International Journal of Biomedical Imaging最新文献

{"title":"Relative Perfusion Differences between Parathyroid Adenomas and the Thyroid on Multiphase 4DCT","authors":"S. Raeymaeckers, Yannick De Brucker, Maurizio Tosi, N. Buls, J. Mey","doi":"10.1155/2022/2984789","DOIUrl":"https://doi.org/10.1155/2022/2984789","url":null,"abstract":"A multiphase 4DCT technique can be useful for the detection of parathyroid adenomas. Up to 16 different phases can be obtained without significant increase of exposure dose using wide beam axial scanning. This technique also allows for the calculation of perfusion parameters in suspected lesions. We present data on 19 patients with histologically proven parathyroid adenomas. We find a strong correlation between 2 perfusion parameters when comparing parathyroid adenomas and thyroid tissue: parathyroid adenomas show a 55% increase in blood flow (BF) (p < 0.001) and a 50% increase in blood volume (BV) (p < 0.001) as compared to normal thyroid tissue. The analysis of the ROC curve for the different perfusion parameters demonstrates a significantly high area under the curve for BF and BV, confirming these two perfusion parameters to be a possible discriminating tool to discern between parathyroid adenomas and thyroid tissue. These findings can help to discern parathyroid from thyroid tissue and may aid in the detection of parathyroid adenomas.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47701953","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}

{"title":"MRI Reconstruction with Separate Magnitude and Phase Priors Based on Dual-Tree Complex Wavelet Transform","authors":"W. He, Linman Zhao","doi":"10.1155/2022/7251674","DOIUrl":"https://doi.org/10.1155/2022/7251674","url":null,"abstract":"The methods of compressed sensing magnetic resonance imaging (CS-MRI) can be divided into two categories roughly based on the number of target variables. One group devotes to estimating the complex-valued MRI image. And the other calculates the magnitude and phase parts of the complex-valued MRI image, respectively, by enforcing separate penalties on them. We propose a new CS-based method based on dual-tree complex wavelet (DT CWT) sparsity, which is under the frame of the second class of CS-MRI. Owing to the separate regularization frame, this method reduces the impact of the phase jumps (that means the jumps or discontinuities of phase values) on magnitude reconstruction. Moreover, by virtue of the excellent features of DT CWT, such as nonoscillating envelope of coefficients and multidirectional selectivity, the proposed method is capable of capturing more details in the magnitude and phase images. The experimental results show that the proposed method recovers the image contour and edges information well and can eliminate the artifacts in magnitude results caused by phase jumps.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49417825","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}

{"title":"Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization","authors":"Dr. MANOHARAN SUBRAMANIAN, Velmurugan Lingamuthu, Chandran Venkatesan, S. Perumal","doi":"10.1155/2022/3211793","DOIUrl":"https://doi.org/10.1155/2022/3211793","url":null,"abstract":"In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2022 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44197574","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}

{"title":"Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image","authors":"R. K. Hapsari, Miswanto, R. Rulaningtyas, H. Suprajitno, H. Gan","doi":"10.1155/2022/5336373","DOIUrl":"https://doi.org/10.1155/2022/5336373","url":null,"abstract":"Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44414554","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}

{"title":"Value CMR: Towards a Comprehensive, Rapid, Cost-Effective Cardiovascular Magnetic Resonance Imaging.","authors":"El-Sayed H Ibrahim,&nbsp;Luba Frank,&nbsp;Dhiraj Baruah,&nbsp;V Emre Arpinar,&nbsp;Andrew S Nencka,&nbsp;Kevin M Koch,&nbsp;L Tugan Muftuler,&nbsp;Orhan Unal,&nbsp;Jadranka Stojanovska,&nbsp;Jason C Rubenstein,&nbsp;Sherry-Ann Brown,&nbsp;John Charlson,&nbsp;Elizabeth M Gore,&nbsp;Carmen Bergom","doi":"10.1155/2021/8851958","DOIUrl":"https://doi.org/10.1155/2021/8851958","url":null,"abstract":"<p><p>Cardiac magnetic resonance imaging (CMR) is considered the gold standard for measuring cardiac function. Further, in a single CMR exam, information about cardiac structure, tissue composition, and blood flow could be obtained. Nevertheless, CMR is underutilized due to long scanning times, the need for multiple breath-holds, use of a contrast agent, and relatively high cost. In this work, we propose a rapid, comprehensive, contrast-free CMR exam that does not require repeated breath-holds, based on recent developments in imaging sequences. Time-consuming conventional sequences have been replaced by advanced sequences in the proposed CMR exam. Specifically, conventional 2D cine and phase-contrast (PC) sequences have been replaced by optimized 3D-cine and 4D-flow sequences, respectively. Furthermore, conventional myocardial tagging has been replaced by fast strain-encoding (SENC) imaging. Finally, T1 and T2 mapping sequences are included in the proposed exam, which allows for myocardial tissue characterization. The proposed rapid exam has been tested in vivo. The proposed exam reduced the scan time from >1 hour with conventional sequences to <20 minutes. Corresponding cardiovascular measurements from the proposed rapid CMR exam showed good agreement with those from conventional sequences and showed that they can differentiate between healthy volunteers and patients. Compared to 2D cine imaging that requires 12-16 separate breath-holds, the implemented 3D-cine sequence allows for whole heart coverage in 1-2 breath-holds. The 4D-flow sequence allows for whole-chest coverage in less than 10 minutes. Finally, SENC imaging reduces scan time to only one slice per heartbeat. In conclusion, the proposed rapid, contrast-free, and comprehensive cardiovascular exam does not require repeated breath-holds or to be supervised by a cardiac imager. These improvements make it tolerable by patients and would help improve cost effectiveness of CMR and increase its adoption in clinical practice.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2021 ","pages":"8851958"},"PeriodicalIF":7.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9653604","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}

{"title":"Three-Dimensional Imaging of Pulmonary Fibrotic Foci at the Alveolar Scale Using Tissue-Clearing Treatment with Staining Techniques of Extracellular Matrix.","authors":"Kohei Togami,&nbsp;Hiroaki Ozaki,&nbsp;Yuki Yumita,&nbsp;Anri Kitayama,&nbsp;Hitoshi Tada,&nbsp;Sumio Chono","doi":"10.1155/2020/8815231","DOIUrl":"https://doi.org/10.1155/2020/8815231","url":null,"abstract":"<p><p>Idiopathic pulmonary fibrosis is a progressive, chronic lung disease characterized by the accumulation of extracellular matrix proteins, including collagen and elastin. Imaging of extracellular matrix in fibrotic lungs is important for evaluating its pathological condition as well as the distribution of drugs to pulmonary focus sites and their therapeutic effects. In this study, we compared techniques of staining the extracellular matrix with optical tissue-clearing treatment for developing three-dimensional imaging methods for focus sites in pulmonary fibrosis. Mouse models of pulmonary fibrosis were prepared via the intrapulmonary administration of bleomycin. Fluorescent-labeled tomato lectin, collagen I antibody, and Col-F, which is a fluorescent probe for collagen and elastin, were used to compare the imaging of fibrotic foci in intact fibrotic lungs. These lung samples were cleared using the Clear^T2 tissue-clearing technique. The cleared lungs were two dimensionally observed using laser-scanning confocal microscopy, and the images were compared with those of the lung tissue sections. Moreover, three-dimensional images were reconstructed from serial two-dimensional images. Fluorescent-labeled tomato lectin did not enable the visualization of fibrotic foci in cleared fibrotic lungs. Although collagen I in fibrotic lungs could be visualized via immunofluorescence staining, collagen I was clearly visible only until 40 <i>μ</i>m from the lung surface. Col-F staining facilitated the visualization of collagen and elastin to a depth of 120 <i>μ</i>m in cleared lung tissues. Furthermore, we visualized the three-dimensional extracellular matrix in cleared fibrotic lungs using Col-F, and the images provided better visualization than immunofluorescence staining. These results suggest that Clear^T2 tissue-clearing treatment combined with Col-F staining represents a simple and rapid technique for imaging fibrotic foci in intact fibrotic lungs. This study provides important information for imaging various organs with extracellular matrix-related diseases.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2020 ","pages":"8815231"},"PeriodicalIF":7.6,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38827591","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}

{"title":"A Modified Phase Cycling Method for Complex-Valued MRI Reconstruction.","authors":"Wei He,&nbsp;Yu Zhang,&nbsp;Junling Ding,&nbsp;Linman Zhao","doi":"10.1155/2020/8846220","DOIUrl":"https://doi.org/10.1155/2020/8846220","url":null,"abstract":"<p><p>The phase cycling method is a state-of-the-art method to reconstruct complex-valued MR image. However, when it follows practical two-dimensional (2D) subsampling Cartesian acquisition which is only enforcing random sampling in the phase-encoding direction, a number of artifacts in magnitude appear. A modified approach is proposed to remove these artifacts under practical MRI subsampling, by adding one-dimensional total variation (TV) regularization into the phase cycling method to \"pre-process\" the magnitude component before its update. Furthermore, an operation used in SFISTA is employed to update the magnitude and phase images for better solutions. The results of the experiments show the ability of the proposed method to eliminate the ring artifacts and improve the magnitude reconstruction.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2020 ","pages":"8846220"},"PeriodicalIF":7.6,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2020/8846220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38680662","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}

{"title":"Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment.","authors":"Rui Liu, Yuanyuan Jia, Xiangqian He, Zhe Li, Jinhua Cai, Hao Li, Xiao Yang","doi":"10.1155/2020/8866700","DOIUrl":"10.1155/2020/8866700","url":null,"abstract":"<p><p>In the study of pediatric automatic bone age assessment (BAA) in clinical practice, the extraction of the object area in hand radiographs is an important part, which directly affects the prediction accuracy of the BAA. But no perfect segmentation solution has been found yet. This work is to develop an automatic hand radiograph segmentation method with high precision and efficiency. We considered the hand segmentation task as a classification problem. The optimal segmentation threshold for each image was regarded as the prediction target. We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. Furthermore, the experimental results also verified that hand radiograph segmentation could bring an average improvement for BAA performance of at least 13%.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2020 ","pages":"8866700"},"PeriodicalIF":7.6,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38593312","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}

{"title":"Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases.","authors":"Arun Sharma, Sheeba Rani, Dinesh Gupta","doi":"10.1155/2020/8889023","DOIUrl":"10.1155/2020/8889023","url":null,"abstract":"<p><p>The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2020 ","pages":"8889023"},"PeriodicalIF":7.6,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38498557","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}

{"title":"Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks","authors":"Mundher Mohammed Taresh, N. Zhu, T. Ali, Asaad Shakir Hameed, Modhi Lafta Mutar","doi":"10.1101/2020.08.25.20182170","DOIUrl":"https://doi.org/10.1101/2020.08.25.20182170","url":null,"abstract":"Novel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID- 19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images. Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was derived from several open sources of X-Rays, and the data available online. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PYCM was used to support the statistical parameters. The study revealed the superiority of Model vgg16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep Learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":"2021 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43397552","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}