J. Mitra, A. Oliver, R. Martí, X. Lladó, J. Vilanova, F. Mériaudeau
{"title":"A Thin-Plate Spline Based Multimodal Prostate Registration with Optimal Correspondences","authors":"J. Mitra, A. Oliver, R. Martí, X. Lladó, J. Vilanova, F. Mériaudeau","doi":"10.1109/SITIS.2010.12","DOIUrl":"https://doi.org/10.1109/SITIS.2010.12","url":null,"abstract":"Accurate extraction of prostate biopsy samples during Transectal Ultra Sound (TRUS) guided prostate biopsy is facilitated with the registration of pre-acquired Magnetic Resonance (MR) images with the Ultrasound (US) images. This paper proposes a novel method of generating optimal correspondences to register the MR and US images using Thin-Plate Splines (TPS) transformation. The correspondence generation method exploits the prostate shape geometry in both the modalities and is fully automatic. Normalized Mutual Information (NMI) is employed for the quantitative determination of optimal number of correspondences in terms of maximization of registration similarity. Qualitative registration results, that conform to the NMI measures are also shown for different numbers of correspondences. Shepard’s interpolation method is used with the TPS in order to deal with the interpolation error of backward TPS transformation. The accuracy of our method of correspondence generation is qualitatively evaluated in comparison with two intuitive geometric contour sampling methods. An average Dice Similarity Coefficient (DSC) value of 0.97 ± 0.01 for 4 patient datasets is obtained for the TPS registration using our novel method of correspondences.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"413 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122482496","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}
S. Ghose, A. Oliver, R. Martí, X. Lladó, J. Freixenet, J. Vilanova, F. Mériaudeau
{"title":"Prostate Segmentation with Texture Enhanced Active Appearance Model","authors":"S. Ghose, A. Oliver, R. Martí, X. Lladó, J. Freixenet, J. Vilanova, F. Mériaudeau","doi":"10.1109/SITIS.2010.14","DOIUrl":"https://doi.org/10.1109/SITIS.2010.14","url":null,"abstract":"Prostate contour segmented from Trans Rectal Ultra Sound (TRUS) and Magnetic Resonance (MR) images could improve inter-modality registration accuracy and reduce computational complexity of the procedure. However, prostate segmentation in each of these modalities is a challenging task in presence of imaging artifacts, intensity heterogeneities, and large inter patient shape variabilities of the prostate. We propose to use Haar wavelet approximation coefficients to extract texture features of the prostate region in both modalities to guide a deformable parametric model to segment the prostate in a multi-resolution framework. Principal Component Analysis (PCA) of the shape and texture information of the prostate region obtained from the training data aids contour propagation of the deformable parametric model. Prior knowledge of the optimization space is utilized for optimal segmentation of the prostate. Our method achieves a mean Dice Similarity Coefficient (DSC) value of 0.95±0.01, with mean segmentation time of 0.72±0.05 seconds in a leave-one-out validation framework with 25 TRUS images grabbed from a video sequence. DSC value of 0.88 ± 0.06 with a mean segmentation time of 0.81 ± 0.02 seconds was recorded for MR images when validated with 15 central slice images of 15 datasets from the MICCAI prostate segmentation challenge 2009. Our proposed method performs computationally efficient accurate multi-modal prostate segmentation in presence of intensity heterogeneities and imaging artifacts.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129046503","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}
{"title":"Effective Feature Selection for Face Recognition Based on Correspondence Analysis and Trained Artificial Neural Network","authors":"Z. Pazoki, F. Farokhi","doi":"10.1109/SITIS.2010.23","DOIUrl":"https://doi.org/10.1109/SITIS.2010.23","url":null,"abstract":"this paper presents a face recognition method based on correspondence analysis (CA) and trained artificial neural network. In this algorithm, features are extracted using CA, then these features are fed to Multi layer Perceptron (MLP)network for classification and finally, after training the network, effective features are selected with UTA algorithm. The obtained experimental results indicate high average accuracy (98%) and the minimum run time of the algorithm as well.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123475008","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}
{"title":"The Comparison of Different Classifiers for Precision Improvement in Image Retrieval","authors":"M. S. Lotfabadi, Rezvam Mahmoudie","doi":"10.1109/SITIS.2010.39","DOIUrl":"https://doi.org/10.1109/SITIS.2010.39","url":null,"abstract":"In many researches, valuable studies have been done for feature extraction from images data-base, but because of weak classifiers using, good results have not been achieved. In this paper, different classifiers are compared in order to increase image retrieval system precision. Five different classifiers are used in the paper: the support vector-machine, the MLP neural network, the K-nearest neighbor, the rough neural network, and the rough fuzzy neural network. The rough fuzzy neural network and the rough neural network have not been used in image retrieval implication up to now. The innovation of this research is the using of these classifiers in the image retrieval implication. From the performed test, it is concluded that the rough fuzzy neural network classifier has performed better than other classifiers and increased the image retrieval precision. The COREL image data-base with 1000 images in ten content groups has been used and the classifiers have been compared.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115487783","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}
Yoshitaka Sakurai, K. Takada, Takashi Kawabe, S. Tsuruta
{"title":"Biological Sensor Fusion Using Sensor Reliability Evaluation for Situation Assesment","authors":"Yoshitaka Sakurai, K. Takada, Takashi Kawabe, S. Tsuruta","doi":"10.1109/SITIS.2010.28","DOIUrl":"https://doi.org/10.1109/SITIS.2010.28","url":null,"abstract":"In Web-based CSCW (Computer-Supported Cooperative Work), remote members communicate their intentions in cyberspace. However, different from face-to-face communication, partners' situations including their interest, concentration, boredom, and tiredness cannot be easily transmitted. Oversight and mishearing of remote partners is often overlooked. Besides, it is further difficult to understand their real intentions sufficiently. To overcome these problems, biological sensor fusion for dependable Web-based CSCW Systems is proposed. This assesses or estimates situations of remote users through fusing information of multiple biological sensors and the related general contexts. By transmitting and using information of estimated usersf situations, the system augments the cyberspace through stressing or providing warnings by multimedia. This paper clarifies the mechanism of sensor fusion engine that uses probabilistic or statistical data to increase estimation reliability and can learn from log data of feature vectors or symptoms to decrease knowledge acquisition bottleneck. The proposed method aims at improving reliability of situation estimation by evaluating the reliability and feed-backing the evaluation results by information requests. The reliability of the output on each process layer is evaluated based on the past log and related information. Based on the evaluated reliability, estimated situations are improved by information request to related sensors.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131950601","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}
Yoshitaka Sakurai, K. Takada, Natsuki Tsukamoto, T. Onoyama, R. Knauf, S. Tsuruta
{"title":"Backtrack and Restart Genetic Algorithm to Optimize Delivery Schedule","authors":"Yoshitaka Sakurai, K. Takada, Natsuki Tsukamoto, T. Onoyama, R. Knauf, S. Tsuruta","doi":"10.1109/SITIS.2010.24","DOIUrl":"https://doi.org/10.1109/SITIS.2010.24","url":null,"abstract":"A delivery route optimization system greatly improves the real time delivery efficiency. To realize such an optimization, its distribution network requires solving several tens to hundreds (max. 1500-2000) cities Traveling Salesman Problems (TSP) within interactive response time (around 3 seconds) with expert-level accuracy (below 3% level of error rate). To meet these requirements, a Backtrack and Restart Genetic Algorithm (Br-GA) is proposed and compared with conventional ones, especially such as an Inner Random Restart Genetic Algorithm (Irr-GA). This method combines Backtracking and GA having simple heuristics such as 2-opt and NI (Nearest Insertion) so that, in case of stagflation, GA can restarts with the state of populations going back to the state in the generation before stagflation. Including these heuristics, field experts and field engineers can easily understand the way and use it. Using the tool applying their method, they can easily create/modify the solutions or conditions interactively depending on their field needs. Experimental results proved that the method meets the above-mentioned delivery scheduling requirements more than other methods from the viewpoint of optimality as well as simplicity. Especially as to optimality, Br-GA is superior to even Irr-GA.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"32 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113944564","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}
{"title":"Multiple Birth and Cut Algorithm for Point Process Optimization","authors":"A. Gamal-Eldin, X. Descombes, J. Zerubia","doi":"10.1109/SITIS.2010.17","DOIUrl":"https://doi.org/10.1109/SITIS.2010.17","url":null,"abstract":"In this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to the energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing the advantages and disadvantages, where the most important advantage is the reduction of the number of parameters. We validated our algorithm on the counting problem of flamingos in colony, where our algorithm outperforms the performance of the MBD algorithm.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125645297","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}
{"title":"Thermal Physiological Moment Invariants for Face Identification","authors":"K. Abas, O. Ono","doi":"10.1109/SITIS.2010.11","DOIUrl":"https://doi.org/10.1109/SITIS.2010.11","url":null,"abstract":"This paper presents the extension of our previous work on moment invariants with respect to centroid point (obtained from frontal mugs hot images) for thermal-based face identification system. In previous work, seeded region growing method was applied for background filtering. Sequentially, the system decomposes a background filtered thermal image into 4 thermal regions via 3-valued threshold method. In the extension of the pre-processes, we employed anis tropic diffusion between these two steps. This step aids in decreasing the effect of noise. Furthermore, previously we only considered frontal mugs hot images as registered images, therefore, in this paper we include mid-profiles and left and right profile in the registered dataset. This is to overcome for images with angular pose that exceeds 45 degrees to the left and right. Due to multiple angular pose available in the registered dataset, customized and simple pose estimation is introduced in this paper. The pose estimation utilizes information previously obtained during the pre-processes, thus avoiding additional steps that is required if current available pose estimation methods were to be employed. This system employs minimum distance measurement method for classification purposes. The encouraging performance of this system is represented by a cumulative match characteristic curve (also known as the CMC curve) at the end of this paper.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127821300","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}
{"title":"A Preliminary Study to Reduce the Missing Wedge Effect by Using a Noise Robust Mojette Reconstruction","authors":"B. Recur, P. Desbarats, J. Domenger","doi":"10.1109/SITIS.2010.33","DOIUrl":"https://doi.org/10.1109/SITIS.2010.33","url":null,"abstract":"Apart from the usual methods based on the Radon theorem, the Mojette transform proposes a specific algorithm called Corner Based Inversion (CBI) to reconstruct an image from its projections. Contrary to other transforms, it offers two interesting properties. First, the acquisition follows discrete image geometry and resolves the well-known irregular sampling problem. Second, it updates projection values during the reconstruction such that the sinogram contains only data for not yet reconstructed pixels. These properties could be a solution to reduce the missing wedge effect in tomography. Unfortunately, the CBI algorithm is noise sensitive and reconstruction from corrupted data fails. In this paper, we first develop and optimize a noise-robust CBI algorithm based on data redundancy and noise modelling in the projections. Afterwards, this algorithm is applied in discrete tomography from a specific Radon acquisition. Reconstructed image results are discussed and applications and perspectives to reduce the missing wedge effect are also developed.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133934919","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}
{"title":"Starfruit Image Segmentation Based on YCbCr Color Space","authors":"R. Amirulah, M. Mokji, Z. Ibrahim, U. U. Sheikh","doi":"10.1109/SITIS.2010.36","DOIUrl":"https://doi.org/10.1109/SITIS.2010.36","url":null,"abstract":"Segmentation in a classification system plays an important role as it will determine the region of interest for the classification. In this work, the segmentation of star fruit is performed in YCbCr color space. Because of the background of the image to be classify has 2 different colors, which are black and white, the segmentation is obtained using a fixed threshold value for the Cb component. This is because the value for Cb is equivalent in black, white or gray scale pixels. By fixing the threshold for Cb component, the region of interest (ROI) can easily be determined and then proceeded to the classification process. The results in this paper show that the ROI can be determined completely by this method.","PeriodicalId":128396,"journal":{"name":"2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130313763","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}