International Journal of Biomedical Imaging最新文献

{"title":"Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations.","authors":"Chong Zhang,&nbsp;Xuanjing Shen,&nbsp;Hang Cheng,&nbsp;Qingji Qian","doi":"10.1155/2019/7305832","DOIUrl":"10.1155/2019/7305832","url":null,"abstract":"<p><p>Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method's sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm's stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/7305832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37242426","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":"Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area.","authors":"Abdelmajid Bousselham,&nbsp;Omar Bouattane,&nbsp;Mohamed Youssfi,&nbsp;Abdelhadi Raihani","doi":"10.1155/2019/1758948","DOIUrl":"https://doi.org/10.1155/2019/1758948","url":null,"abstract":"<p><p>Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan-Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2019-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/1758948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37116337","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":"An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation.","authors":"Berkay Kanberoglu,&nbsp;Dhritiman Das,&nbsp;Priya Nair,&nbsp;Pavan Turaga,&nbsp;David Frakes","doi":"10.1155/2019/9435163","DOIUrl":"10.1155/2019/9435163","url":null,"abstract":"<p><p>Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images. The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation. Optical flow and/or other registration-based interpolators have proven useful in such interpolation roles in the past. When acquired images are comprised of signals that describe the flow velocity of fluids, additional information is available to guide the interpolation process. In this paper, we present an optical-flow based framework for image interpolation that also minimizes resultant divergence in the interpolated data.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2019-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/9435163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37049103","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":"Measuring Surface Area of Skin Lesions with 2D and 3D Algorithms.","authors":"Houman Mirzaalian Dastjerdi,&nbsp;Dominique Töpfer,&nbsp;Stefan J Rupitsch,&nbsp;Andreas Maier","doi":"10.1155/2019/4035148","DOIUrl":"https://doi.org/10.1155/2019/4035148","url":null,"abstract":"<p><strong>Purpose: </strong>The treatment of skin lesions of various kinds is a common task in clinical routine. Apart from wound care, the assessment of treatment efficacy plays an important role. In this paper, we present a new approach to measure the skin lesion surface in two and three dimensions.</p><p><strong>Methods: </strong>For the 2D approach, a single photo containing a flexible paper ruler is taken. After semi-automatic segmentation of the lesion, evaluation is based on local scale estimation using the ruler. For the 3D approach, reconstruction is based on Structure from Motion. Roughly outlining the region of interest around the lesion is required for both methods.</p><p><strong>Results: </strong>The measurement evaluation was performed on 117 phantom images and five phantom videos for 2D and 3D approach, respectively. We found an absolute error of 0.99±1.18  cm² and a relative error 9.89± 9.31% for 2D. These errors are <1  cm² and <5% for five test phantoms in our 3D case. As expected, the error of 2D surface area measurement increased by approximately 10% for wounds on the bent surface compared to wounds on the flat surface. Using our method, the only user interaction is to roughly outline the region of interest around the lesion.</p><p><strong>Conclusions: </strong>We developed a new wound segmentation and surface area measurement technique for skin lesions even on a bent surface. The 2D technique provides the user with a fast, user-friendly segmentation and measurement tool with reasonable accuracy for home care assessment of treatment. For 3D only preliminary results could be provided. Measurements were only based on phantoms and have to be repeated with real clinical data.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2019-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2019/4035148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36976156","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":"Magnetic Resonance Angiography Shows Increased Arterial Blood Supply Associated with Murine Mammary Cancer.","authors":"Devkumar Mustafi,&nbsp;Abby Leinroth,&nbsp;Xiaobing Fan,&nbsp;Erica Markiewicz,&nbsp;Marta Zamora,&nbsp;Jeffrey Mueller,&nbsp;Suzanne D Conzen,&nbsp;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":null,"pages":null},"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}

{"title":"Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs.","authors":"S K Chaya Devi,&nbsp;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":null,"pages":null},"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}

{"title":"An Automated Approach for Epilepsy Detection Based on Tunable <i>Q</i>-Wavelet and Firefly Feature Selection Algorithm.","authors":"Ahmed I Sharaf,&nbsp;Mohamed Abu El-Soud,&nbsp;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":null,"pages":null},"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}

{"title":"Super-Resolution of Magnetic Resonance Images via Convex Optimization with Local and Global Prior Regularization and Spectrum Fitting.","authors":"Naoki Kawamura,&nbsp;Tatsuya Yokota,&nbsp;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":null,"pages":null},"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}

{"title":"Corrigendum to \"Polychromatic Iterative Statistical Material Image Reconstruction for Photon-Counting Computed Tomography\".","authors":"Thomas Weidinger,&nbsp;Thorsten M Buzug,&nbsp;Thomas G Flohr,&nbsp;Steffen Kappler,&nbsp;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":null,"pages":null},"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}

{"title":"Medical Image Blind Integrity Verification with Krawtchouk Moments.","authors":"Xu Zhang,&nbsp;Xilin Liu,&nbsp;Yang Chen,&nbsp;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":null,"pages":null},"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}