Pub Date : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231641
Mayssa Tayachi, Laurent Nana, F. Benzarti, A. Pascu
In this paper, we come up with a new watermarking method combining a zero-watermarking approach with a non-zero-watermarking approach for the purpose of authentication and identification of medical DICOM images, as well as the confidentiality of patient information. Indeed, the storage and transmission of medical images need strong confidentiality, authentication and integrity. In the proposed approach, pertinent features extracted from the DICOM image are used, on the one hand, for the zero-watermarking based on the Jacobian model, and on the other hand, to construct a watermark embedded in the black background region using linear interpolation technique. The watermark is encapsulated only in the black background region in order to avoid affecting the anatomical part whose modification may cause a wrong diagnosis. The main contribution of this paper is the method used to generate a robust watermark based on the Jacobian model and pertinent features extracted from the medical image. Experimental results reveal the quality of the method proposed: high PSNR (peak signal to noise ratio) and SSIM (similarity structure index measure) values as well as high robustness against different attacks.
{"title":"A Dual Watermarking Approach for DICOM Images","authors":"Mayssa Tayachi, Laurent Nana, F. Benzarti, A. Pascu","doi":"10.1109/ATSIP49331.2020.9231641","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231641","url":null,"abstract":"In this paper, we come up with a new watermarking method combining a zero-watermarking approach with a non-zero-watermarking approach for the purpose of authentication and identification of medical DICOM images, as well as the confidentiality of patient information. Indeed, the storage and transmission of medical images need strong confidentiality, authentication and integrity. In the proposed approach, pertinent features extracted from the DICOM image are used, on the one hand, for the zero-watermarking based on the Jacobian model, and on the other hand, to construct a watermark embedded in the black background region using linear interpolation technique. The watermark is encapsulated only in the black background region in order to avoid affecting the anatomical part whose modification may cause a wrong diagnosis. The main contribution of this paper is the method used to generate a robust watermark based on the Jacobian model and pertinent features extracted from the medical image. Experimental results reveal the quality of the method proposed: high PSNR (peak signal to noise ratio) and SSIM (similarity structure index measure) values as well as high robustness against different attacks.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114905361","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 : 2020-09-01DOI: 10.1109/atsip49331.2020.9231837
{"title":"ATSIP 2020 Front Matters","authors":"","doi":"10.1109/atsip49331.2020.9231837","DOIUrl":"https://doi.org/10.1109/atsip49331.2020.9231837","url":null,"abstract":"","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132560877","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 : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231731
Asmae Touil, Karim Kalti, Pierre-Henri Conze, B. Solaiman, M. Mahjoub
In this paper, we propose a new morphological-based method for automatic detection of microcalcifications in digitized mammograms. It uses various structuring elements to deal with the diversity of microcalcification characteristics. The obtained morphological maps are converted to a continuous suspicion map (SM) based on the structural similarity index (SSIM). This new semantic representation map is then locally analyzed, using superpixels, to automatically estimate adaptive threshold values and finally identify potential microcalcification areas. The proposed method was evaluated using the publicly-available INBreast database. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to some state-of-the-art methods.
{"title":"Morphological-based microcalcification detection using adaptive thresholding and structural similarity indices","authors":"Asmae Touil, Karim Kalti, Pierre-Henri Conze, B. Solaiman, M. Mahjoub","doi":"10.1109/ATSIP49331.2020.9231731","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231731","url":null,"abstract":"In this paper, we propose a new morphological-based method for automatic detection of microcalcifications in digitized mammograms. It uses various structuring elements to deal with the diversity of microcalcification characteristics. The obtained morphological maps are converted to a continuous suspicion map (SM) based on the structural similarity index (SSIM). This new semantic representation map is then locally analyzed, using superpixels, to automatically estimate adaptive threshold values and finally identify potential microcalcification areas. The proposed method was evaluated using the publicly-available INBreast database. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to some state-of-the-art methods.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133050061","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 : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231870
Salim Klibi, Kais Tounsi, Zouhaier Ben Rabah, B. Solaiman, I. Farah
A major environmental threat is soil salinity caused by natural and human-induced processes. Therefore, soil salinity status monitoring is required to ensure sustainable land use and management. Hyperspectral satellite images can make a significant contribution to the detection of soil salinity. The increase in production in semi-arid and arid regions such as Zaghouan in the northeast of Tunisia requires good soil management because this resource is a determining factor for agricultural production. This paper aims to predict soil salinity in this area using spectral signature and features vector of the Hyperion hyperspectral image. The AutoEncoder (AE) is one of neural network architectures that were adopted for feature representation. Support Vector Machines (SVM), K-Nearest-Neighbors (KNN) and Decision Tree (DT) were used for the classification. Results showed that the AE-SVM combination outperforms among the three other approaches used for soil salinity prediction.
{"title":"Soil salinity prediction using a machine learning approach through hyperspectral satellite image","authors":"Salim Klibi, Kais Tounsi, Zouhaier Ben Rabah, B. Solaiman, I. Farah","doi":"10.1109/ATSIP49331.2020.9231870","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231870","url":null,"abstract":"A major environmental threat is soil salinity caused by natural and human-induced processes. Therefore, soil salinity status monitoring is required to ensure sustainable land use and management. Hyperspectral satellite images can make a significant contribution to the detection of soil salinity. The increase in production in semi-arid and arid regions such as Zaghouan in the northeast of Tunisia requires good soil management because this resource is a determining factor for agricultural production. This paper aims to predict soil salinity in this area using spectral signature and features vector of the Hyperion hyperspectral image. The AutoEncoder (AE) is one of neural network architectures that were adopted for feature representation. Support Vector Machines (SVM), K-Nearest-Neighbors (KNN) and Decision Tree (DT) were used for the classification. Results showed that the AE-SVM combination outperforms among the three other approaches used for soil salinity prediction.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116766651","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 : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231712
Bilel Tarchoun, Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, M. Mahjoub
Autonomous driving systems and driver assistance systems are becoming the center of attention in transport technology. Given its safety criticality, pedestrian detection is a highly important task. Transport oriented intelligent systems use embedded sensors for the detection task. However, vehicle side detection is starting to show its limitations especially when dealing with certain challenges such as occlusions. In this paper, we propose an infrastructure side perception system that has a bird’s eye view. We introduce a new deep pedestrian detector that can use the detection results to warn nearby vehicles of the presence of pedestrians on the road. The results show that our proposed system is able to detect pedestrians in most conditions with 70.41% precision and 69.17% recall.
{"title":"Deep CNN-based Pedestrian Detection for Intelligent Infrastructure","authors":"Bilel Tarchoun, Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, M. Mahjoub","doi":"10.1109/ATSIP49331.2020.9231712","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231712","url":null,"abstract":"Autonomous driving systems and driver assistance systems are becoming the center of attention in transport technology. Given its safety criticality, pedestrian detection is a highly important task. Transport oriented intelligent systems use embedded sensors for the detection task. However, vehicle side detection is starting to show its limitations especially when dealing with certain challenges such as occlusions. In this paper, we propose an infrastructure side perception system that has a bird’s eye view. We introduce a new deep pedestrian detector that can use the detection results to warn nearby vehicles of the presence of pedestrians on the road. The results show that our proposed system is able to detect pedestrians in most conditions with 70.41% precision and 69.17% recall.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115699804","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 : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231706
A. Oueslati, Nadia Feddaoui, S. Belghith, K. Hamrouni
this paper presents one of our contributions in our palm vein recognition system, this contribution consists on region of interest extraction (ROI)in images obtained by near infrared. The proposed ROI extraction is find using a new process to find the candidate key points then a square based on perpendicular lines algorithme is used to detect the region of interest, our method is performed at 99% of correct segmentation rate.
{"title":"An Efficient palm vein Region of Interest extraction method","authors":"A. Oueslati, Nadia Feddaoui, S. Belghith, K. Hamrouni","doi":"10.1109/ATSIP49331.2020.9231706","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231706","url":null,"abstract":"this paper presents one of our contributions in our palm vein recognition system, this contribution consists on region of interest extraction (ROI)in images obtained by near infrared. The proposed ROI extraction is find using a new process to find the candidate key points then a square based on perpendicular lines algorithme is used to detect the region of interest, our method is performed at 99% of correct segmentation rate.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115729457","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 : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231862
I. Montassar, A. Benazza-Benyahia
This paper tackles the problem of estimating water turbidity by analyzing images. This computer-vision solution avoids to resort to use specific laboratory instruments and, hence facilitates the water characterization in situ. Our contribution consists in designing a whole image processing chain composed of pre-processing, segmentation, feature extraction and classification modules. The second originality of our work relies on comparing two dual approaches for the segmentation and feature extraction: handcrafted and deep neural network based approaches. Finally, the lack of a publicly available dataset has motivated the building of an appropriate dataset. Experimental results indicate satisfactory performances of the proposed approaches.
{"title":"Water turbidity estimation in water sampled images","authors":"I. Montassar, A. Benazza-Benyahia","doi":"10.1109/ATSIP49331.2020.9231862","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231862","url":null,"abstract":"This paper tackles the problem of estimating water turbidity by analyzing images. This computer-vision solution avoids to resort to use specific laboratory instruments and, hence facilitates the water characterization in situ. Our contribution consists in designing a whole image processing chain composed of pre-processing, segmentation, feature extraction and classification modules. The second originality of our work relies on comparing two dual approaches for the segmentation and feature extraction: handcrafted and deep neural network based approaches. Finally, the lack of a publicly available dataset has motivated the building of an appropriate dataset. Experimental results indicate satisfactory performances of the proposed approaches.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127166932","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 : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231782
Abderrazek Zeraii, I. Alaya, M. Mars, C. Drissi, T. Kraiem
Diffusion weighted imaging (DWI-MRI) is debatably the method of choice for characterizing brain microstructure non-invasively and in vivo in the chronic phase post-stroke. In fact, the degree of motor impairment after stroke is closely linked to the structural integrity of the corticospinal tract (CST). The aim of our study was to extract tract biophysical characteristics such as fractional anisotropy (FA) and Apparent Diffusion Coefficient (ADC) from the CST and to identify the optimal parameters of measuring CST integrity. We conduct experiments with ten healthy human subjects. For each subject, biophysical values were calculated for two Regions of Interest (the posterior limb of the internal capsule and the anterior pons) at two b-value (b=1000s/mm2 and b=3000 s/mm2). In this work, our results showed that the pons region more accurately predict CST integrity than the posterior limb of internal capsule using a b-value equal to 1000 s/mm2. FA and ADC are a promising metric for clinical applications.
{"title":"Optimal Parameters of Diffusion MRI measuring Corticospinal Tract Integrity in healthy subjects","authors":"Abderrazek Zeraii, I. Alaya, M. Mars, C. Drissi, T. Kraiem","doi":"10.1109/ATSIP49331.2020.9231782","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231782","url":null,"abstract":"Diffusion weighted imaging (DWI-MRI) is debatably the method of choice for characterizing brain microstructure non-invasively and in vivo in the chronic phase post-stroke. In fact, the degree of motor impairment after stroke is closely linked to the structural integrity of the corticospinal tract (CST). The aim of our study was to extract tract biophysical characteristics such as fractional anisotropy (FA) and Apparent Diffusion Coefficient (ADC) from the CST and to identify the optimal parameters of measuring CST integrity. We conduct experiments with ten healthy human subjects. For each subject, biophysical values were calculated for two Regions of Interest (the posterior limb of the internal capsule and the anterior pons) at two b-value (b=1000s/mm2 and b=3000 s/mm2). In this work, our results showed that the pons region more accurately predict CST integrity than the posterior limb of internal capsule using a b-value equal to 1000 s/mm2. FA and ADC are a promising metric for clinical applications.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126604905","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 : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231545
R. Baklouti, A. Hamida, M. Mansouri, H. Nounou, M. Nounou
Fault Detection (FD) is a fundamental step in process monitoring. Owning to its simplicity and effectiveness to deal with nonlinear and highly correlated process variables, kernel principal component analysis (KPCA) has been successfully used in process monitoring. However, the major drawback of this method-based kernel generalized likelihood ratio test (KGLRT) is the neglect of small faults. Inspired by the effectiveness of this detection metric and motivated by the advantages of the univariate exponentially weighted movng average (EWMA), we propose, in this paper, a KPCA-based EWMA-KGLRT FD algorithm. Hence, its performance is illustrated and compared to the conventional KPCA-based KGLRT method through continuously simulated tank reactor (CSTR). In fact, the experimental results confirmed the performance of the proposed algorithm in terms of missed detection (MD) and false alarm (FA) rates.
{"title":"EWMA Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes","authors":"R. Baklouti, A. Hamida, M. Mansouri, H. Nounou, M. Nounou","doi":"10.1109/ATSIP49331.2020.9231545","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231545","url":null,"abstract":"Fault Detection (FD) is a fundamental step in process monitoring. Owning to its simplicity and effectiveness to deal with nonlinear and highly correlated process variables, kernel principal component analysis (KPCA) has been successfully used in process monitoring. However, the major drawback of this method-based kernel generalized likelihood ratio test (KGLRT) is the neglect of small faults. Inspired by the effectiveness of this detection metric and motivated by the advantages of the univariate exponentially weighted movng average (EWMA), we propose, in this paper, a KPCA-based EWMA-KGLRT FD algorithm. Hence, its performance is illustrated and compared to the conventional KPCA-based KGLRT method through continuously simulated tank reactor (CSTR). In fact, the experimental results confirmed the performance of the proposed algorithm in terms of missed detection (MD) and false alarm (FA) rates.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127006409","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 : 2020-09-01DOI: 10.1109/ATSIP49331.2020.9231701
Ibtissem Hadj Ali, M. Mahjoub
An essential issue for the recognition of handwritten mathematical formulas is the identification of the structural relationships between each pairs of adjacent symbols that compose the entire mathematical formula. The classification of the structural relationship is a key problem as this classification often determines the semantic interpretation of an expression. In this work, we propose a system for the identification of spatial relationships based on geometric features and a new descriptor named spatial histogram. After the combination of extracted features, we classify the relationship into six different classes using four different classifiers in order to determine the most efficient. In our proposed system, a support vector machine (SVM) classifier, Random Forest, Adaboost and KNN are employed. Experimental results show that our features give promising results.
{"title":"Structure relationship classification for the recognition of mathematical expression handwritten in Arabic","authors":"Ibtissem Hadj Ali, M. Mahjoub","doi":"10.1109/ATSIP49331.2020.9231701","DOIUrl":"https://doi.org/10.1109/ATSIP49331.2020.9231701","url":null,"abstract":"An essential issue for the recognition of handwritten mathematical formulas is the identification of the structural relationships between each pairs of adjacent symbols that compose the entire mathematical formula. The classification of the structural relationship is a key problem as this classification often determines the semantic interpretation of an expression. In this work, we propose a system for the identification of spatial relationships based on geometric features and a new descriptor named spatial histogram. After the combination of extracted features, we classify the relationship into six different classes using four different classifiers in order to determine the most efficient. In our proposed system, a support vector machine (SVM) classifier, Random Forest, Adaboost and KNN are employed. Experimental results show that our features give promising results.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125838268","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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