Murat Dundar - Academia.edu (original) (raw)
Papers by Murat Dundar
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
ABSTRACT
2009 Ieee International Geoscience and Remote Sensing Symposium, 2009
ABSTRACT In challenging environments, in order to uniquely define a sample as a target, multiple ... more ABSTRACT In challenging environments, in order to uniquely define a sample as a target, multiple representations of the samples might be required. As a case study, we consider cars in the parking lots of an urban imagery as targets. What makes this problem challenging is the copresence of several parking garages and parking lots in the same imagery. Both the cars in the parking lots and in the parking garages present with similar spectral characteristics. Spectral representation alone is not sufficient to uniquely define a pixel as a car in the parking lot. In this example, before a pixel is confirmed as a target or rejected as not being a target, classifiers corresponding to spectral and spatial representations of the samples has to concord. The current study discusses some possible ways these classifiers can be trained so that the rate of true concordance is maximized. We consider independent training and feature concatenation first and then propose a joint optimization scheme. The proposed approach aims to optimize multiple classifiers at once so as to maximize concordance among the classifiers while minimizing the classification error.
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
ABSTRACT In challenging environments, in order to uniquely define a sample as a target, multiple ... more ABSTRACT In challenging environments, in order to uniquely define a sample as a target, multiple representations of the samples might be required. As a case study, we consider cars in the parking lots of an urban imagery as targets. What makes this problem challenging is the copresence of several parking garages and parking lots in the same imagery. Both the cars in the parking lots and in the parking garages present with similar spectral characteristics. Spectral representation alone is not sufficient to uniquely define a pixel as a car in the parking lot. In this example, before a pixel is confirmed as a target or rejected as not being a target, classifiers corresponding to spectral and spatial representations of the samples has to concord. The current study discusses some possible ways these classifiers can be trained so that the rate of true concordance is maximized. We consider independent training and feature concatenation first and then propose a joint optimization scheme. The proposed approach aims to optimize multiple classifiers at once so as to maximize concordance among the classifiers while minimizing the classification error.
Kernel Methods for Remote Sensing Data Analysis, 2009
2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006
ABSTRACT We describe a modification of the standard support vector machine (SVM) classifier that ... more ABSTRACT We describe a modification of the standard support vector machine (SVM) classifier that exploits the tendency for spatially contiguous pixels to be similarly classified. A quadratic term characterizing the spatial correlations in a multispectral image is added into the standard SVM optimization criterion. The mathematical structure of the SVM programming problem is retained, and the solution can be expressed in terms of the ordinary SVM solution with a modified dot product. The spatial correlations are characterized by a "contiguity matrix" psi whose computation does not require labeled data; thus, the method provides a way to use a mix of labeled and unlabeled data. We present numerical comparisons of classification performance for this contiguity-enhanced SVM against a standard SVM for two multispectral data sets.
Advances in neural information processing systems
PURPOSE The purpose of this study was that of evaluating the sensitivity and generalization capab... more PURPOSE The purpose of this study was that of evaluating the sensitivity and generalization capability of a new computer aided detection (CAD) algorithm in CT colonoscopy applied to datasets from multiple sites. METHOD AND MATERIALS The database of high-resolution CT images was obtained from NYU Medical Center (number of patients (n) =105, positives=61), Cleveland Clinic Foundation (n=42, positives= 9), and two EU sites in Vienna and Belgium (n=16, positives=15). The sensitivity was established with respect to CTC by comparison to colonoscopy. CT data was acquired using a Volume Zoom CT scanner 4x1mm slice detector and a Sensation16, all with 120kV, and exposure from 50 to110mAs, with B10f & B30f kernels. The slice thickness ranged between 1.25mm, 1.5mm & 2.0 mm with reconstruction interval of values between 0.7mm and 1.2mm and axial resolution between 0.54mm & 1.2mm. The CAD algorithm performed: candidate generation, based on shape characteristics; feature extraction at each candid...
PURPOSE Evaluate the performance of a module for suppression of false positive CAD marks located ... more PURPOSE Evaluate the performance of a module for suppression of false positive CAD marks located on the ileo cecal valve (ICV) in a prototype polyp detection system when applied to clean or tagged CTC datasets METHOD AND MATERIALS The ICV detection component uses 5 steps: a) detecting the ICV orifice leveraging its distinctive local curvature profile, b) a rough estimation of its orientation by aligning it with the local gradients at a given location. Estimation of the c) position, d) size of the ICV and e) refining the orientation is calculated by performing marginal space learning. In each of the 5 steps, all potential orifice candidates are evaluated using different classifiers. The top 100 candidates with maximal orifice probabilities are selected for further parameter estimation and searching. The number of selected candidates is set to maintain a good trade-off between detection accuracy and speed. The position of 116 manually marked ICV orifices were used to generate a traini...
PURPOSE To evaluate the accuracy of a prototype CAD system in CT Colonography (CTC) on a database... more PURPOSE To evaluate the accuracy of a prototype CAD system in CT Colonography (CTC) on a database of 1245 cases from multiple sites with clean and tagged preparation METHOD AND MATERIALS The database contains 1245 high-resolution MDCT datasets (545 clean and 700 tagged) partitioned randomly into training (208 clean and 169 tagged) and testing (337 clean and 531 tagged) sets. Data was obtained from 10 sites in the US and Europe, acquired using different CT scanner types from different manufacturers (4, 16, and 64 detectors), with a wide range of acquisition parameters. Axial spacing ranged from 0.48-0.97mm; reconstruction interval from 0.5 -4mm; exposure from 10 -200mAs. A majority of cases were acquired with 120 or 130 kVP. All available cases, including those with poor preparation, distention, and artifacts, were included. The test set also had data from sites not used in the training set. The CAD system was optimized to provide best sensitivity for polyps >= 6mm in extent RESUL...
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
... USA 51 Valley Stream Parkway Malvern, PA 19355 murat.dundar@siemens.com Jinbo Bi Knowledge So... more ... USA 51 Valley Stream Parkway Malvern, PA 19355 murat.dundar@siemens.com Jinbo Bi Knowledge Solutions and CAD Siemens Medical Solutions Inc. ... cpu time per can-didate, and was used together with the feature set 1 and CG features to learn the second classifier. ...
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2009
Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremend... more Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3D medical imaging. It is a prerequisite for all CAD systems to extract the air-distended colon segments from 3D abdomen computed tomography scans. In this paper, we present a two-level statistical approach of first separating colon segments from small intestine, stomach and other extra-colonic parts by classification on a new geometric feature set; then evaluating the overall performance confidence using distance and geometry statistics over patients. The proposed method is fully automatic and validated using both the classification results in the first level and its numerical impacts on false positive reduction of extra-colonic findings in a CAD system. It shows superior performance than the state-of-art knowledge or anatomy based colon segmentation algorithms.
Lecture Notes in Computer Science, 2008
Fifth IEEE International Conference on Data Mining (ICDM'05), 2005
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
ABSTRACT
2009 Ieee International Geoscience and Remote Sensing Symposium, 2009
ABSTRACT In challenging environments, in order to uniquely define a sample as a target, multiple ... more ABSTRACT In challenging environments, in order to uniquely define a sample as a target, multiple representations of the samples might be required. As a case study, we consider cars in the parking lots of an urban imagery as targets. What makes this problem challenging is the copresence of several parking garages and parking lots in the same imagery. Both the cars in the parking lots and in the parking garages present with similar spectral characteristics. Spectral representation alone is not sufficient to uniquely define a pixel as a car in the parking lot. In this example, before a pixel is confirmed as a target or rejected as not being a target, classifiers corresponding to spectral and spatial representations of the samples has to concord. The current study discusses some possible ways these classifiers can be trained so that the rate of true concordance is maximized. We consider independent training and feature concatenation first and then propose a joint optimization scheme. The proposed approach aims to optimize multiple classifiers at once so as to maximize concordance among the classifiers while minimizing the classification error.
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
ABSTRACT In challenging environments, in order to uniquely define a sample as a target, multiple ... more ABSTRACT In challenging environments, in order to uniquely define a sample as a target, multiple representations of the samples might be required. As a case study, we consider cars in the parking lots of an urban imagery as targets. What makes this problem challenging is the copresence of several parking garages and parking lots in the same imagery. Both the cars in the parking lots and in the parking garages present with similar spectral characteristics. Spectral representation alone is not sufficient to uniquely define a pixel as a car in the parking lot. In this example, before a pixel is confirmed as a target or rejected as not being a target, classifiers corresponding to spectral and spatial representations of the samples has to concord. The current study discusses some possible ways these classifiers can be trained so that the rate of true concordance is maximized. We consider independent training and feature concatenation first and then propose a joint optimization scheme. The proposed approach aims to optimize multiple classifiers at once so as to maximize concordance among the classifiers while minimizing the classification error.
Kernel Methods for Remote Sensing Data Analysis, 2009
2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006
ABSTRACT We describe a modification of the standard support vector machine (SVM) classifier that ... more ABSTRACT We describe a modification of the standard support vector machine (SVM) classifier that exploits the tendency for spatially contiguous pixels to be similarly classified. A quadratic term characterizing the spatial correlations in a multispectral image is added into the standard SVM optimization criterion. The mathematical structure of the SVM programming problem is retained, and the solution can be expressed in terms of the ordinary SVM solution with a modified dot product. The spatial correlations are characterized by a "contiguity matrix" psi whose computation does not require labeled data; thus, the method provides a way to use a mix of labeled and unlabeled data. We present numerical comparisons of classification performance for this contiguity-enhanced SVM against a standard SVM for two multispectral data sets.
Advances in neural information processing systems
PURPOSE The purpose of this study was that of evaluating the sensitivity and generalization capab... more PURPOSE The purpose of this study was that of evaluating the sensitivity and generalization capability of a new computer aided detection (CAD) algorithm in CT colonoscopy applied to datasets from multiple sites. METHOD AND MATERIALS The database of high-resolution CT images was obtained from NYU Medical Center (number of patients (n) =105, positives=61), Cleveland Clinic Foundation (n=42, positives= 9), and two EU sites in Vienna and Belgium (n=16, positives=15). The sensitivity was established with respect to CTC by comparison to colonoscopy. CT data was acquired using a Volume Zoom CT scanner 4x1mm slice detector and a Sensation16, all with 120kV, and exposure from 50 to110mAs, with B10f & B30f kernels. The slice thickness ranged between 1.25mm, 1.5mm & 2.0 mm with reconstruction interval of values between 0.7mm and 1.2mm and axial resolution between 0.54mm & 1.2mm. The CAD algorithm performed: candidate generation, based on shape characteristics; feature extraction at each candid...
PURPOSE Evaluate the performance of a module for suppression of false positive CAD marks located ... more PURPOSE Evaluate the performance of a module for suppression of false positive CAD marks located on the ileo cecal valve (ICV) in a prototype polyp detection system when applied to clean or tagged CTC datasets METHOD AND MATERIALS The ICV detection component uses 5 steps: a) detecting the ICV orifice leveraging its distinctive local curvature profile, b) a rough estimation of its orientation by aligning it with the local gradients at a given location. Estimation of the c) position, d) size of the ICV and e) refining the orientation is calculated by performing marginal space learning. In each of the 5 steps, all potential orifice candidates are evaluated using different classifiers. The top 100 candidates with maximal orifice probabilities are selected for further parameter estimation and searching. The number of selected candidates is set to maintain a good trade-off between detection accuracy and speed. The position of 116 manually marked ICV orifices were used to generate a traini...
PURPOSE To evaluate the accuracy of a prototype CAD system in CT Colonography (CTC) on a database... more PURPOSE To evaluate the accuracy of a prototype CAD system in CT Colonography (CTC) on a database of 1245 cases from multiple sites with clean and tagged preparation METHOD AND MATERIALS The database contains 1245 high-resolution MDCT datasets (545 clean and 700 tagged) partitioned randomly into training (208 clean and 169 tagged) and testing (337 clean and 531 tagged) sets. Data was obtained from 10 sites in the US and Europe, acquired using different CT scanner types from different manufacturers (4, 16, and 64 detectors), with a wide range of acquisition parameters. Axial spacing ranged from 0.48-0.97mm; reconstruction interval from 0.5 -4mm; exposure from 10 -200mAs. A majority of cases were acquired with 120 or 130 kVP. All available cases, including those with poor preparation, distention, and artifacts, were included. The test set also had data from sites not used in the training set. The CAD system was optimized to provide best sensitivity for polyps >= 6mm in extent RESUL...
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
... USA 51 Valley Stream Parkway Malvern, PA 19355 murat.dundar@siemens.com Jinbo Bi Knowledge So... more ... USA 51 Valley Stream Parkway Malvern, PA 19355 murat.dundar@siemens.com Jinbo Bi Knowledge Solutions and CAD Siemens Medical Solutions Inc. ... cpu time per can-didate, and was used together with the feature set 1 and CG features to learn the second classifier. ...
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2009
Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremend... more Computer aided detection (CAD) of colonic polyps in computed tomographic colonography has tremendously impacted colorectal cancer diagnosis using 3D medical imaging. It is a prerequisite for all CAD systems to extract the air-distended colon segments from 3D abdomen computed tomography scans. In this paper, we present a two-level statistical approach of first separating colon segments from small intestine, stomach and other extra-colonic parts by classification on a new geometric feature set; then evaluating the overall performance confidence using distance and geometry statistics over patients. The proposed method is fully automatic and validated using both the classification results in the first level and its numerical impacts on false positive reduction of extra-colonic findings in a CAD system. It shows superior performance than the state-of-art knowledge or anatomy based colon segmentation algorithms.
Lecture Notes in Computer Science, 2008
Fifth IEEE International Conference on Data Mining (ICDM'05), 2005