International Journal of Biomedical Imaging最新文献

{"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":"Water Cycle Bat Algorithm and Dictionary-Based Deformable Model for Lung Tumor Segmentation.","authors":"Mamtha V Shetty,&nbsp;D Jayadevappa,&nbsp;G N Veena","doi":"10.1155/2021/3492099","DOIUrl":"https://doi.org/10.1155/2021/3492099","url":null,"abstract":"<p><p>Among the different types of cancers, lung cancer is one of the widespread diseases which causes the highest number of deaths every year. The early detection of lung cancer is very essential for increasing the survival rate in patients. Although computed tomography (CT) is the preferred choice for lungs imaging, sometimes CT images may produce less tumor visibility regions and unconstructive rates in tumor portions. Hence, the development of an efficient segmentation technique is necessary. In this paper, water cycle bat algorithm- (WCBA-) based deformable model approach is proposed for lung tumor segmentation. In the preprocessing stage, a median filter is used to remove the noise from the input image and to segment the lung lobe regions, and Bayesian fuzzy clustering is applied. In the proposed method, deformable model is modified by the dictionary-based algorithm to segment the lung tumor accurately. In the dictionary-based algorithm, the update equation is modified by the proposed WCBA and is designed by integrating water cycle algorithm (WCA) and bat algorithm (BA).</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":"3492099"},"PeriodicalIF":7.6,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8629667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39951397","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":"Automatic Quantification of Atherosclerosis in Contrast-Enhanced MicroCT Scans of Mouse Aortas Ex Vivo.","authors":"Vincent A Stadelmann,&nbsp;Gabrielle Boyd,&nbsp;Martin Guillot,&nbsp;Jean-Guy Bienvenu,&nbsp;Charles Glaus,&nbsp;Aurore Varela","doi":"10.1155/2021/4998786","DOIUrl":"https://doi.org/10.1155/2021/4998786","url":null,"abstract":"<p><strong>Objective: </strong>While microCT evaluation of atherosclerotic lesions in mice has been formally validated, existing image processing methods remain undisclosed. We aimed to develop and validate a reproducible image processing workflow based on phosphotungstic acid-enhanced microCT scans for the volumetric quantification of atherosclerotic lesions in entire mouse aortas. <i>Approach and Results</i>. 42 WT and 42 apolipoprotein E knockout mouse aortas were scanned. The walls, lumen, and plaque objects were segmented using dual-threshold algorithms. Aortic and plaque volumes were computed by voxel counting and lesion surface by triangulation. The results were validated against manual and histological evaluations. Knockout mice had a significant increase in plaque volume compared to wild types with a plaque to aorta volume ratio of 0.3%, 2.8%, and 9.8% at weeks 13, 18, and 26, respectively. Automatic segmentation correlated with manual (<i>r</i> ² ≥ 0.89; <i>p</i> < .001) and histological evaluations (<i>r</i> ² > 0.96; <i>p</i> < .001).</p><p><strong>Conclusions: </strong>The semiautomatic workflow enabled rapid quantification of atherosclerotic plaques in mice with minimal manual work.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":"4998786"},"PeriodicalIF":7.6,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39474114","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":"Corrigendum to \"Robust Diffeomorphic Mapping via Geodesically Controlled Active Shapes\".","authors":"Daniel J Tward,&nbsp;Jun Ma,&nbsp;Michael I Miller,&nbsp;Laurent Younes","doi":"10.1155/2021/9780202","DOIUrl":"https://doi.org/10.1155/2021/9780202","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1155/2013/205494.].</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":"9780202"},"PeriodicalIF":7.6,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39239836","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":"Geometric Regularized Hopfield Neural Network for Medical Image Enhancement.","authors":"Fayadh Alenezi,&nbsp;K C Santosh","doi":"10.1155/2021/6664569","DOIUrl":"https://doi.org/10.1155/2021/6664569","url":null,"abstract":"<p><p>One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":" ","pages":"6664569"},"PeriodicalIF":7.6,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7847341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25341347","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":"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}