to be classified. The selection criteria are based on class separability. We propose a system based on SVM classification with the selection of characteristics to classify a brain tumor using three types of 3D MRI images. Our system can follow-up the evolution of a tumor along a therapeutic treatment.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121022265","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586752
Doaa Youssef, N. Solouma, Amr El-dib, Mai Mabrouk, Abo-Bakr Youssef
Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and wet macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the exudates, the blood vessels, the optic disc and the macula and the region between them. The earlier the detection of exudates in fundus images, the stronger the kept sight level. So, early detection of exudates in fundus images is of great importance for early diagnosis and proper treatment. In this paper, we provide a feature-based method for early detection of exudates. The method is based on segmenting all objects that have contrast with the background including the exudates. The exudates could then be extracted after eliminating the other objects from the image. We proposed a new method for extracting the blood vessel tree based on simple morphological operations. The circular structure of the optic disc is obtained using Hough transform. The regions representing the blood vessel tree and the optic disc are set to zero in the segmented image to get an initial estimate of exudates. The final estimation of exudates are obtained by morphological reconstruction. This method is shown to be promising as we can detect the very small areas of exudates.
{"title":"New feature-based detection of blood vessels and exudates in color fundus images","authors":"Doaa Youssef, N. Solouma, Amr El-dib, Mai Mabrouk, Abo-Bakr Youssef","doi":"10.1109/IPTA.2010.5586752","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586752","url":null,"abstract":"Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and wet macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the exudates, the blood vessels, the optic disc and the macula and the region between them. The earlier the detection of exudates in fundus images, the stronger the kept sight level. So, early detection of exudates in fundus images is of great importance for early diagnosis and proper treatment. In this paper, we provide a feature-based method for early detection of exudates. The method is based on segmenting all objects that have contrast with the background including the exudates. The exudates could then be extracted after eliminating the other objects from the image. We proposed a new method for extracting the blood vessel tree based on simple morphological operations. The circular structure of the optic disc is obtained using Hough transform. The regions representing the blood vessel tree and the optic disc are set to zero in the segmented image to get an initial estimate of exudates. The final estimation of exudates are obtained by morphological reconstruction. This method is shown to be promising as we can detect the very small areas of exudates.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126007368","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586784
F. Morain-Nicolier, Jérôme Landré, S. Ruan
This communication deals with finding the position of a reference shape in a given image. The proposed matcher is constructed from local dissimilarity maps. These maps allow to efficiently and robustly measure the differences between two images. It is shown an example that the matcher potentially returns less false-positives than a reference method (chamfer matching). This is possible as the local dissimilarity measure is symmetric, which makes it more robust to noise. We show that the proposed matcher is a generalization of the chamfer matching. It also allows fast computation times. A good robustness to noise is confirmed from presented simulations.
{"title":"Binary pattern matching from a local dissimilarity measure","authors":"F. Morain-Nicolier, Jérôme Landré, S. Ruan","doi":"10.1109/IPTA.2010.5586784","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586784","url":null,"abstract":"This communication deals with finding the position of a reference shape in a given image. The proposed matcher is constructed from local dissimilarity maps. These maps allow to efficiently and robustly measure the differences between two images. It is shown an example that the matcher potentially returns less false-positives than a reference method (chamfer matching). This is possible as the local dissimilarity measure is symmetric, which makes it more robust to noise. We show that the proposed matcher is a generalization of the chamfer matching. It also allows fast computation times. A good robustness to noise is confirmed from presented simulations.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125034186","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586774
Qing Liu, Sun'an Wang, Xiaohui Zhang, Yun Hou
Based on the feature of CCD image forming, the internal principle of image forming is analyzed, and the loss of charge transfer is calculated by the Shockley - Read - Hall equation, in which the distribution function between the charge transfer is reconstructed. Rational polynomial interpolation algorithm is used to determine the unknown pixel points for the adjacent pixels that do not overcome the loss of charge transfer to enhance the image. It is an self-adaptive interpolation algorithm, in which the interpolation function can be adjusted automatically with electrical potential difference of the adjoining pixels and its energy zone, by means of which, the image can be magnified self-adaptively. Remote sensing image is tested, and the example results show that not only the image quality is improved, but also the clear margin and contour information is kept with this algorithm. And thus the processed images are more conducive to the naked eye.
{"title":"Improvement of the space resolution of the optical remote sensing image by the principle of CCD imaging","authors":"Qing Liu, Sun'an Wang, Xiaohui Zhang, Yun Hou","doi":"10.1109/IPTA.2010.5586774","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586774","url":null,"abstract":"Based on the feature of CCD image forming, the internal principle of image forming is analyzed, and the loss of charge transfer is calculated by the Shockley - Read - Hall equation, in which the distribution function between the charge transfer is reconstructed. Rational polynomial interpolation algorithm is used to determine the unknown pixel points for the adjacent pixels that do not overcome the loss of charge transfer to enhance the image. It is an self-adaptive interpolation algorithm, in which the interpolation function can be adjusted automatically with electrical potential difference of the adjoining pixels and its energy zone, by means of which, the image can be magnified self-adaptively. Remote sensing image is tested, and the example results show that not only the image quality is improved, but also the clear margin and contour information is kept with this algorithm. And thus the processed images are more conducive to the naked eye.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128504125","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586799
F. Renard, V. Noblet, A. Grigis, C. Heinrich, S. Kremer
Diffusion Magnetic Resonance Imaging (DMRI) is an emerging technique permitting to visualize the neuronal architecture of brain white matter by measuring the diffusion of water molecules in tissues. A DMRI acquisition is composed of a collection of diffusion weighted images (DWIs) that characterize the diffusion property in several noncolinear directions. Resampling such acquisitions to obtain measures of diffusion in other directions is a problem that may arise when registering or comparing DWIs. In this paper, we present a comparison of several spherical interpolation schemes for DWIs. Numerical experiments are achieved on both synthetic and real data.
{"title":"Comparison of interpolation methods for angular resampling of diffusion weighted images","authors":"F. Renard, V. Noblet, A. Grigis, C. Heinrich, S. Kremer","doi":"10.1109/IPTA.2010.5586799","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586799","url":null,"abstract":"Diffusion Magnetic Resonance Imaging (DMRI) is an emerging technique permitting to visualize the neuronal architecture of brain white matter by measuring the diffusion of water molecules in tissues. A DMRI acquisition is composed of a collection of diffusion weighted images (DWIs) that characterize the diffusion property in several noncolinear directions. Resampling such acquisitions to obtain measures of diffusion in other directions is a problem that may arise when registering or comparing DWIs. In this paper, we present a comparison of several spherical interpolation schemes for DWIs. Numerical experiments are achieved on both synthetic and real data.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127472093","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 : 2010-07-07DOI: 10.1109/IPTA.2010.5586769
Kamal Nasrollahi, T. Moeslund
Super resolution algorithms are necessary for improving the quality of low resolution video sequences from surveillance cameras. These algorithms have two main problems: first, they hardly can improve the quality of their inputs by factors bigger than two. Second, applying them to real video sequences usually produces unstable and noisy output. The proposed system in this paper deals with these two problems. The latter, which is due to the unavoidable registration errors of video sequences, is dealt with by using a face quality assessment technique. A combination of different types of super resolution algorithms in a hybrid system is used to cope with the former. The system is tested using real world videos from uncontrolled environments and the results are promising.
{"title":"Hybrid super resolution using refined face logs","authors":"Kamal Nasrollahi, T. Moeslund","doi":"10.1109/IPTA.2010.5586769","DOIUrl":"https://doi.org/10.1109/IPTA.2010.5586769","url":null,"abstract":"Super resolution algorithms are necessary for improving the quality of low resolution video sequences from surveillance cameras. These algorithms have two main problems: first, they hardly can improve the quality of their inputs by factors bigger than two. Second, applying them to real video sequences usually produces unstable and noisy output. The proposed system in this paper deals with these two problems. The latter, which is due to the unavoidable registration errors of video sequences, is dealt with by using a face quality assessment technique. A combination of different types of super resolution algorithms in a hybrid system is used to cope with the former. The system is tested using real world videos from uncontrolled environments and the results are promising.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116753605","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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