Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490074
J. Kärcher, Volker J Schmid
In this paper, we propose a compartment model with two interstitial space compartments for the quantitative description of the contrast medium kinetics in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The model accounts for heterogeneity of contrast medium uptake behavior within voxels and thus more appropriately describes the uptake behavior in malignant tissue, especially at tumor margins. The posterior distribution obtained with a Bayesian approach provides valuable information on model fit and complexity as well as criteria for model selection. We propose a model selection technique to choose between the proposed two compartment model and the standard one compartment model per voxel. Results are evaluated for simulated and in vivo data.
{"title":"Two tissue compartment model in DCE-MRI: A bayesian approach","authors":"J. Kärcher, Volker J Schmid","doi":"10.1109/ISBI.2010.5490074","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490074","url":null,"abstract":"In this paper, we propose a compartment model with two interstitial space compartments for the quantitative description of the contrast medium kinetics in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The model accounts for heterogeneity of contrast medium uptake behavior within voxels and thus more appropriately describes the uptake behavior in malignant tissue, especially at tumor margins. The posterior distribution obtained with a Bayesian approach provides valuable information on model fit and complexity as well as criteria for model selection. We propose a model selection technique to choose between the proposed two compartment model and the standard one compartment model per voxel. Results are evaluated for simulated and in vivo data.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114928881","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490372
Howard Zhou, James M. Rehg, Mei Chen
Automated segmentation of pigmented skin lesions (PSLs) from dermoscopy images is an important step for computer-aided diagnosis of skin cancer. The segmentation task involves classifying each image pixel as either lesion or skin. It is challenging because lesion and skin can often have similar appearance. We present a novel exemplar-based algorithm for lesion segmentation which leverages the context provided by a global color model to retrieve annotated examples which are most similar to a given query image. Pixel labels are generated through a probabilistic voting rule and smoothed using a dermoscopy-specific spatial prior. We compare our method to three competing techniques using a large dataset of dermoscopy images with hand-segmented ground truth,We show that our exemplar-based approach yields significantly better segmentations and is computationally efficient.
{"title":"Exemplar-based segmentation of pigmented skin lesions from dermoscopy images","authors":"Howard Zhou, James M. Rehg, Mei Chen","doi":"10.1109/ISBI.2010.5490372","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490372","url":null,"abstract":"Automated segmentation of pigmented skin lesions (PSLs) from dermoscopy images is an important step for computer-aided diagnosis of skin cancer. The segmentation task involves classifying each image pixel as either lesion or skin. It is challenging because lesion and skin can often have similar appearance. We present a novel exemplar-based algorithm for lesion segmentation which leverages the context provided by a global color model to retrieve annotated examples which are most similar to a given query image. Pixel labels are generated through a probabilistic voting rule and smoothed using a dermoscopy-specific spatial prior. We compare our method to three competing techniques using a large dataset of dermoscopy images with hand-segmented ground truth,We show that our exemplar-based approach yields significantly better segmentations and is computationally efficient.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"312 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123625558","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490352
Abu Sayeed Md. Sohail, M. Rahman, P. Bhattacharya, Srinivasan Krishnamurthy, S. Mudur
This paper presents an effective solution for content-based retrieval and classification of ultrasound medical images representing three types of ovarian cysts: Simple Cyst, Endometrioma, and Teratoma. Our proposed solution comprises of the followings: extraction of low level ultrasound image features combining histogram moments with Gray Level Co-Occurrence Matrix (GLCM) based statistical texture descriptors, image retrieval using a similarity model based on Gower's similarity coefficient which measures the relevance between the query image and the target images, and use of multiclass Support Vector Machine (SVM) for classifying the low level ultrasound image features into their corresponding high level categories. Efficiency of the above solution for ultrasound medical image retrieval and classification has been evaluated using an inprogress database, presently consisting of 478 ultrasound ovarian images. Performance-wise, in retrieval of ultrasound images, our proposed solution has demonstrated above 77% and 75% of average precision considering the first 20 and 40 retrieved results respectively, and an average classification accuracy of 86.90%.
{"title":"Retrieval and classification of ultrasound images of ovarian cysts combining texture features and histogram moments","authors":"Abu Sayeed Md. Sohail, M. Rahman, P. Bhattacharya, Srinivasan Krishnamurthy, S. Mudur","doi":"10.1109/ISBI.2010.5490352","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490352","url":null,"abstract":"This paper presents an effective solution for content-based retrieval and classification of ultrasound medical images representing three types of ovarian cysts: Simple Cyst, Endometrioma, and Teratoma. Our proposed solution comprises of the followings: extraction of low level ultrasound image features combining histogram moments with Gray Level Co-Occurrence Matrix (GLCM) based statistical texture descriptors, image retrieval using a similarity model based on Gower's similarity coefficient which measures the relevance between the query image and the target images, and use of multiclass Support Vector Machine (SVM) for classifying the low level ultrasound image features into their corresponding high level categories. Efficiency of the above solution for ultrasound medical image retrieval and classification has been evaluated using an inprogress database, presently consisting of 478 ultrasound ovarian images. Performance-wise, in retrieval of ultrasound images, our proposed solution has demonstrated above 77% and 75% of average precision considering the first 20 and 40 retrieved results respectively, and an average classification accuracy of 86.90%.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122130850","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}
This paper presents an algorithm using discriminative sparse representations to segment tissues in optical images of the uterine cervix. Because of the large variations in the image appearance caused by the changing of illumination and specular reflection, the different classes of color and texture features in optical images are often overlapped with each other. Using sparse representations they can be transformed to higher dimension with sparse constraints and become more linearly separated. Different from the previous reconstructive sparse representation, the discriminative method considers positive and negative samples simultaneously, which means that these generated dictionaries can be discriminative and perform better for their own classes but worse for the others. New data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we used our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of the uterine cervix. Compared with the other general methods including SVM, nearest neighbor and reconstructive sparse representations, our approach showed higher sensitivity and specificity.
{"title":"Discriminative sparse representations for cervigram image segmentation","authors":"Shaoting Zhang, Junzhou Huang, Dimitris N. Metaxas, Wei Wang, Xiaolei Huang","doi":"10.1109/ISBI.2010.5490397","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490397","url":null,"abstract":"This paper presents an algorithm using discriminative sparse representations to segment tissues in optical images of the uterine cervix. Because of the large variations in the image appearance caused by the changing of illumination and specular reflection, the different classes of color and texture features in optical images are often overlapped with each other. Using sparse representations they can be transformed to higher dimension with sparse constraints and become more linearly separated. Different from the previous reconstructive sparse representation, the discriminative method considers positive and negative samples simultaneously, which means that these generated dictionaries can be discriminative and perform better for their own classes but worse for the others. New data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we used our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of the uterine cervix. Compared with the other general methods including SVM, nearest neighbor and reconstructive sparse representations, our approach showed higher sensitivity and specificity.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124042387","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490313
C. Seiler, X. Pennec, M. Reyes
Currently there is an increase usage of CT-based bone diagnosis because low-radiation and cost-effective 2D imaging modalities do not provide the necessary 3D information for bone diagnosis. The fundamental objective of our work is to build a model connecting 2D X-ray information to 3D CT information through regression. As a first step we propose an univariate non-parametric regression on individual predictor variables to explore the non-linearity of the data. To later combine these univariate models we then replace them with parametric models. We examine two predictors, shaft length and caput collum diaphysis angle on a database of 182 CT images of femurs. We show that for each predictor it is possible to describe 99% of the variance through a simple up to second order parametric model. These findings will allow us to extend to the multivariate case in the future.
{"title":"Parametric regression of 3D medical images through the exploration of non-parametric regression models","authors":"C. Seiler, X. Pennec, M. Reyes","doi":"10.1109/ISBI.2010.5490313","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490313","url":null,"abstract":"Currently there is an increase usage of CT-based bone diagnosis because low-radiation and cost-effective 2D imaging modalities do not provide the necessary 3D information for bone diagnosis. The fundamental objective of our work is to build a model connecting 2D X-ray information to 3D CT information through regression. As a first step we propose an univariate non-parametric regression on individual predictor variables to explore the non-linearity of the data. To later combine these univariate models we then replace them with parametric models. We examine two predictors, shaft length and caput collum diaphysis angle on a database of 182 CT images of femurs. We show that for each predictor it is possible to describe 99% of the variance through a simple up to second order parametric model. These findings will allow us to extend to the multivariate case in the future.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125939948","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490321
T. Lefèvre, B. Mory, R. Ardon, Javier Sanchez-Castro, A. Yezzi
This paper presents a novel robust automatic method for the segmentation of the Inferior Vena Cava (IVC) in the proximity of the liver. In clinical diagnosis and surgery planning, IVC segmentation is essential since it strongly impacts both liver volumetry accuracy and vascularity analysis. Given the anatomical variability, the lack of clear boundaries and complexity of the surrounding structures along the IVC, its segmentation remains a difficult and open problem. To cope with such challenging conditions, we developed an implicit representation of a generalized cylinder and optimized a local region-based criterion under dedicated anatomical constraints. Our method was tested on a dataset of 20 contrast-enhanced CT scans, achieving 80% success rate in fully automatic mode. The remaining cases needed minimal user input (one point) to reach 95% success under radiology expert criteria.
{"title":"Automatic Inferior Vena Cava segmentation in contrast-enhanced CT volumes","authors":"T. Lefèvre, B. Mory, R. Ardon, Javier Sanchez-Castro, A. Yezzi","doi":"10.1109/ISBI.2010.5490321","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490321","url":null,"abstract":"This paper presents a novel robust automatic method for the segmentation of the Inferior Vena Cava (IVC) in the proximity of the liver. In clinical diagnosis and surgery planning, IVC segmentation is essential since it strongly impacts both liver volumetry accuracy and vascularity analysis. Given the anatomical variability, the lack of clear boundaries and complexity of the surrounding structures along the IVC, its segmentation remains a difficult and open problem. To cope with such challenging conditions, we developed an implicit representation of a generalized cylinder and optimized a local region-based criterion under dedicated anatomical constraints. Our method was tested on a dataset of 20 contrast-enhanced CT scans, achieving 80% success rate in fully automatic mode. The remaining cases needed minimal user input (one point) to reach 95% success under radiology expert criteria.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124855798","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490394
Sungeun Eom, Ryoma Bise, T. Kanade
We present a method for robustly detecting hematopoietic stem cells (HSCs) in phase contrast microscopy images. HSCs appear to be easy to detect since they typically appear as round objects. However, when HSCs are touching and overlapping, showing the variations in shape and appearance, standard pattern detection methods, such as Hough transform and correlation, do not perform well. The proposed method exploits the output pattern of a ring filter bank applied to the input image, which consists of a series of matched filters with multiple-radius ring-shaped templates. By modeling the profile of each filter response as a quadratic surface, we explore the variations of peak curvatures and peak values of the filter responses when the ring radius varies. The method is validated on thousands of phase contrast microscopy images with different acquisition settings, achieving 96.5% precision and 94.4% recall.
{"title":"Detection of hematopoietic stem cells in microscopy images using a bank of ring filters","authors":"Sungeun Eom, Ryoma Bise, T. Kanade","doi":"10.1109/ISBI.2010.5490394","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490394","url":null,"abstract":"We present a method for robustly detecting hematopoietic stem cells (HSCs) in phase contrast microscopy images. HSCs appear to be easy to detect since they typically appear as round objects. However, when HSCs are touching and overlapping, showing the variations in shape and appearance, standard pattern detection methods, such as Hough transform and correlation, do not perform well. The proposed method exploits the output pattern of a ring filter bank applied to the input image, which consists of a series of matched filters with multiple-radius ring-shaped templates. By modeling the profile of each filter response as a quadratic surface, we explore the variations of peak curvatures and peak values of the filter responses when the ring radius varies. The method is validated on thousands of phase contrast microscopy images with different acquisition settings, achieving 96.5% precision and 94.4% recall.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125033690","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490310
L. Humbert, T. Whitmarsh, M. D. Craene, L. D. R. Barquero, K. Fritscher, R. Schubert, F. Eckstein, T. Link, Alejandro F Frangi
The diagnosis of osteoporosis and the prevention of femur fractures is a major challenge for our society. However, the diagnosis performed in clinical routine from Dual Energy X-ray Absorptiometry (DXA) images is limited. This paper proposes a 3D reconstruction method of both the shape and the Bone Mineral Density (BMD) distribution of the proximal femur from routinely used DXA images. The reconstruction accuracy that can be obtained from single-view and multi-view DXA devices was assessed. This evaluation, from 20 bone specimens and simulated DXA images, highlighted a mean shape accuracy of 1.3mm and a BMD accuracy of 4.4% from a single-view DXA image. A multi-view configuration with 2 views (frontal-sagittal) appeared as a good compromise (mean shape accuracy of 0.9mm and BMD accuracy of 3.2%). We are currently using this method for in vivo clinical studies in order to improve the diagnosis of osteoporosis and the prevention of femur fractures.
{"title":"3D reconstruction of both shape and Bone Mineral Density distribution of the femur from DXA images","authors":"L. Humbert, T. Whitmarsh, M. D. Craene, L. D. R. Barquero, K. Fritscher, R. Schubert, F. Eckstein, T. Link, Alejandro F Frangi","doi":"10.1109/ISBI.2010.5490310","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490310","url":null,"abstract":"The diagnosis of osteoporosis and the prevention of femur fractures is a major challenge for our society. However, the diagnosis performed in clinical routine from Dual Energy X-ray Absorptiometry (DXA) images is limited. This paper proposes a 3D reconstruction method of both the shape and the Bone Mineral Density (BMD) distribution of the proximal femur from routinely used DXA images. The reconstruction accuracy that can be obtained from single-view and multi-view DXA devices was assessed. This evaluation, from 20 bone specimens and simulated DXA images, highlighted a mean shape accuracy of 1.3mm and a BMD accuracy of 4.4% from a single-view DXA image. A multi-view configuration with 2 views (frontal-sagittal) appeared as a good compromise (mean shape accuracy of 0.9mm and BMD accuracy of 3.2%). We are currently using this method for in vivo clinical studies in order to improve the diagnosis of osteoporosis and the prevention of femur fractures.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129511676","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490230
A. Chessel, B. Cinquin, S. Bardin, J. Boulanger, J. Salamero, C. Kervrann
Endocytosis/recycling and exocytosis aremechanisms conserved through evolution allowing cells to communicate with their external medium. In order to study these dynamic processes, the present work proposes a patch-based method for detecting recycling or exocytotic events at the Plasma membrane in fast TIRF microscopy combined with the computation of normalized temporal representations of those events. Evaluation, performed on TIRF sequences showing Transferrin receptor (TfR) recycling, validates a high detection rate fully compatible with an automatic data extraction and analysis of the plasma membrane recycling process.
{"title":"A detection-based framework for the analysis of recycling in TIRF microscopy","authors":"A. Chessel, B. Cinquin, S. Bardin, J. Boulanger, J. Salamero, C. Kervrann","doi":"10.1109/ISBI.2010.5490230","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490230","url":null,"abstract":"Endocytosis/recycling and exocytosis aremechanisms conserved through evolution allowing cells to communicate with their external medium. In order to study these dynamic processes, the present work proposes a patch-based method for detecting recycling or exocytotic events at the Plasma membrane in fast TIRF microscopy combined with the computation of normalized temporal representations of those events. Evaluation, performed on TIRF sequences showing Transferrin receptor (TfR) recycling, validates a high detection rate fully compatible with an automatic data extraction and analysis of the plasma membrane recycling process.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128498377","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490060
E. Miqueles, A. R. Pierro
X-Ray fluorescence computed tomography (xfct) aims at reconstructing fluorescence density from emission data given the measured x-ray attenuation. In this paper, inspired by the classical results from Logan & Shepp [3], we briefly discuss the existence of generalized ridge functions providing the minimal norm solution of the inverse problem. An algorithm to construct such functions is presented, based on results from Kazantsev [4]. Numerical results are also shown, with real and simulated data.
{"title":"Xfct inversion by generalized ridge functions","authors":"E. Miqueles, A. R. Pierro","doi":"10.1109/ISBI.2010.5490060","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490060","url":null,"abstract":"X-Ray fluorescence computed tomography (xfct) aims at reconstructing fluorescence density from emission data given the measured x-ray attenuation. In this paper, inspired by the classical results from Logan & Shepp [3], we briefly discuss the existence of generalized ridge functions providing the minimal norm solution of the inverse problem. An algorithm to construct such functions is presented, based on results from Kazantsev [4]. Numerical results are also shown, with real and simulated data.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129584165","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}
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