Proceedings of IEEE Southeastcon. IEEE Southeastcon最新文献

Specular Reflection Removal for 3D Reconstruction of Tissues using Endoscopy Videos. 利用内窥镜视频进行组织三维重建的镜面反射去除。

Proceedings of IEEE Southeastcon. IEEE Southeastcon Pub Date : 2023-04-01 Epub Date: 2023-05-08 DOI: 10.1109/southeastcon51012.2023.10115137

Muhammad Emaduddin, Tansel Halic, Doga Demirel, Coskun Bayrak, Venkata S Arikatla, Suvranu De

{"title":"Specular Reflection Removal for 3D Reconstruction of Tissues using Endoscopy Videos.","authors":"Muhammad Emaduddin, Tansel Halic, Doga Demirel, Coskun Bayrak, Venkata S Arikatla, Suvranu De","doi":"10.1109/southeastcon51012.2023.10115137","DOIUrl":"https://doi.org/10.1109/southeastcon51012.2023.10115137","url":null,"abstract":"Endoscopy is widely employed for diagnostic examination of the interior of organs and body cavities and numerous surgical interventions. Still, the inability to correlate individual 2D images with 3D organ morphology limits its applications, especially in intra-operative planning and navigation, disease physiology, cancer surveillance, etc. As a result, most endoscopy videos, which carry enormous data potential, are used only for real-time guidance and are discarded after collection. We present a complete method for the 3D reconstruction of inner organs that suggests image extraction techniques from endoscopic videos and a novel image pre-processing technique to reconstruct and visualize a 3D model of organs from an endoscopic video. We use advanced computer vision methods and do not require any modifications to the clinical-grade endoscopy hardware. We have also formalized an image acquisition protocol through experimentation with a calibrated test bed. We validate the accuracy and robustness of our reconstruction using a test bed with known ground truth. Our method can significantly contribute to endoscopy-based diagnostic and surgical procedures using comprehensive tissue and tumor 3D visualization.","PeriodicalId":90950,"journal":{"name":"Proceedings of IEEE Southeastcon. IEEE Southeastcon","volume":"2023 ","pages":"246-252"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Reduction Solution for Driving Simulator. 驾驶模拟器数据缩减解决方案。

Proceedings of IEEE Southeastcon. IEEE Southeastcon Pub Date : 2020-03-01 Epub Date: 2020-11-16 DOI: 10.1109/SoutheastCon44009.2020.9249691

Piyush Pawar, Thomas Anthony, Benjamin McManus, Despina Stavrinos

引用次数: 0

Autofocusing for Microscopic Images using Harris Corner Response Measure. 使用哈里斯角响应测量的显微图像自动对焦。

Proceedings of IEEE Southeastcon. IEEE Southeastcon Pub Date : 2014-03-01 DOI: 10.1109/SECON.2014.6950754

Madhu S Sigdel, Madhav Sigdel, Semih Dinç, İmren Dinç, Marc L Pusey, Ramazan S Aygün

引用次数: 2

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery. 半监督学习对蛋白质结晶图像分类的评价。

Proceedings of IEEE Southeastcon. IEEE Southeastcon Pub Date : 2014-03-01 DOI: 10.1109/SECON.2014.6950649

Madhav Sigdel, İmren Dinç, Semih Dinç, Madhu S Sigdel, Marc L Pusey, Ramazan S Aygün

{"title":"Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.","authors":"Madhav Sigdel, İmren Dinç, Semih Dinç, Madhu S Sigdel, Marc L Pusey, Ramazan S Aygün","doi":"10.1109/SECON.2014.6950649","DOIUrl":"https://doi.org/10.1109/SECON.2014.6950649","url":null,"abstract":"In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.","PeriodicalId":90950,"journal":{"name":"Proceedings of IEEE Southeastcon. IEEE Southeastcon","volume":"2014 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/SECON.2014.6950649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33252071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images. 归一化和主成分分析对蛋白质结晶图像分类器性能的评价。

Proceedings of IEEE Southeastcon. IEEE Southeastcon Pub Date : 2014-03-01 DOI: 10.1109/SECON.2014.6950744

İmren Dinç, Madhav Sigdel, Semih Dinç, Madhu S Sigdel, Marc L Pusey, Ramazan S Aygün

{"title":"Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images.","authors":"İmren Dinç, Madhav Sigdel, Semih Dinç, Madhu S Sigdel, Marc L Pusey, Ramazan S Aygün","doi":"10.1109/SECON.2014.6950744","DOIUrl":"https://doi.org/10.1109/SECON.2014.6950744","url":null,"abstract":"In this paper, we investigate the performance of classification of protein crystallization images captured during protein crystal growth process. We group protein crystallization images into 3 categories: noncrystals, likely leads (conditions that may yield formation of crystals) and crystals. In this research, we only consider the subcategories of noncrystal and likely leads protein crystallization images separately. We use 5 different classifiers to solve this problem and we applied some data preprocessing methods such as principal component analysis (PCA), min-max (MM) normalization and z-score (ZS) normalization methods to our datasets in order to evaluate their effects on classifiers for the noncrystal and likely leads datasets. We performed our experiments on 1606 noncrystal and 245 likely leads images independently. We had satisfactory results for both datasets. We reached 96.8% accuracy for noncrystal dataset and 94.8% accuracy for likely leads dataset. Our target is to investigate the best classifiers with optimal preprocessing techniques on both noncrystal and likely leads datasets.","PeriodicalId":90950,"journal":{"name":"Proceedings of IEEE Southeastcon. IEEE Southeastcon","volume":"2014 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/SECON.2014.6950744","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33252072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18