Pub Date : 2020-01-06eCollection Date: 2020-01-01DOI: 10.1155/2020/9035096
Mohamed Nasor, Walid Obaid
Brain tumors are a major health problem that affect the lives of many people. These tumors are classified as benign or cancerous. The latter can be fatal if not properly diagnosed and treated. Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patient's full recovery after treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.
{"title":"Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting.","authors":"Mohamed Nasor, Walid Obaid","doi":"10.1155/2020/9035096","DOIUrl":"https://doi.org/10.1155/2020/9035096","url":null,"abstract":"<p><p>Brain tumors are a major health problem that affect the lives of many people. These tumors are classified as benign or cancerous. The latter can be fatal if not properly diagnosed and treated. Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patient's full recovery after treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of <i>k</i>-means clustering, patch-based image processing, object counting, and tumor evaluation. The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2020 ","pages":"9035096"},"PeriodicalIF":7.6,"publicationDate":"2020-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2020/9035096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38010019","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}
[This corrects the article DOI: 10.1155/2017/6028645.].
[这更正了文章DOI: 10.1155/2017/6028645]。
{"title":"Corrigendum to “Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery”","authors":"Siming Bayer, A. Maier, M. Ostermeier, R. Fahrig","doi":"10.1155/2019/9249016","DOIUrl":"https://doi.org/10.1155/2019/9249016","url":null,"abstract":"[This corrects the article DOI: 10.1155/2017/6028645.].","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/9249016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48243249","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}
Mario Amrehn, S. Steidl, Reinier Kortekaas, Maddalena Strumia, M. Weingarten, M. Kowarschik, A. Maier
For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype's user interface (UI) features and segmentation methodologies.
{"title":"A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems","authors":"Mario Amrehn, S. Steidl, Reinier Kortekaas, Maddalena Strumia, M. Weingarten, M. Kowarschik, A. Maier","doi":"10.1155/2019/1464592","DOIUrl":"https://doi.org/10.1155/2019/1464592","url":null,"abstract":"For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype's user interface (UI) features and segmentation methodologies.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2019 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/1464592","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47847902","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}
Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.
{"title":"Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction","authors":"K. Cauley, G. Mongelluzzo, S. Fielden","doi":"10.1155/2019/1720270","DOIUrl":"https://doi.org/10.1155/2019/1720270","url":null,"abstract":"Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/1720270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48989856","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}
Devkumar Mustafi, Abby Leinroth, Xiaobing Fan, Erica Markiewicz, Marta Zamora, Jeffrey Mueller, Suzanne D Conzen, Gregory S Karczmar
Breast cancer is a major cause of morbidity and mortality in Western women. Tumor neoangiogenesis, the formation of new blood vessels from pre-existing ones, may be used as a prognostic marker for cancer progression. Clinical practice uses dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) to detect cancers based on increased blood flow and capillary permeability. However, DCE-MRI requires repeated injections of contrast media. Therefore we explored the use of noninvasive time-of-flight (TOF) MR angiography for serial studies of mouse mammary glands to measure the number and size of arteries feeding mammary glands with and without cancer. Virgin female C3(1) SV40 TAg mice (n=9), aged 18-20 weeks, were imaged on a 9.4 Tesla small animal scanner. Multislice T2-weighted (T2W) images and TOF-MRI angiograms were acquired over inguinal mouse mammary glands. The data were analyzed to determine tumor burden in each mammary gland and the volume of arteries feeding each mammary gland. After in vivo MRI, inguinal mammary glands were excised and fixed in formalin for histology. TOF angiography detected arteries with a diameter as small as 0.1 mm feeding the mammary glands. A significant correlation (r=0.79; p< 0.0001) was found between tumor volume and the arterial blood volume measured in mammary glands. Mammary arterial blood volumes ranging from 0.08 mm3 to 3.81 mm3 were measured. Tumors and blood vessels found on in vivo T2W and TOF images, respectively, were confirmed with ex vivo histological images. These results demonstrate increased recruitment of arteries to mammary glands with cancer, likely associated with neoangiogenesis. Neoangiogenesis may be detected by TOF angiography without injection of contrast agents. This would be very useful in mouse models where repeat placement of I.V. lines is challenging. In addition, analogous methods could be tested in humans to evaluate the vasculature of suspicious lesions without using contrast agents.
{"title":"Magnetic Resonance Angiography Shows Increased Arterial Blood Supply Associated with Murine Mammary Cancer.","authors":"Devkumar Mustafi, Abby Leinroth, Xiaobing Fan, Erica Markiewicz, Marta Zamora, Jeffrey Mueller, Suzanne D Conzen, Gregory S Karczmar","doi":"10.1155/2019/5987425","DOIUrl":"https://doi.org/10.1155/2019/5987425","url":null,"abstract":"Breast cancer is a major cause of morbidity and mortality in Western women. Tumor neoangiogenesis, the formation of new blood vessels from pre-existing ones, may be used as a prognostic marker for cancer progression. Clinical practice uses dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) to detect cancers based on increased blood flow and capillary permeability. However, DCE-MRI requires repeated injections of contrast media. Therefore we explored the use of noninvasive time-of-flight (TOF) MR angiography for serial studies of mouse mammary glands to measure the number and size of arteries feeding mammary glands with and without cancer. Virgin female C3(1) SV40 TAg mice (n=9), aged 18-20 weeks, were imaged on a 9.4 Tesla small animal scanner. Multislice T2-weighted (T2W) images and TOF-MRI angiograms were acquired over inguinal mouse mammary glands. The data were analyzed to determine tumor burden in each mammary gland and the volume of arteries feeding each mammary gland. After in vivo MRI, inguinal mammary glands were excised and fixed in formalin for histology. TOF angiography detected arteries with a diameter as small as 0.1 mm feeding the mammary glands. A significant correlation (r=0.79; p< 0.0001) was found between tumor volume and the arterial blood volume measured in mammary glands. Mammary arterial blood volumes ranging from 0.08 mm3 to 3.81 mm3 were measured. Tumors and blood vessels found on in vivo T2W and TOF images, respectively, were confirmed with ex vivo histological images. These results demonstrate increased recruitment of arteries to mammary glands with cancer, likely associated with neoangiogenesis. Neoangiogenesis may be detected by TOF angiography without injection of contrast agents. This would be very useful in mouse models where repeat placement of I.V. lines is challenging. In addition, analogous methods could be tested in humans to evaluate the vasculature of suspicious lesions without using contrast agents.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2019 ","pages":"5987425"},"PeriodicalIF":7.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/5987425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10679678","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}
Pub Date : 2018-10-18eCollection Date: 2018-01-01DOI: 10.1155/2018/9752638
S K Chaya Devi, T Satya Savithri
Lung cancer is one of the major types of cancer in the world. Survival rate can be increased if the disease can be identified early. Posterior and anterior chest radiography and computerized tomography scans are the most used diagnosis techniques for detecting tumor from lungs. Posterior and anterior chest radiography requires less radiation dose and is available in most of the diagnostic centers and it costs less compared to the remaining diagnosis techniques. So PA chest radiography became the most commonly used technique for lung cancer detection. Because of superimposed anatomical structures present in the image, sometimes radiologists cannot find abnormalities from the image. To help radiologists in diagnosing tumor from PA chest radiographic images range of CAD scheme has been developed for the past three decades. These computerized tools may be used by radiologists as a second opinion in detecting tumor. Literature survey on detecting tumors from chest graphs is presented in this paper.
{"title":"Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs.","authors":"S K Chaya Devi, T Satya Savithri","doi":"10.1155/2018/9752638","DOIUrl":"https://doi.org/10.1155/2018/9752638","url":null,"abstract":"<p><p>Lung cancer is one of the major types of cancer in the world. Survival rate can be increased if the disease can be identified early. Posterior and anterior chest radiography and computerized tomography scans are the most used diagnosis techniques for detecting tumor from lungs. Posterior and anterior chest radiography requires less radiation dose and is available in most of the diagnostic centers and it costs less compared to the remaining diagnosis techniques. So PA chest radiography became the most commonly used technique for lung cancer detection. Because of superimposed anatomical structures present in the image, sometimes radiologists cannot find abnormalities from the image. To help radiologists in diagnosing tumor from PA chest radiographic images range of CAD scheme has been developed for the past three decades. These computerized tools may be used by radiologists as a second opinion in detecting tumor. Literature survey on detecting tumors from chest graphs is presented in this paper.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"9752638"},"PeriodicalIF":7.6,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/9752638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36781771","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}
Pub Date : 2018-09-10eCollection Date: 2018-01-01DOI: 10.1155/2018/5812872
Ahmed I Sharaf, Mohamed Abu El-Soud, Ibrahim M El-Henawy
Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew's correlation coefficient.
{"title":"An Automated Approach for Epilepsy Detection Based on Tunable <i>Q</i>-Wavelet and Firefly Feature Selection Algorithm.","authors":"Ahmed I Sharaf, Mohamed Abu El-Soud, Ibrahim M El-Henawy","doi":"10.1155/2018/5812872","DOIUrl":"https://doi.org/10.1155/2018/5812872","url":null,"abstract":"<p><p>Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable <i>Q</i>-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew's correlation coefficient.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"5812872"},"PeriodicalIF":7.6,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/5812872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36541471","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}
Pub Date : 2018-09-02eCollection Date: 2018-01-01DOI: 10.1155/2018/9262847
Naoki Kawamura, Tatsuya Yokota, Hidekata Hontani
Given a low-resolution image, there are many challenges to obtain a super-resolved, high-resolution image. Many of those approaches try to simultaneously upsample and deblur an image in signal domain. However, the nature of the super-resolution is to restore high-frequency components in frequency domain rather than upsampling in signal domain. In that sense, there is a close relationship between super-resolution of an image and extrapolation of the spectrum. In this study, we propose a novel framework for super-resolution, where the high-frequency components are theoretically restored with respect to the frequency fidelities. This framework helps to introduce multiple simultaneous regularizers in both signal and frequency domains. Furthermore, we propose a new super-resolution model where frequency fidelity, low-rank (LR) prior, low total variation (TV) prior, and boundary prior are considered at once. The proposed method is formulated as a convex optimization problem which can be solved by the alternating direction method of multipliers. The proposed method is the generalized form of the multiple super-resolution methods such as TV super-resolution, LR and TV super-resolution, and the Gerchberg method. Experimental results show the utility of the proposed method comparing with some existing methods using both simulational and practical images.
{"title":"Super-Resolution of Magnetic Resonance Images via Convex Optimization with Local and Global Prior Regularization and Spectrum Fitting.","authors":"Naoki Kawamura, Tatsuya Yokota, Hidekata Hontani","doi":"10.1155/2018/9262847","DOIUrl":"https://doi.org/10.1155/2018/9262847","url":null,"abstract":"<p><p>Given a low-resolution image, there are many challenges to obtain a super-resolved, high-resolution image. Many of those approaches try to simultaneously upsample and deblur an image in signal domain. However, the nature of the super-resolution is to restore high-frequency components in frequency domain rather than upsampling in signal domain. In that sense, there is a close relationship between super-resolution of an image and extrapolation of the spectrum. In this study, we propose a novel framework for super-resolution, where the high-frequency components are theoretically restored with respect to the frequency fidelities. This framework helps to introduce multiple simultaneous regularizers in both signal and frequency domains. Furthermore, we propose a new super-resolution model where frequency fidelity, low-rank (LR) prior, low total variation (TV) prior, and boundary prior are considered at once. The proposed method is formulated as a convex optimization problem which can be solved by the alternating direction method of multipliers. The proposed method is the generalized form of the multiple super-resolution methods such as TV super-resolution, LR and TV super-resolution, and the Gerchberg method. Experimental results show the utility of the proposed method comparing with some existing methods using both simulational and practical images.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"9262847"},"PeriodicalIF":7.6,"publicationDate":"2018-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/9262847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36518770","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}
Pub Date : 2018-08-09eCollection Date: 2018-01-01DOI: 10.1155/2018/5932653
Thomas Weidinger, Thorsten M Buzug, Thomas G Flohr, Steffen Kappler, Karl Stierstorfer
[This corrects the article DOI: 10.1155/2016/5871604.].
[这更正了文章DOI: 10.1155/2016/5871604]。
{"title":"Corrigendum to \"Polychromatic Iterative Statistical Material Image Reconstruction for Photon-Counting Computed Tomography\".","authors":"Thomas Weidinger, Thorsten M Buzug, Thomas G Flohr, Steffen Kappler, Karl Stierstorfer","doi":"10.1155/2018/5932653","DOIUrl":"https://doi.org/10.1155/2018/5932653","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1155/2016/5871604.].</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"5932653"},"PeriodicalIF":7.6,"publicationDate":"2018-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/5932653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36455689","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}
Pub Date : 2018-07-02eCollection Date: 2018-01-01DOI: 10.1155/2018/2572431
Xu Zhang, Xilin Liu, Yang Chen, Huazhong Shu
A new blind integrity verification method for medical image is proposed in this paper. It is based on a new kind of image features, known as Krawtchouk moments, which we use to distinguish the original images from the modified ones. Basically, with our scheme, image integrity verification is accomplished by classifying images into the original and modified categories. Experiments conducted on medical images issued from different modalities verified the validity of the proposed method and demonstrated that it can be used to detect and discriminate image modifications of different types with high accuracy. We also compared the performance of our scheme with a state-of-the-art solution suggested for medical images-solution that is based on histogram statistical properties of reorganized block-based Tchebichef moments. Conducted tests proved the better behavior of our image feature set.
{"title":"Medical Image Blind Integrity Verification with Krawtchouk Moments.","authors":"Xu Zhang, Xilin Liu, Yang Chen, Huazhong Shu","doi":"10.1155/2018/2572431","DOIUrl":"https://doi.org/10.1155/2018/2572431","url":null,"abstract":"<p><p>A new blind integrity verification method for medical image is proposed in this paper. It is based on a new kind of image features, known as Krawtchouk moments, which we use to distinguish the original images from the modified ones. Basically, with our scheme, image integrity verification is accomplished by classifying images into the original and modified categories. Experiments conducted on medical images issued from different modalities verified the validity of the proposed method and demonstrated that it can be used to detect and discriminate image modifications of different types with high accuracy. We also compared the performance of our scheme with a state-of-the-art solution suggested for medical images-solution that is based on histogram statistical properties of reorganized block-based Tchebichef moments. Conducted tests proved the better behavior of our image feature set.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2018 ","pages":"2572431"},"PeriodicalIF":7.6,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2018/2572431","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36351567","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}