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Proceedings. International Conference on Image Processing最新文献

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DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS. 基于深度学习的电子冷冻子图监督语义分割。
Proceedings. International Conference on Image Processing Pub Date : 2018-10-01 Epub Date: 2018-09-06 DOI: 10.1109/icip.2018.8451386
Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric Xing, Min Xu
{"title":"DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS.","authors":"Chang Liu,&nbsp;Xiangrui Zeng,&nbsp;Ruogu Lin,&nbsp;Xiaodan Liang,&nbsp;Zachary Freyberg,&nbsp;Eric Xing,&nbsp;Min Xu","doi":"10.1109/icip.2018.8451386","DOIUrl":"10.1109/icip.2018.8451386","url":null,"abstract":"<p><p>Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree of structural complexity and practical imaging limitations, systematic macromolecular structural recovery inside CECT images remains challenging. Particularly, the recovery of a macromolecule is likely to be biased by its neighbor structures due to the high molecular crowding. To reduce the bias, here we introduce a novel 3D convolutional neural network inspired by Fully Convolutional Network and Encoder-Decoder Architecture for the supervised segmentation of macromolecules of interest in subtomograms. The tests of our models on realistically simulated CECT data demonstrate that our new approach has significantly improved segmentation performance compared to our baseline approach. Also, we demonstrate that the proposed model has generalization ability to segment new structures that do not exist in training data.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2018 ","pages":"1578-1582"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icip.2018.8451386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41143083","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: 14
WEAKLY SUPERVISED FOOD IMAGE SEGMENTATION USING CLASS ACTIVATION MAPS. 使用类激活映射的弱监督食物图像分割。
Proceedings. International Conference on Image Processing Pub Date : 2017-09-01 Epub Date: 2018-02-22 DOI: 10.1109/ICIP.2017.8296487
Yu Wang, Fengqing Zhu, Carol J Boushey, Edward J Delp
{"title":"WEAKLY SUPERVISED FOOD IMAGE SEGMENTATION USING CLASS ACTIVATION MAPS.","authors":"Yu Wang, Fengqing Zhu, Carol J Boushey, Edward J Delp","doi":"10.1109/ICIP.2017.8296487","DOIUrl":"10.1109/ICIP.2017.8296487","url":null,"abstract":"<p><p>Food image segmentation plays a crucial role in image-based dietary assessment and management. Successful methods for object segmentation generally rely on a large amount of labeled data on the pixel level. However, such training data are not yet available for food images and expensive to obtain. In this paper, we describe a weakly supervised convolutional neural network (CNN) which only requires image level annotation. We propose a graph based segmentation method which uses the class activation maps trained on food datasets as a top-down saliency model. We evaluate the proposed method for both classification and segmentation tasks. We achieve competitive classification accuracy compared to the previously reported results.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2017 ","pages":"1277-1281"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6226049/pdf/nihms-995023.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36654725","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
STRUCTURED LOW-RANK RECOVERY OF PIECEWISE CONSTANT SIGNALS WITH PERFORMANCE GUARANTEES. 具有性能保证的片常数信号结构化低阶恢复。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/icip.2016.7532500
Greg Ongie, Sampurna Biswas, Mathews Jacob
{"title":"STRUCTURED LOW-RANK RECOVERY OF PIECEWISE CONSTANT SIGNALS WITH PERFORMANCE GUARANTEES.","authors":"Greg Ongie, Sampurna Biswas, Mathews Jacob","doi":"10.1109/icip.2016.7532500","DOIUrl":"10.1109/icip.2016.7532500","url":null,"abstract":"<p><p>We derive theoretical guarantees for the exact recovery of piecewise constant two-dimensional images from a minimal number of non-uniform Fourier samples using a convex matrix completion algorithm. We assume the discontinuities of the image are localized to the zero level-set of a bandlimited function, which induces certain linear dependencies in Fourier domain, such that a multifold Toeplitz matrix built from the Fourier data is known to be low-rank. The recovery algorithm arranges the known Fourier samples into the structured matrix then attempts recovery of the missing Fourier data by minimizing the nuclear norm subject to structure and data constraints. This work adapts results by Chen and Chi on the recovery of isolated Diracs via nuclear norm minimization of a similar multifold Hankel structure. We show that exact recovery is possible with high probability when the bandlimited function describing the edge set satisfies an incoherency property. Finally, we demonstrate the algorithm on the recovery of undersampled MRI data.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"963-967"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985822/pdf/nihms-1667938.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25523731","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
DISJUNCTIVE NORMAL LEVEL SET: AN EFFICIENT PARAMETRIC IMPLICIT METHOD. 析取正态水平集:一种有效的参数隐式方法。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/ICIP.2016.7533171
Fitsum Mesadi, Mujdat Cetin, Tolga Tasdizen
{"title":"DISJUNCTIVE NORMAL LEVEL SET: AN EFFICIENT PARAMETRIC IMPLICIT METHOD.","authors":"Fitsum Mesadi,&nbsp;Mujdat Cetin,&nbsp;Tolga Tasdizen","doi":"10.1109/ICIP.2016.7533171","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533171","url":null,"abstract":"<p><p>Level set methods are widely used for image segmentation because of their capability to handle topological changes. In this paper, we propose a novel parametric level set method called Disjunctive Normal Level Set (DNLS), and apply it to both two phase (single object) and multiphase (multi-object) image segmentations. The DNLS is formed by union of polytopes which themselves are formed by intersections of half-spaces. The proposed level set framework has the following major advantages compared to other level set methods available in the literature. First, segmentation using DNLS converges much faster. Second, the DNLS level set function remains regular throughout its evolution. Third, the proposed multiphase version of the DNLS is less sensitive to initialization, and its computational cost and memory requirement remains almost constant as the number of objects to be simultaneously segmented grows. The experimental results show the potential of the proposed method.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"4299-4303"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2016.7533171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34899067","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: 5
EFFICIENT SUPERPIXEL BASED SEGMENTATION FOR FOOD IMAGE ANALYSIS. 基于超像素的高效食品图像分割分析。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-12-08 DOI: 10.1109/ICIP.2016.7532818
Yu Wang, Chang Liu, Fengqing Zhu, Carol J Boushey, Edward J Delp
{"title":"EFFICIENT SUPERPIXEL BASED SEGMENTATION FOR FOOD IMAGE ANALYSIS.","authors":"Yu Wang,&nbsp;Chang Liu,&nbsp;Fengqing Zhu,&nbsp;Carol J Boushey,&nbsp;Edward J Delp","doi":"10.1109/ICIP.2016.7532818","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7532818","url":null,"abstract":"<p><p>In this paper, we propose a segmentation method based on normalized cut and superpixels. The method relies on color and texture cues for fast computation and efficient use of memory. The method is used for food image segmentation as part of a mobile food record system we have developed for dietary assessment and management. The accurate estimate of nutrients relies on correctly labelled food items and sufficiently well-segmented regions. Our method achieves competitive results using the Berkeley Segmentation Dataset and outperforms some of the most popular techniques in a food image dataset.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"2544-2548"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2016.7532818","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36654756","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: 15
ACCELERATED DYNAMIC MRI USING STRUCTURED LOW RANK MATRIX COMPLETION. 利用结构化低秩矩阵补全加速动态成像。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/icip.2016.7532680
Arvind Balachandrasekaran, Greg Ongie, Mathews Jacob
{"title":"ACCELERATED DYNAMIC MRI USING STRUCTURED LOW RANK MATRIX COMPLETION.","authors":"Arvind Balachandrasekaran, Greg Ongie, Mathews Jacob","doi":"10.1109/icip.2016.7532680","DOIUrl":"10.1109/icip.2016.7532680","url":null,"abstract":"<p><p>We introduce a fast structured low-rank matrix completion algorithm with low memory & computational demand to recover the dynamic MRI data from undersampled measurements. The 3-D dataset is modeled as a piecewise smooth signal, whose discontinuities are localized to the zero sets of a bandlimited function. We show that a structured matrix corresponding to convolution with the Fourier coefficients of the signal derivatives is highly low-rank. This property enables us to recover the signal from undersampled measurements. The application of this scheme in dynamic MRI shows significant improvement over state of the art methods.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"1858-1862"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885618/pdf/nihms-1667948.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25377815","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
CONVEX CLUSTERING AND RECOVERY OF PARTIALLY OBSERVED DATA. 部分观测数据的凸聚类和恢复。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/icip.2016.7533010
Sunrita Poddar, Mathews Jacob
{"title":"CONVEX CLUSTERING AND RECOVERY OF PARTIALLY OBSERVED DATA.","authors":"Sunrita Poddar,&nbsp;Mathews Jacob","doi":"10.1109/icip.2016.7533010","DOIUrl":"https://doi.org/10.1109/icip.2016.7533010","url":null,"abstract":"<p><p>We propose a convex clustering and reconstruction algorithm for data with missing entries. The algorithm uses a similarity measure between every pair of points to cluster and recover the data. The cluster centres can be recovered reliably when the ground-truth similarity matrix is available. Moreover, the similarity matrix can also be reliably estimated from the partially observed data, when the clusters are well-separated and the coherence of the difference between points from different clusters is low. The algorithm performs well using the estimated similarity matrix on a simulated dataset. The method is also successful in reconstructing images from under-sampled Fourier data.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"3498-3502"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icip.2016.7533010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25395794","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: 1
Multiple Degree Total Variation (MDTV) Regularization for Image Restoration. 多度全变分(MDTV)正则化用于图像恢复。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/icip.2016.7532700
Yue Hu, Mathews Jacob
{"title":"Multiple Degree Total Variation (MDTV) Regularization for Image Restoration.","authors":"Yue Hu,&nbsp;Mathews Jacob","doi":"10.1109/icip.2016.7532700","DOIUrl":"https://doi.org/10.1109/icip.2016.7532700","url":null,"abstract":"<p><p>We introduce a novel image regularization termed as multiple degree total variation (MDTV). This type of regularization combines the first and second degree directional derivatives, thus providing a good balance between preservation of edges and region smoothness. In order to solve the resulting optimization problem, we proposed a fast majorize minimize algorithm. We demonstrate the utility of the MDTV regularization in the context of image denoising and compressed sensing. We compare the proposed method with standard TV, and the state of the art higher degree methods, including higher degree total variation (HDTV) and total generalized variation (TGV) based schemes. Numerical results indicate that MDTV penalty provides improved image recovery performance.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"1958-1962"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icip.2016.7532700","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25408395","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: 3
A COMPARISON OF FOOD PORTION SIZE ESTIMATION USING GEOMETRIC MODELS AND DEPTH IMAGES. 使用几何模型和深度图像的食物分量大小估计的比较。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-12-08 DOI: 10.1109/ICIP.2016.7532312
Shaobo Fang, Fengqing Zhu, Chufan Jiang, Song Zhang, Carol J Boushey, Edward J Delp
{"title":"A COMPARISON OF FOOD PORTION SIZE ESTIMATION USING GEOMETRIC MODELS AND DEPTH IMAGES.","authors":"Shaobo Fang,&nbsp;Fengqing Zhu,&nbsp;Chufan Jiang,&nbsp;Song Zhang,&nbsp;Carol J Boushey,&nbsp;Edward J Delp","doi":"10.1109/ICIP.2016.7532312","DOIUrl":"10.1109/ICIP.2016.7532312","url":null,"abstract":"<p><p>Six of the ten leading causes of death in the United States, including cancer, diabetes, and heart disease, can be directly linked to diet. Dietary intake, the process of determining what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many of the above chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper we compare two techniques to estimating food portion size from images of food. The techniques are based on 3D geometric models and depth images. An expectation-maximization based technique is developed to detect the reference plane in depth images, which is essential for portion size estimation using depth images. Our experimental results indicate that volume estimation based on geometric model is more accurate for objects with well-defined 3D shapes compared to estimation using depth images.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"26-30"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2016.7532312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36654755","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: 32
AN AUTOMATIC 3D CT/PET SEGMENTATION FRAMEWORK FOR BONE MARROW PROLIFERATION ASSESSMENT. 用于骨髓增殖评估的自动三维ct / pet分割框架。
Proceedings. International Conference on Image Processing Pub Date : 2016-09-01 Epub Date: 2016-08-19 DOI: 10.1109/ICIP.2016.7533136
Chuong Nguyen, Joseph Havlicek, Quyen Duong, Sara Vesely, Ronald Gress, Liza Lindenberg, Peter Choyke, Jennifer Holter Chakrabarty, Kirsten Williams
{"title":"AN AUTOMATIC 3D CT/PET SEGMENTATION FRAMEWORK FOR BONE MARROW PROLIFERATION ASSESSMENT.","authors":"Chuong Nguyen,&nbsp;Joseph Havlicek,&nbsp;Quyen Duong,&nbsp;Sara Vesely,&nbsp;Ronald Gress,&nbsp;Liza Lindenberg,&nbsp;Peter Choyke,&nbsp;Jennifer Holter Chakrabarty,&nbsp;Kirsten Williams","doi":"10.1109/ICIP.2016.7533136","DOIUrl":"https://doi.org/10.1109/ICIP.2016.7533136","url":null,"abstract":"<p><p>Clinical assessment of bone marrow is limited by an inability to evaluate the marrow space comprehensively and dynamically and there is no current method for automatically assessing hematopoietic activity within the medullary space. Evaluating the hematopoietic space in its entirety could be applicable in blood disorders, malignancies, infections, and medication toxicity. In this paper, we introduce a CT/PET 3D automatic framework for measurement of the hematopoietic compartment proliferation within osseous sites. We first perform a full-body bone structure segmentation using 3D graph-cut on the CT volume. The vertebrae are segmented by detecting the discs between adjacent vertebrae. Finally, we register the bone marrow CT volume with its corresponding PET volume and capture the spinal bone marrow volume. The proposed framework was tested on 17 patients, achieving an average accuracy of 86.37% and a worst case accuracy of 82.3% in automatically extracting the aggregate volume of the spinal marrow cavities.</p>","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"2016 ","pages":"4126-4130"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICIP.2016.7533136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35112981","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: 4
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