The Experts below are selected from a list of 71769 Experts worldwide ranked by ideXlab platform

Data Services, Bobst Library - One of the best experts on this subject based on the ideXlab platform.

  • Village-Level Geospatial Socio-Economic Data for the state of Madhya Pradesh, India, 2001
    2018
    Co-Authors: Data Services, Bobst Library
    Abstract:

    This polygon shapefile represents represents socioeconomic statistics of each village in the state of Madhya Pradesh in 2001. The set utilizes the official cadastral maps published by the Survey of India to develop a digitized fine- resolution (administrative level: village/town; scale of mapping: 1:50,000) vector data set for all of India for 2001. In addition, several vector data sets for village/town boundaries available for some Indian states were utilized. This vector data set for village/town boundary was then geospatially linked to the tabular data for each village/town for the 1991 and 2001 census, downloaded from the online Digital Database of the Census of India1. Prior to linking, the census data went through extensive data cleaning to ensure the data were quality-controlled and standardized. This data was downloaded from the Socioeconomic Data and Applications Center and is also hosted by Columbia University's Center for International Earth Science Information Network. Refer to the documentation for data methodology and codebook

  • Village-Level Geospatial Socio-Economic Data for the state of Bihar, India, 2001
    2018
    Co-Authors: Data Services, Bobst Library
    Abstract:

    This polygon shapefile represents represents socioeconomic statistics of each village in the state of Bihar in 2001. The set utilizes the official cadastral maps published by the Survey of India to develop a digitized fine- resolution (administrative level: village/town; scale of mapping: 1:50,000) vector data set for all of India for 2001. In addition, several vector data sets for village/town boundaries available for some Indian states were utilized. This vector data set for village/town boundary was then geospatially linked to the tabular data for each village/town for the 1991 and 2001 census, downloaded from the online Digital Database of the Census of India1. Prior to linking, the census data went through extensive data cleaning to ensure the data were quality-controlled and standardized. This data was downloaded from the Socioeconomic Data and Applications Center and is also hosted by Columbia University's Center for International Earth Science Information Network. Refer to the documentation for data methodology and codebook

  • Village-Level Geospatial Socio-Economic Data for the state of Sikkim, India, 2001
    2018
    Co-Authors: Data Services, Bobst Library
    Abstract:

    This polygon shapefile represents represents socioeconomic statistics of each village in the state of Sikkim in 2001. The set utilizes the official cadastral maps published by the Survey of India to develop a digitized fine- resolution (administrative level: village/town; scale of mapping: 1:50,000) vector data set for all of India for 2001. In addition, several vector data sets for village/town boundaries available for some Indian states were utilized. This vector data set for village/town boundary was then geospatially linked to the tabular data for each village/town for the 1991 and 2001 census, downloaded from the online Digital Database of the Census of India1. Prior to linking, the census data went through extensive data cleaning to ensure the data were quality-controlled and standardized. This data was downloaded from the Socioeconomic Data and Applications Center and is also hosted by Columbia University's Center for International Earth Science Information Network. Refer to the documentation for data methodology and codebook

  • Village-Level Geospatial Socio-Economic Data for the state of Chhattisgarh, India, 2001
    2018
    Co-Authors: Data Services, Bobst Library
    Abstract:

    This polygon shapefile represents represents socioeconomic statistics of each village in the state of Chhattisgarh in 2001. The set utilizes the official cadastral maps published by the Survey of India to develop a digitized fine- resolution (administrative level: village/town; scale of mapping: 1:50,000) vector data set for all of India for 2001. In addition, several vector data sets for village/town boundaries available for some Indian states were utilized. This vector data set for village/town boundary was then geospatially linked to the tabular data for each village/town for the 1991 and 2001 census, downloaded from the online Digital Database of the Census of India1. Prior to linking, the census data went through extensive data cleaning to ensure the data were quality-controlled and standardized. This data was downloaded from the Socioeconomic Data and Applications Center and is also hosted by Columbia University's Center for International Earth Science Information Network. Refer to the documentation for data methodology and codebook

  • Village-Level Geospatial Socio-Economic Data for the state of Himachal Pradesh, India, 2001
    2018
    Co-Authors: Data Services, Bobst Library
    Abstract:

    This polygon shapefile represents represents socioeconomic statistics of each village in the state of Himachal Pradesh in 2001. The set utilizes the official cadastral maps published by the Survey of India to develop a digitized fine- resolution (administrative level: village/town; scale of mapping: 1:50,000) vector data set for all of India for 2001. In addition, several vector data sets for village/town boundaries available for some Indian states were utilized. This vector data set for village/town boundary was then geospatially linked to the tabular data for each village/town for the 1991 and 2001 census, downloaded from the online Digital Database of the Census of India1. Prior to linking, the census data went through extensive data cleaning to ensure the data were quality-controlled and standardized. This data was downloaded from the Socioeconomic Data and Applications Center and is also hosted by Columbia University's Center for International Earth Science Information Network. Refer to the documentation for data methodology and codebook

Reyer Zwiggelaar - One of the best experts on this subject based on the ideXlab platform.

  • automatic microcalcification and cluster detection for Digital and digitised mammograms
    Knowledge Based Systems, 2012
    Co-Authors: Arnau Oliver, Meritxell Tortajada, Jordi Freixenet, Lidia Tortajada, Melcior Sentis, Xavier Lladó, Albert Torrent, Reyer Zwiggelaar
    Abstract:

    In this paper we present a knowledge-based approach for the automatic detection of microcalcifications and clusters in mammographic images. Our proposal is based on using local features extracted from a bank of filters to obtain a local description of the microcalcifications morphology. The developed approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosted classifier to perform the detection of individual microcalcifications. Subsequently, the microcalcification detection method is extended in order to detect clusters. The validity of our approach is extensively demonstrated using two digitised Databases and one full-field Digital Database. The experimental evaluation is performed in terms of ROC analysis for the microcalcification detection and FROC analysis for the cluster detection, resulting in better than 80% sensitivity at 1 false positive cluster per image.

  • a review of automatic mass detection and segmentation in mammographic images
    Medical Image Analysis, 2010
    Co-Authors: Arnau Oliver, Jordi Freixenet, Joan Marti, Elsa Perez, Josep Pont, Erika R E Denton, Reyer Zwiggelaar
    Abstract:

    The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic Databases: a public digitised Database and a local full-field Digital Database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.

L Costaridou - One of the best experts on this subject based on the ideXlab platform.

  • computer aided diagnosis of mammographic masses based on a supervised content based image retrieval approach
    Pattern Recognition, 2017
    Co-Authors: Lazaros Tsochatzidis, Anna Karahaliou, Nikolaos Arikidis, L Costaridou, Konstantinos Zagoris, Ioannis Pratikakis
    Abstract:

    Abstract In this work, the incorporation of content-based image retrieval (CBIR) into computer aided diagnosis (CADx) is investigated, in order to contribute to the decision-making process of radiologists in the characterization of mammographic masses. The proposed scheme comprises two stages: A margin-specific supervised CBIR stage that retrieves images from reference cases along with a decision stage that is based on the retrieved items. The feature set utilized exploits state-of-the-art features along with a newly proposed texture descriptor, namely mHOG, targeted to capturing margin and core specific mass properties. Performance evaluation considers the CBIR and diagnosis stages separately and is addressed by using standard measures on an enhanced version of the widely adopted Digital Database for screening mammography (DDSM). The proposed scheme achieved improved performance of CADx of masses in X-ray mammography experimentally compared to the state-of-the-art.

  • breast cancer diagnosis analyzing texture of tissue surrounding microcalcifications
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
    Co-Authors: Anna Karahaliou, I Boniatis, Spyros Skiadopoulos, Filippos Sakellaropoulos, Nikolaos Arikidis, E Likaki, G Panayiotakis, L Costaridou
    Abstract:

    The current study investigates texture properties of the tissue surrounding microcalcification (MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed consists of 85 dense mammographic images, originating from the Digital Database for screening mammography. mammograms analyzed contain 100 subtle MC clusters (46 benign and 54 malignant). The tissue surrounding MCs is defined on original and wavelet decomposed images, based on a redundant discrete wavelet transform. Gray-level texture and wavelet coefficient texture features at three decomposition levels are extracted from surrounding tissue regions of interest (ST-ROIs). Specifically, gray-level first-order statistics, gray-level cooccurrence matrices features, and Lawspsila texture energy measures are extracted from original image ST-ROIs. Wavelet coefficient first-order statistics and wavelet coefficient cooccurrence matrices features are extracted from subimages ST-ROIs. The ability of each feature set in differentiating malignant from benign tissue is investigated using a probabilistic neural network. Classification outputs of most discriminating feature sets are combined using a majority voting rule. The proposed combined scheme achieved an area under receiver operating characteristic curve (Az) of 0.989. Results suggest that MCspsila ST texture analysis can contribute to computer-aided diagnosis of breast cancer.

  • texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis
    British Journal of Radiology, 2007
    Co-Authors: Anna Karahaliou, I Boniatis, Spyros Skiadopoulos, E Likaki, G Panayiotakis, P Sakellaropoulos, L Costaridou
    Abstract:

    Diagnosis of microcalcifications (MCs) is challenged by the presence of dense breast parenchyma, resulting in low specificity values and thus in unnecessary biopsies. The current study investigates whether texture properties of the tissue surrounding MCs can contribute to breast cancer diagnosis. A case sample of 100 biopsy-proved MC clusters (46 benign, 54 malignant) from 85 dense mammographic images, included in the Digital Database for Screening Mammography, was analysed. Regions of interest (ROIs) containing the MCs were pre-processed using a wavelet-based contrast enhancement method, followed by local thresholding to segment MCs; the segmented MCs were excluded from original image ROIs, and the remaining area (surrounding tissue) was subjected to texture analysis. Four categories of textural features (first order statistics, co-occurrence matrices features, run length matrices features and Laws' texture energy measures) were extracted from the surrounding tissue. The ability of each feature category ...

Anna Karahaliou - One of the best experts on this subject based on the ideXlab platform.

  • computer aided diagnosis of mammographic masses based on a supervised content based image retrieval approach
    Pattern Recognition, 2017
    Co-Authors: Lazaros Tsochatzidis, Anna Karahaliou, Nikolaos Arikidis, L Costaridou, Konstantinos Zagoris, Ioannis Pratikakis
    Abstract:

    Abstract In this work, the incorporation of content-based image retrieval (CBIR) into computer aided diagnosis (CADx) is investigated, in order to contribute to the decision-making process of radiologists in the characterization of mammographic masses. The proposed scheme comprises two stages: A margin-specific supervised CBIR stage that retrieves images from reference cases along with a decision stage that is based on the retrieved items. The feature set utilized exploits state-of-the-art features along with a newly proposed texture descriptor, namely mHOG, targeted to capturing margin and core specific mass properties. Performance evaluation considers the CBIR and diagnosis stages separately and is addressed by using standard measures on an enhanced version of the widely adopted Digital Database for screening mammography (DDSM). The proposed scheme achieved improved performance of CADx of masses in X-ray mammography experimentally compared to the state-of-the-art.

  • breast cancer diagnosis analyzing texture of tissue surrounding microcalcifications
    International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
    Co-Authors: Anna Karahaliou, I Boniatis, Spyros Skiadopoulos, Filippos Sakellaropoulos, Nikolaos Arikidis, E Likaki, G Panayiotakis, L Costaridou
    Abstract:

    The current study investigates texture properties of the tissue surrounding microcalcification (MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed consists of 85 dense mammographic images, originating from the Digital Database for screening mammography. mammograms analyzed contain 100 subtle MC clusters (46 benign and 54 malignant). The tissue surrounding MCs is defined on original and wavelet decomposed images, based on a redundant discrete wavelet transform. Gray-level texture and wavelet coefficient texture features at three decomposition levels are extracted from surrounding tissue regions of interest (ST-ROIs). Specifically, gray-level first-order statistics, gray-level cooccurrence matrices features, and Lawspsila texture energy measures are extracted from original image ST-ROIs. Wavelet coefficient first-order statistics and wavelet coefficient cooccurrence matrices features are extracted from subimages ST-ROIs. The ability of each feature set in differentiating malignant from benign tissue is investigated using a probabilistic neural network. Classification outputs of most discriminating feature sets are combined using a majority voting rule. The proposed combined scheme achieved an area under receiver operating characteristic curve (Az) of 0.989. Results suggest that MCspsila ST texture analysis can contribute to computer-aided diagnosis of breast cancer.

  • texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis
    British Journal of Radiology, 2007
    Co-Authors: Anna Karahaliou, I Boniatis, Spyros Skiadopoulos, E Likaki, G Panayiotakis, P Sakellaropoulos, L Costaridou
    Abstract:

    Diagnosis of microcalcifications (MCs) is challenged by the presence of dense breast parenchyma, resulting in low specificity values and thus in unnecessary biopsies. The current study investigates whether texture properties of the tissue surrounding MCs can contribute to breast cancer diagnosis. A case sample of 100 biopsy-proved MC clusters (46 benign, 54 malignant) from 85 dense mammographic images, included in the Digital Database for Screening Mammography, was analysed. Regions of interest (ROIs) containing the MCs were pre-processed using a wavelet-based contrast enhancement method, followed by local thresholding to segment MCs; the segmented MCs were excluded from original image ROIs, and the remaining area (surrounding tissue) was subjected to texture analysis. Four categories of textural features (first order statistics, co-occurrence matrices features, run length matrices features and Laws' texture energy measures) were extracted from the surrounding tissue. The ability of each feature category ...

Arnau Oliver - One of the best experts on this subject based on the ideXlab platform.

  • automatic microcalcification and cluster detection for Digital and digitised mammograms
    Knowledge Based Systems, 2012
    Co-Authors: Arnau Oliver, Meritxell Tortajada, Jordi Freixenet, Lidia Tortajada, Melcior Sentis, Xavier Lladó, Albert Torrent, Reyer Zwiggelaar
    Abstract:

    In this paper we present a knowledge-based approach for the automatic detection of microcalcifications and clusters in mammographic images. Our proposal is based on using local features extracted from a bank of filters to obtain a local description of the microcalcifications morphology. The developed approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosted classifier to perform the detection of individual microcalcifications. Subsequently, the microcalcification detection method is extended in order to detect clusters. The validity of our approach is extensively demonstrated using two digitised Databases and one full-field Digital Database. The experimental evaluation is performed in terms of ROC analysis for the microcalcification detection and FROC analysis for the cluster detection, resulting in better than 80% sensitivity at 1 false positive cluster per image.

  • a review of automatic mass detection and segmentation in mammographic images
    Medical Image Analysis, 2010
    Co-Authors: Arnau Oliver, Jordi Freixenet, Joan Marti, Elsa Perez, Josep Pont, Erika R E Denton, Reyer Zwiggelaar
    Abstract:

    The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic Databases: a public digitised Database and a local full-field Digital Database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.