The Experts below are selected from a list of 144 Experts worldwide ranked by ideXlab platform
Kunio Doi - One of the best experts on this subject based on the ideXlab platform.
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development of an improved cad scheme for automated detection of lung nodules in digital Chest Images
Medical Physics, 1997Co-Authors: Kunio Doi, Takeshi Kobayashi, Heber Macmahon, Maryellen L GigerAbstract:Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital Chest Images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA Chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference Image (which corresponds to the subtraction of a signal-enhanced Image and a signal-suppressed Image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per Chest Image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one Chest Image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.
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Adaptive feature analysis of false positives for computerized detection of lung nodules in digital Chest Images
Medical Imaging 1997: Image Processing, 1997Co-Authors: Heber Macmahon, Maryellen L Giger, Kunio DoiAbstract:To assist radiologists in diagnosing early lung cancer, we have developed a computer-aided diagnosis (CAD) scheme for automated detection of lung nodules in digital Chest Images. The database used for this study consisted of two hundred PA Chest radiographs, including 100 normals and 100 abnormals. Our CAD scheme has four basic steps, namely, (1) preprocessing, (2) identification of initial nodule candidates (rule-based test #1), (3) grouping of initial nodule candidates into six groups, and (4) elimination of false positives (rule-based test #2 - #5 and artificial neural network). Our CAD scheme achieves, on average, a sensitivity of 70%, with 1.7 false positives per Chest Image. We believe that this CAD scheme with its current performance is ready for clinical evaluation.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Image feature analysis for computer aided diagnosis detection of right and left hemidiaphragm edges and delineation of lung field in Chest radiographs
Medical Physics, 1996Co-Authors: Kunio DoiAbstract:Diaphragm edges, together with ribcage edges, in Chest radiographs provide useful information on the location, shape, and size of the lung fields that are required by computer‐aided diagnosis(CAD) schemes for automated detection of various abnormalities. In this continued study, we developed a computerized method for detection of the right and left hemidiaphragm edges. First, the right hemidiaphragm edges in a PA (postero‐anterior) Chest Image were determined by edge gradient analysis. An initial vertical ROI was then placed at the middle of the left hemidiaphragm, based on a ‘‘standard rule’’ for determination of the starting points to search for the left hemidiaphragm edges. Seven categories were used to assess the accuracy of the placement of the initial ROI and the selection of the primary left starting point within the initial ROI. For some categories, it was necessary to select a second left starting point besides the primary one. Therefore, for these categories, two sets of ‘‘detected left hemidiaphragm edges’’ resulted from the two left starting points. Two parameters were used as measures to eliminate the false left hemidiaphragm edges which were due to an incorrect left starting point. Two polynomial functions were applied separately which produced smooth curves for the right and left hemidiaphragm edges. Finally, the delineation of the lung field in a Chest Image was obtained by connecting the right and left hemidiaphragm edge curves with the corresponding ribcage edge curves. The subjective evaluation results indicated that the accuracy for the determination of the right and left hemidiaphragm edges was approximately 97% and 90%, respectively.
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Image feature analysis for computer‐aided diagnosis: Detection of right and left hemidiaphragm edges and delineation of lung field in Chest radiographs
Medical physics, 1996Co-Authors: Kunio DoiAbstract:Diaphragm edges, together with ribcage edges, in Chest radiographs provide useful information on the location, shape, and size of the lung fields that are required by computer‐aided diagnosis(CAD) schemes for automated detection of various abnormalities. In this continued study, we developed a computerized method for detection of the right and left hemidiaphragm edges. First, the right hemidiaphragm edges in a PA (postero‐anterior) Chest Image were determined by edge gradient analysis. An initial vertical ROI was then placed at the middle of the left hemidiaphragm, based on a ‘‘standard rule’’ for determination of the starting points to search for the left hemidiaphragm edges. Seven categories were used to assess the accuracy of the placement of the initial ROI and the selection of the primary left starting point within the initial ROI. For some categories, it was necessary to select a second left starting point besides the primary one. Therefore, for these categories, two sets of ‘‘detected left hemidiaphragm edges’’ resulted from the two left starting points. Two parameters were used as measures to eliminate the false left hemidiaphragm edges which were due to an incorrect left starting point. Two polynomial functions were applied separately which produced smooth curves for the right and left hemidiaphragm edges. Finally, the delineation of the lung field in a Chest Image was obtained by connecting the right and left hemidiaphragm edge curves with the corresponding ribcage edge curves. The subjective evaluation results indicated that the accuracy for the determination of the right and left hemidiaphragm edges was approximately 97% and 90%, respectively.
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Image feature analysis for computer aided diagnosis accurate determination of ribcage boundary in Chest radiographs
Medical Physics, 1995Co-Authors: Kunio DoiAbstract:A computerized method for accurate determination of the ribcage boundary in Chest Images has been developed for use in computer‐aided diagnosis(CAD) schemes for automated detection of abnormalities such as the pulmonary lung nodules, pneumothorax, interstitial disease, cardiomegaly, and interval changes in clinical Chest Images. With our method, the average position of the top of the lung in the Chest Image is determined first. Top lung edges and ribcage edges are determined within search ROIs, which are selected over top lung cages and ribcages. Three polynomial functions are applied separately to yield smooth curves for top lung edges and right and left ribcage edges. The complete ribcage boundary is then obtained by smoothly connecting three curves. A total of 1000 radiographs were digitized to 1k×1k matrix size and a 10‐bit gray scale with a laser scanner and analyzed by our method. The subjective evaluation indicated that our method produced moderately to highly accurate results in approximately 96% of the 1000 cases examined.
Heber Macmahon - One of the best experts on this subject based on the ideXlab platform.
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development of an improved cad scheme for automated detection of lung nodules in digital Chest Images
Medical Physics, 1997Co-Authors: Kunio Doi, Takeshi Kobayashi, Heber Macmahon, Maryellen L GigerAbstract:Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital Chest Images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA Chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference Image (which corresponds to the subtraction of a signal-enhanced Image and a signal-suppressed Image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per Chest Image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one Chest Image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.
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Adaptive feature analysis of false positives for computerized detection of lung nodules in digital Chest Images
Medical Imaging 1997: Image Processing, 1997Co-Authors: Heber Macmahon, Maryellen L Giger, Kunio DoiAbstract:To assist radiologists in diagnosing early lung cancer, we have developed a computer-aided diagnosis (CAD) scheme for automated detection of lung nodules in digital Chest Images. The database used for this study consisted of two hundred PA Chest radiographs, including 100 normals and 100 abnormals. Our CAD scheme has four basic steps, namely, (1) preprocessing, (2) identification of initial nodule candidates (rule-based test #1), (3) grouping of initial nodule candidates into six groups, and (4) elimination of false positives (rule-based test #2 - #5 and artificial neural network). Our CAD scheme achieves, on average, a sensitivity of 70%, with 1.7 false positives per Chest Image. We believe that this CAD scheme with its current performance is ready for clinical evaluation.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Image feature analysis and computer-aided diagnosis in digital radiography: automated detection of pneumothorax in Chest Images.
Medical physics, 1992Co-Authors: Shigeru Sanada, Kunio Doi, Heber MacmahonAbstract:In order to aid radiologists in the diagnosis of pneumothorax from Chest radiographs, an automated method for detection of subtle pneumothorax is being developed. The computerized method is based on the detection of a fine curved‐line pattern, which is a unique feature of radiographic findings of pneumothorax. Initially, regions of interest (ROIs) are determined in each upper lung area, where subtle pneumothoraces commonly appear. The pneumothorax pattern is enhanced by the selection of edge gradients within a limited range of orientations. Rib edges included in this edge‐enhanced Image are removed, based on the locations of posterior ribs that are determined separately. A subtle curved line due to pneumothorax is then detected by means of the Hough transform. The detected pneumothorax pattern is marked on the Chest Image displayed on a CRT monitor. With the present computer method applied to 50 Chest Images (28 normals and 22 abnormals with pneumothorax), we were able to detect 77% of pneumothoraces, with 0.44 false‐positives per Image.
Harumi Itoh - One of the best experts on this subject based on the ideXlab platform.
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Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging - Microstructure analysis of the pulmonary acinus using a synchrotron radiation CT
Medical Imaging 2015: Biomedical Applications in Molecular Structural and Functional Imaging, 2015Co-Authors: Y. Tokumoto, Koichi Minami, Yoshiki Kawata, Noboru Niki, Keiji Umetani, Yasutaka Nakano, Hiroaki Sakai, Hironobu Ohmatsu, Harumi ItohAbstract:Conversion of Images at micro level of normal and with very early stage disease of the lung and quantitative analysis of morphology on CT Image can contribute to the Chest Image diagnosis to the next generation. Previous, anatomy and pathology analysis of pulmonary lobule have been conducted to better understand the CT Image of peripheral lung tissue disease. However, it is difficult to figure out three-dimensional (3D) conformation because of analyzing at the slice Image. The purpose of this study is a 3D microstructual and quantitative analyses of pulmonary acinus with spatial resolution in the range of several micrometers by using a synchrotron radiation micro CT (SRμCT). In this paper, we present a semi-automatic method for segmenting the secondary pulmonary lobule into acinus or subacinus and extracting small vessel in human acinus Imaged by the SRμCT.
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microstructure analysis of the pulmonary acinus using a synchrotron radiation ct
Proceedings of SPIE, 2015Co-Authors: Y. Tokumoto, Koichi Minami, Yoshiki Kawata, Noboru Niki, Keiji Umetani, Yasutaka Nakano, Hiroaki Sakai, Hironobu Ohmatsu, Harumi ItohAbstract:Conversion of Images at micro level of normal and with very early stage disease of the lung and quantitative analysis of morphology on CT Image can contribute to the Chest Image diagnosis to the next generation. Previous, anatomy and pathology analysis of pulmonary lobule have been conducted to better understand the CT Image of peripheral lung tissue disease. However, it is difficult to figure out three-dimensional (3D) conformation because of analyzing at the slice Image. The purpose of this study is a 3D microstructual and quantitative analyses of pulmonary acinus with spatial resolution in the range of several micrometers by using a synchrotron radiation micro CT (SRμCT). In this paper, we present a semi-automatic method for segmenting the secondary pulmonary lobule into acinus or subacinus and extracting small vessel in human acinus Imaged by the SRμCT.
Youji Tabata - One of the best experts on this subject based on the ideXlab platform.
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Examination of Ability to Support the Interpreting of CT Images of Emergency Transport Patients by the Radiological Technologist─About the Factors That Influences Image Interpretation Assistance─
Nihon Hoshasen Gijutsu Gakkai zasshi, 2019Co-Authors: Shinji Tokunaga, Tsukasa Doi, Youji TabataAbstract:The Japanese Ministry of Health, Labor and Welfare announced a revision of the law about the expansion of duties by the radiological technologist in team medical care in April, 2010. In that respect, the importance of reading Images with support from the radiological technologist became higher. We compared the interpretation results of the radiologist with the Image analysis by the radiological technologist of the CT Images of emergency patients. And we checked for symptoms and diseases which were frequently overlooked by the technologists. Inexperienced radiological technologists overlooked considerably more than experienced radiological technologists. Our results showed that abdominal or Chest Image analysis differed more often than head scan analysis. The reasons given for the differences include a lack of clear indication for abdominal tumors, and we overlooked a lot of diseases such as pneumonia and enteritis. We also had several cases of abdominal and Chest Images over-reading by radiological technologists. To improve these, radiological technologists should deepen their knowledge of normal anatomy and work to improve recalling diseases that are inferred from the patient's symptoms. This will greatly improve the Image interpretation support by the radiological technologists.
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examination of ability to support the interpreting of ct Images of emergency transport patients by the radiological technologist about the factors that influences Image interpretation assistance
Nihon Hōshasen Gijutsu Gakkai zasshi, 2019Co-Authors: Shinji Tokunaga, Tsukasa Doi, Youji TabataAbstract:The Japanese Ministry of Health, Labor and Welfare announced a revision of the law about the expansion of duties by the radiological technologist in team medical care in April, 2010. In that respect, the importance of reading Images with support from the radiological technologist became higher. We compared the interpretation results of the radiologist with the Image analysis by the radiological technologist of the CT Images of emergency patients. And we checked for symptoms and diseases which were frequently overlooked by the technologists. Inexperienced radiological technologists overlooked considerably more than experienced radiological technologists. Our results showed that abdominal or Chest Image analysis differed more often than head scan analysis. The reasons given for the differences include a lack of clear indication for abdominal tumors, and we overlooked a lot of diseases such as pneumonia and enteritis. We also had several cases of abdominal and Chest Images over-reading by radiological technologists. To improve these, radiological technologists should deepen their knowledge of normal anatomy and work to improve recalling diseases that are inferred from the patient's symptoms. This will greatly improve the Image interpretation support by the radiological technologists.
Maryellen L Giger - One of the best experts on this subject based on the ideXlab platform.
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development of an improved cad scheme for automated detection of lung nodules in digital Chest Images
Medical Physics, 1997Co-Authors: Kunio Doi, Takeshi Kobayashi, Heber Macmahon, Maryellen L GigerAbstract:Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital Chest Images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA Chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference Image (which corresponds to the subtraction of a signal-enhanced Image and a signal-suppressed Image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per Chest Image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one Chest Image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.
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Adaptive feature analysis of false positives for computerized detection of lung nodules in digital Chest Images
Medical Imaging 1997: Image Processing, 1997Co-Authors: Heber Macmahon, Maryellen L Giger, Kunio DoiAbstract:To assist radiologists in diagnosing early lung cancer, we have developed a computer-aided diagnosis (CAD) scheme for automated detection of lung nodules in digital Chest Images. The database used for this study consisted of two hundred PA Chest radiographs, including 100 normals and 100 abnormals. Our CAD scheme has four basic steps, namely, (1) preprocessing, (2) identification of initial nodule candidates (rule-based test #1), (3) grouping of initial nodule candidates into six groups, and (4) elimination of false positives (rule-based test #2 - #5 and artificial neural network). Our CAD scheme achieves, on average, a sensitivity of 70%, with 1.7 false positives per Chest Image. We believe that this CAD scheme with its current performance is ready for clinical evaluation.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.