Pub Date : 2011-04-11DOI: 10.1109/CIMI.2011.5952041
S. Lahmiri, M. Boukadoum
A new classification system for brain images obtained by magnetic resonance imaging (MRI) is presented. A three-stage approach is used for its design. It consists of second-level discrete wavelet transform decomposition of the image under study, feature extraction from the LH and HL sub-bands using first order statistics, and subsequent classification with the k-nearest neighbor (k-NN), learning vector quantization (LVQ), and probabilistic neural networks (PNN) algorithms. Then, an ensemble classifier system is developed where the previous machines form the base classifiers and support vector machines (SVM) are employed to aggregate decisions. The proposed approach was tested on a bank of normal and pathological MRIs and the obtained results show a higher performance overall than when using features extracted from the LL sub-band, as usually done, leading to the conclusion that the horizontal and vertical sub-bands of the wavelet transform can effectively and efficiently encode the discriminating features of normal and pathological images. The experimental results also show that using an ensemble classifier improves the correct classification rates.
{"title":"Brain MRI classification using an ensemble system and LH and HL wavelet sub-bands features","authors":"S. Lahmiri, M. Boukadoum","doi":"10.1109/CIMI.2011.5952041","DOIUrl":"https://doi.org/10.1109/CIMI.2011.5952041","url":null,"abstract":"A new classification system for brain images obtained by magnetic resonance imaging (MRI) is presented. A three-stage approach is used for its design. It consists of second-level discrete wavelet transform decomposition of the image under study, feature extraction from the LH and HL sub-bands using first order statistics, and subsequent classification with the k-nearest neighbor (k-NN), learning vector quantization (LVQ), and probabilistic neural networks (PNN) algorithms. Then, an ensemble classifier system is developed where the previous machines form the base classifiers and support vector machines (SVM) are employed to aggregate decisions. The proposed approach was tested on a bank of normal and pathological MRIs and the obtained results show a higher performance overall than when using features extracted from the LL sub-band, as usually done, leading to the conclusion that the horizontal and vertical sub-bands of the wavelet transform can effectively and efficiently encode the discriminating features of normal and pathological images. The experimental results also show that using an ensemble classifier improves the correct classification rates.","PeriodicalId":314088,"journal":{"name":"2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114082687","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 : 2011-04-11DOI: 10.1109/CIMI.2011.5952040
Ana G. Salazar-Gonzalez, Yongmin Li, Xiaohui Liu
Glaucoma is one of the main causes of blindness worldwide. Segmentation of vascular system and optic disc is an important step in the development of an automatic retinal screening system. In this paper we present an unsupervised method for the optic disc segmentation. The main obstruction in the optic disc segmentation process is the presence of blood vessels breaking the continuity of the object. While many other methods have addressed this problem trying to eliminate the vessels, we have incorporated the blood vessel information into our formulation. The blood vessel inside of the optic disc are used to give continuity to the object to segment. Our approach is based on the graph cut technique, where the graph is constructed considering the relationship between neighboring pixels and by the likelihood of them belonging to the foreground and background from prior information. Our method was tested on two public datasets, DIARETDB1 and DRIVE. The performance of our method was measured by calculating the overlapping ratio (Oratio), sensitivity and the mean absolute distance (MAD) with respect to the manually labeled images. Experimental results demonstrate that our method outperforms other methods on these datasets.
{"title":"Optic disc segmentation by incorporating blood vessel compensation","authors":"Ana G. Salazar-Gonzalez, Yongmin Li, Xiaohui Liu","doi":"10.1109/CIMI.2011.5952040","DOIUrl":"https://doi.org/10.1109/CIMI.2011.5952040","url":null,"abstract":"Glaucoma is one of the main causes of blindness worldwide. Segmentation of vascular system and optic disc is an important step in the development of an automatic retinal screening system. In this paper we present an unsupervised method for the optic disc segmentation. The main obstruction in the optic disc segmentation process is the presence of blood vessels breaking the continuity of the object. While many other methods have addressed this problem trying to eliminate the vessels, we have incorporated the blood vessel information into our formulation. The blood vessel inside of the optic disc are used to give continuity to the object to segment. Our approach is based on the graph cut technique, where the graph is constructed considering the relationship between neighboring pixels and by the likelihood of them belonging to the foreground and background from prior information. Our method was tested on two public datasets, DIARETDB1 and DRIVE. The performance of our method was measured by calculating the overlapping ratio (Oratio), sensitivity and the mean absolute distance (MAD) with respect to the manually labeled images. Experimental results demonstrate that our method outperforms other methods on these datasets.","PeriodicalId":314088,"journal":{"name":"2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132335041","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 : 2011-04-11DOI: 10.1109/CIMI.2011.5952043
E. Shahamatnia, M. Ebadzadeh
Active contour model has been widely used in image processing applications such as boundary delineation, image segmentation, stereo matching, shape recognition and object tracking. In this paper a novel particle swarm optimization scheme has been introduced to evolve snake over time in a way to reduce time complexity while improving quality of results. Traditional active contour models converge slowly and are prone to local minima due to their complex nature. Various evolutionary techniques including genetic algorithms, particle swarm optimization and predator prey optimization have been successfully employed to tackle this problem. Most of these methods are general problem solvers that, more or less, formulate the snake model equations as a minimization problem and try to optimize it. In contrary, our proposed approach integrates concepts from active contour model into particle swarm optimization so that each particle will represent a snaxel of the active contour. Canonical velocity update equation in particle swarm algorithm is modified to embrace the snake kinematics. This new model makes it possible to have advantages of swarm based searching strategies and active contour principles all together. Aptness of the proposed approach has been examined through several experiments on synthetic and real world images of CT and MRI images of brain and the results demonstrate its promising performance particularly in handling boundary concavities and snake initialization problems.
{"title":"Application of particle swarm optimization and snake model hybrid on medical imaging","authors":"E. Shahamatnia, M. Ebadzadeh","doi":"10.1109/CIMI.2011.5952043","DOIUrl":"https://doi.org/10.1109/CIMI.2011.5952043","url":null,"abstract":"Active contour model has been widely used in image processing applications such as boundary delineation, image segmentation, stereo matching, shape recognition and object tracking. In this paper a novel particle swarm optimization scheme has been introduced to evolve snake over time in a way to reduce time complexity while improving quality of results. Traditional active contour models converge slowly and are prone to local minima due to their complex nature. Various evolutionary techniques including genetic algorithms, particle swarm optimization and predator prey optimization have been successfully employed to tackle this problem. Most of these methods are general problem solvers that, more or less, formulate the snake model equations as a minimization problem and try to optimize it. In contrary, our proposed approach integrates concepts from active contour model into particle swarm optimization so that each particle will represent a snaxel of the active contour. Canonical velocity update equation in particle swarm algorithm is modified to embrace the snake kinematics. This new model makes it possible to have advantages of swarm based searching strategies and active contour principles all together. Aptness of the proposed approach has been examined through several experiments on synthetic and real world images of CT and MRI images of brain and the results demonstrate its promising performance particularly in handling boundary concavities and snake initialization problems.","PeriodicalId":314088,"journal":{"name":"2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124269934","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 : 2011-04-11DOI: 10.1109/CIMI.2011.5952044
S. Damas, O. Cordón, J. Santamaría
In the last few decades, image registration (IR) has been established as a very active research area in computer vision. Over the years, it has been applied to a broad range of real-world problems ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. IR has been usually tackled by iterative approaches considering numerical optimization methods which are likely to get stuck in local optima. Recently, a large number of IR methods based on the use of metaheuristics and evolutionary computation paradigms has been proposed providing outstanding results. In this contribution, we aim to develop a preliminary experimental study on some of the most recognized feature-based IR methods considering evolutionary algorithms. To do so, the IR framework is first presented and a brief description of some prominent evolutionary-based IR proposals are reviewed. Finally, a selection of some of the most representative methods are benchmarked facing challenging 3D medical image registration problem instances.
{"title":"Evaluation of various evolutionary methods for medical image registration","authors":"S. Damas, O. Cordón, J. Santamaría","doi":"10.1109/CIMI.2011.5952044","DOIUrl":"https://doi.org/10.1109/CIMI.2011.5952044","url":null,"abstract":"In the last few decades, image registration (IR) has been established as a very active research area in computer vision. Over the years, it has been applied to a broad range of real-world problems ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. IR has been usually tackled by iterative approaches considering numerical optimization methods which are likely to get stuck in local optima. Recently, a large number of IR methods based on the use of metaheuristics and evolutionary computation paradigms has been proposed providing outstanding results. In this contribution, we aim to develop a preliminary experimental study on some of the most recognized feature-based IR methods considering evolutionary algorithms. To do so, the IR framework is first presented and a brief description of some prominent evolutionary-based IR proposals are reviewed. Finally, a selection of some of the most representative methods are benchmarked facing challenging 3D medical image registration problem instances.","PeriodicalId":314088,"journal":{"name":"2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125986236","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 : 2011-04-11DOI: 10.1109/CIMI.2011.5952042
Hedi Yazid, Karim Kalti, N. Amara, F. Elouni, K. Tlili
We propose in this paper a bayesian network based similarity measure for the retrieving of magnetic resonance imaging exams containing cerebral tumors. Bayesian networks proved their efficiency and reliability in several Artificial Intelligence problems and especially in computer aided decision applications. To diagnose a cerebral tumor in a MRI exam, we need to interpret diverse sequences and to refer to visual characteristics and, also, to the patient clinical information such as age, sex, other diseases, etc. Our main idea is argued by the uncertain aspect embodied of the decision making process. This aspect will be translated as a probabilistic decision model. Our work is tested on several medical cases collected from Sahloul Hospital. The retrieval results seem to be promising.
{"title":"A probabilistic network based similiarity measure for cerebral tumors MRI cases retrieval","authors":"Hedi Yazid, Karim Kalti, N. Amara, F. Elouni, K. Tlili","doi":"10.1109/CIMI.2011.5952042","DOIUrl":"https://doi.org/10.1109/CIMI.2011.5952042","url":null,"abstract":"We propose in this paper a bayesian network based similarity measure for the retrieving of magnetic resonance imaging exams containing cerebral tumors. Bayesian networks proved their efficiency and reliability in several Artificial Intelligence problems and especially in computer aided decision applications. To diagnose a cerebral tumor in a MRI exam, we need to interpret diverse sequences and to refer to visual characteristics and, also, to the patient clinical information such as age, sex, other diseases, etc. Our main idea is argued by the uncertain aspect embodied of the decision making process. This aspect will be translated as a probabilistic decision model. Our work is tested on several medical cases collected from Sahloul Hospital. The retrieval results seem to be promising.","PeriodicalId":314088,"journal":{"name":"2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134432302","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：