Journal articles: 'Medical image sequences'

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Sagerer, G. "Automatic interpretation of medical image sequences." Pattern Recognition Letters 8, no. 2 (September 1988): 87–102. http://dx.doi.org/10.1016/0167-8655(88)90050-5.

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Wang, Xiaowei, Shoulin Yin, Muhammad Shafiq, Asif Ali Laghari, Shahid Karim, Omar Cheikhrouhou, Wajdi Alhakami, and Habib Hamam. "A New V-Net Convolutional Neural Network Based on Four-Dimensional Hyperchaotic System for Medical Image Encryption." Security and Communication Networks 2022 (February 14, 2022): 1–14. http://dx.doi.org/10.1155/2022/4260804.

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In the transmission of medical images, if the image is not processed, it is very likely to leak data and personal privacy, resulting in unpredictable consequences. Traditional encryption algorithms have limited ability to deal with complex data. The chaotic system is characterized by randomness and ergodicity, which has advantages over traditional encryption algorithms in image encryption processing. A novel V-net convolutional neural network (CNN) based on four-dimensional hyperchaotic system for medical image encryption is presented in this study. Firstly, the plaintext medical images are processed into 4D hyperchaotic sequence images, including image segmentation, chaotic system processing, and pseudorandom sequence generation. Then, V-net CNN is used to train chaotic sequences to eliminate the periodicity of chaotic sequences. Finally, the chaotic sequence image is diffused to change the raw image pixel to realize the encryption processing. Simulation test analysis demonstrates that the proposed algorithm has better effect, robustness, and plaintext sensitivity.

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Ding, Wei Li, Feng Jiang, and Jia Qing Yan. "Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method." Applied Mechanics and Materials 58-60 (June 2011): 2370–75. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2370.

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Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

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Malawski, Filip, and Lukasz Czekierda. "COMPRESSION OF IMAGE SEQUENCES IN INTERACTIVE MEDICAL TELECONSULTATIONS." Computer Science 18, no. 1 (2017): 95. http://dx.doi.org/10.7494/csci.2017.18.1.95.

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Yi, Fan, and Peihua Qiu. "Edge-Preserving Denoising of Image Sequences." Entropy 23, no. 10 (October 12, 2021): 1332. http://dx.doi.org/10.3390/e23101332.

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To monitor the Earth’s surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time. These image sequences give us a unique resource to study the Earth’s surface, changes of the Earth resource over time, and their implications in agriculture, geology, forestry, and more. Besides natural sciences, image sequences are also commonly used in functional magnetic resonance imaging (fMRI) of medical studies for understanding the functioning of brains and other organs. In practice, observed images almost always contain noise and other contaminations. For a reliable subsequent image analysis, it is important to remove such contaminations in advance. This paper focuses on image sequence denoising, which has not been well-discussed in the literature yet. To this end, an edge-preserving image denoising procedure is suggested. The suggested method is based on a jump-preserving local smoothing procedure, in which the bandwidths are chosen such that the possible spatio-temporal correlations in the observed image intensities are accommodated properly. Both theoretical arguments and numerical studies show that this method works well in the various cases considered.

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An, Dezhi, Jun Lu, Shengcai Zhang, Yan Li, and António M. Lopes. "A Novel Selective Encryption Method Based on Skin Lesion Detection." Mathematical Problems in Engineering 2020 (September 28, 2020): 1–13. http://dx.doi.org/10.1155/2020/7982192.

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Due to the semitrusted cloud, privacy protection of medical images in medical imaging clouds has become a precondition. For the privacy of patients and the security of medical images in the cloud, this paper proposes a selective encryption based on DNA sequence and chaotic maps for skin lesion image. Initially, we design a transition region-based level set evolution functional which is merged into a variational level set expression with two extra energy functionals, to segment skin lesion image. Once skin lesion detection has been performed, the detected skin lesion pixels are encrypted by employing chaotic systems and DNA sequences. We apply 2D-LASM and 1D-LSS to produce the pseudorandom sequences and use the hash function of the plaintext image to calculate the secret keys of the encryption system. Results demonstrate that the proposed segmentation method is particularly suitable for the detection of skin lesion images with strong noise and complex background. Meanwhile, security analysis also reveals that this selective encryption has a large security key space and high sensitivity to the plaintext image and the secret key.

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Ma, Bin, Bing Li, Xiao-Yu Wang, Chun-Peng Wang, Jian Li, and Yun-Qing Shi. "Code Division Multiplexing and Machine Learning Based Reversible Data Hiding Scheme for Medical Image." Security and Communication Networks 2019 (January 17, 2019): 1–9. http://dx.doi.org/10.1155/2019/4732632.

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In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.

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Wang, Yi Gang, Gang Yi Jiang, and Mei Yu. "Study on Medical Micro-Image Mosaic with SIFT Features." Advanced Materials Research 121-122 (June 2010): 476–81. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.476.

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In order to obtain panoramic view of medical micro-image in remote medical diagnosis system, we should mosaic micro-image sequence accurately in remote node. SIFT features are invariant to image scaling, rotation and translation. The aim of this study is to mosaic micro-image sequence by extracting SIFT features. Firstly, search coordinate of potential features through Gaussian pyramid (with 7 octaves × 6 scales) and then filter pseudo-keypoints. Secondly, describe these features with 128-D vector. The next step is to calculate matching keypoint pairs between two time-successive images according to minimum Euclidean distance. In this step, standard deviation comparing is proposed to eliminate wrong matching pairs and appropriate threshold(8000) through experiment is used to insure matching pairs. Then, construct image motion equation considering rotation and translation and compute motion parameter by solving equation according matching pairs. Finally mosaic all images. These steps are applied in 5 micro-image sequences such as slices of lung tissue, spleen, kidney, frog blood cell and sunflower tissue. The experiment results show that the gap between two images is vanishing and the proposed method can satisfy with medical micro-images sequence mosaic.

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Jiang, Huiyan, Hanqing Tan, and Benqiang Yang. "A Priori Knowledge and Probability Density Based Segmentation Method for Medical CT Image Sequences." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/769751.

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This paper briefly introduces a novel segmentation strategy for CT images sequences. As first step of our strategy, we extract a priori intensity statistical information from object region which is manually segmented by radiologists. Then we define a search scope for object and calculate probability density for each pixel in the scope using a voting mechanism. Moreover, we generate an optimal initial level set contour based on a priori shape of object of previous slice. Finally the modified distance regularity level set method utilizes boundaries feature and probability density to conform final object. The main contributions of this paper are as follows: a priori knowledge is effectively used to guide the determination of objects and a modified distance regularization level set method can accurately extract actual contour of object in a short time. The proposed method is compared to other seven state-of-the-art medical image segmentation methods on abdominal CT image sequences datasets. The evaluated results demonstrate our method performs better and has the potential for segmentation in CT image sequences.

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Huang, Tongyuan, Jia Xu, Yuling Yang, and Baoru Han. "Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition." Mathematics 10, no. 7 (April 2, 2022): 1154. http://dx.doi.org/10.3390/math10071154.

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With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.

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Kolhar, Manjur, and Sultan Mesfer Aldossary. "Privacy-Preserving Convolutional Bi-LSTM Network for Robust Analysis of Encrypted Time-Series Medical Images." AI 4, no. 3 (August 28, 2023): 706–20. http://dx.doi.org/10.3390/ai4030037.

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Deep learning (DL) algorithms can improve healthcare applications. DL has improved medical imaging diagnosis, therapy, and illness management. The use of deep learning algorithms on sensitive medical images presents privacy and data security problems. Improving medical imaging while protecting patient anonymity is difficult. Thus, privacy-preserving approaches for deep learning model training and inference are gaining popularity. These picture sequences are analyzed using state-of-the-art computer aided detection/diagnosis techniques (CAD). Algorithms that upload medical photos to servers pose privacy issues. This article presents a convolutional Bi-LSTM network to assess completely homomorphic-encrypted (HE) time-series medical images. From secret image sequences, convolutional blocks learn to extract selective spatial features and Bi-LSTM-based analytical sequence layers learn to encode time data. A weighted unit and sequence voting layer uses geographical with varying weights to boost efficiency and reduce incorrect diagnoses. Two rigid benchmarks—the CheXpert, and the BreaKHis public datasets—illustrate the framework’s efficacy. The technique outperforms numerous rival methods with an accuracy above 0.99 for both datasets. These results demonstrate that the proposed outline can extract visual representations and sequential dynamics from encrypted medical picture sequences, protecting privacy while attaining good medical image analysis performance.

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Kesavamurthy, T., and Subha Rani. "Dicom Color Medical Image Compression using 3D-SPIHT for Pacs Application." International Journal of Biomedical Science 4, no. 2 (June 15, 2008): 113–19. http://dx.doi.org/10.59566/ijbs.2008.4113.

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The proposed algorithm presents an application of 3D-SPIHT algorithm to color volumetric dicom medical images using 3D wavelet decomposition and a 3D spatial dependence tree. The wavelet decomposition is accomplished with biorthogonal 9/7 filters. 3D-SPIHT is the modern-day benchmark for three dimensional image compressions. The three-dimensional coding is based on the observation that the sequences of images are contiguous in the temporal axis and there is no motion between slices. Therefore, the 3D discrete wavelet transform can fully exploit the inter-slices correlations. The set partitioning techniques involve a progressive coding of the wavelet coefficients. The 3D-SPIHT is implemented and the Rate-distortion (Peak Signal-to-Noise Ratio (PSNR) vs. bit rate) performances are presented for volumetric medical datasets by using biorthogonal 9/7. The results are compared with the previous results of JPEG 2000 standards. Results show that 3D-SPIHT method exploits the color space relationships as well as maintaining the full embeddedness required by color image sequences compression and gives better performance in terms of the PSNR and compression ratio than the JPEG 2000. The results suggest an effective practical implementation for PACS applications.

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Thiyagalingam, Jeyarajan, Daniel Goodman, Julia A. Schnabel, Anne Trefethen, and Vicente Grau. "On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences." International Journal of Biomedical Imaging 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/137604.

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Images are ubiquitous in biomedical applications from basic research to clinical practice. With the rapid increase in resolution, dimensionality of the images and the need for real-time performance in many applications, computational requirements demand proper exploitation of multicore architectures. Towards this, GPU-specific implementations of image analysis algorithms are particularly promising. In this paper, we investigate the mapping of an enhanced motion estimation algorithm to novel GPU-specific architectures, the resulting challenges and benefits therein. Using a database of three-dimensional image sequences, we show that the mapping leads to substantial performance gains, up to a factor of 60, and can provide near-real-time experience. We also show how architectural peculiarities of these devices can be best exploited in the benefit of algorithms, most specifically for addressing the challenges related to their access patterns and different memory configurations. Finally, we evaluate the performance of the algorithm on three different GPU architectures and perform a comprehensive analysis of the results.

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Neelakanta, Perambur S., and Deepti Pappusetty. "Bioinformatics-Inspired Algorithms for 2D-Image Analysis-Application to Synthetic and Medical Images Part I." International Journal of Biomedical and Clinical Engineering 1, no. 1 (January 2012): 14–38. http://dx.doi.org/10.4018/ijbce.2012010102.

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To ascertain specific features in bio-/medical-images, a new avenue of using the so-called Needleman-Wunsch (NW) and Smith-Waterman (SW) algorithms (of bioinformatics) is indicated. In general, NW/SW algorithms are adopted in genomic science to obtain optimal (global and local) alignment of two linear sequences (like DNA nucleotide bases) to determine the similarity features between them and such 1D-sequence algorithms are presently extended to compare 2D-images via binary correlation. The efficacy of the proposed method is tested with synthetic images and a brain scan image. Thus, the way of finding the location of a distinct part in a synthetic image and that of a tumour in the brain scan image is demonstrated.

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Gong, Jian, Kangjian He, Lisiqi Xie, Dan Xu, and Tao Yang. "A Fast Image Guide Registration Supported by Single Direction Projected CBCT." Electronics 11, no. 4 (February 18, 2022): 645. http://dx.doi.org/10.3390/electronics11040645.

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Image registration is an important research topic in medical image-guided therapy, which is dedicated to registering the high-dose imaging sequences with low-dose/faster means. Registering computer tomography (CT) scanning sequences with cone beam computer tomography (CBCT) scanning sequences is a typical application and has been widely used in CBCT-guided radiotherapy. The main problem is the difference in image clarity of these two image sequences. To solve this problem, for the single projection image sequence matching tasks encountered in medical practice, a novel local quality based curved section encoding strategy is proposed in this paper, which is called the high-quality curved section (HQCS). As an optimized cross-section regularly encoded along the sequence of image, this curved section could be used in order to solve the matching problem. Referencing the independent ground truth provided by medical image physicians, with an experiment combined with the four most widely used indicators used on image registration, matching performance of HQCS on CT/CBCT datasets was tested with varying clarity. Experimental results show that the proposed HQCS can register the CT/CBCT effectively and outperforms the commonly used methods. Specifically, the proposed HQCS has low time complexity and higher scalability, which indicates that the application enhanced the task of diagnosis.

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Säring, D., H. Handels, and J. Ehrhardt. "Structure-preserving Interpolation of Temporal and Spatial Image Sequences Using an Optical Flow-based Method." Methods of Information in Medicine 46, no. 03 (2007): 300–307. http://dx.doi.org/10.1160/me9047.

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Summary Objectives: Modern tomographic imaging devices enable the acquisition of spatial and temporal image sequences. But, the spatial and temporal resolution of such devices is limited and therefore image interpolation techniques are needed to represent images at a desired level of discretization. This paper presents a method for structure-preserving interpolation between neighboring slices in temporal or spatial image sequences. Methods: In a first step, the spatiotemporal velocity field between image slices is determined using an optical flow-based registration method in order to establish spatial correspondence between adjacent slices. An iterative algorithm is applied using the spatial and temporal image derivatives and a spatiotemporal smoothing step. Afterwards, the calculated velocity field is used to generate an interpolated image at the desired time by averaging intensities between corresponding points. Three quantitative measures are defined to evaluate the performance of the interpolation method. Results: The behaviorand capability of the algorithm is demonstrated by synthetic images. A population of 17 temporal and spatial image sequences are utilized to compare the optical flow-based interpolation method to linear and shape-based interpolation. The quantitative results show that the optical flow-based method outperforms the linear and shape-based interpolation statistically significantly. Conclusions: The interpolation method presented is able to generate image sequences with appropriate spatial or temporal resolution needed for image comparison, analysis or visualization tasks. Quantitative and qualitative measures extracted from synthetic phantoms and medical image data show that the new method definitely has advantages over linear and shape-based interpolation.

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Buvat, J., H. Benali, F. Frouin, J. P. Basin, and R. Di Paola. "Target apex-seeking in factor analysis of medical image sequences." Physics in Medicine and Biology 38, no. 1 (January 1, 1993): 123–37. http://dx.doi.org/10.1088/0031-9155/38/1/009.

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Ukrit, Ferni, and G. R. Suresh. "Enhancing Efficiency of SPIHT Using HCC for Medical Image Sequences." i-manager's Journal on Computer Science 1, no. 2 (August 15, 2013): 1–7. http://dx.doi.org/10.26634/jcom.1.2.2446.

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Buvat, I., H. Benali, and R. Di Paola. "Statistical distribution of factors and factor images in factor analysis of medical image sequences." Physics in Medicine and Biology 43, no. 6 (June 1, 1998): 1695–711. http://dx.doi.org/10.1088/0031-9155/43/6/023.

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Hennessy, S. J., L. B. Wong, D. B. Yeates, and I. F. Miller. "Automated measurement of ciliary beat frequency." Journal of Applied Physiology 60, no. 6 (June 1, 1986): 2109–13. http://dx.doi.org/10.1152/jappl.1986.60.6.2109.

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Measurements of ciliary beat frequency using video images are dependent on observer interpretation. To obtain objective estimates of ciliary beat frequency from video-image sequences, a computer-based method was developed. Regions of interest of video-image sequences were selected and digitized. Variations in numerical values representing light intensity resulting from cilia beating were extracted and analyzed using autocorrelation techniques. The ciliary beat frequencies obtained for 14 in vitro experiments on ciliated cells or epithelium from the frog palate (Rana catesbeiana) over the range of frequencies 2–25 Hz correlated well with independent observer measurements (r = 0.979). The addition of such computer-based methods to video observer-based systems allows more objective and efficient determinations of ciliary beat frequency.

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Gambini, Juliana, Sasha Hurovitz, Debora Chan, and Rodrigo Ramele. "Object Detection and Statistical Analysis of Microscopy Image Sequences." ELCVIA Electronic Letters on Computer Vision and Image Analysis 21, no. 1 (April 28, 2022): 47–58. http://dx.doi.org/10.5565/rev/elcvia.1482.

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Confocal microscope images are wide useful in medical diagnosis and research. The automatic interpretation of this type of images is very important but it is a challenging endeavor in image processing area, since these images are heavily contaminated with noise, have low contrast and low resolution. This work deals with the problem of analyzing the penetration velocity of a chemotherapy drug in an ocular tumor called retinoblastoma. The primary retinoblastoma cells cultures are exposed to topotecan drug and the penetration evolution is documented by producing sequences of microscopy images. It is possible to quantify the penetration rate of topotecan drug because it produces fluorescence emission by laser excitation which is captured by the camera.In order to estimate the topotecan penetration time in the whole retinoblastoma cell culture, a procedure based on an active contour detection algorithm, a neural network classifier and a statistical model and its validation, is proposed.This new inference model allows to estimate the penetration time. Results show that the penetration mean time strongly depends on tumorsphere size and on chemotherapeutic treatment that the patient has previously received.

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Patel, Vishal, Alan Wang, Andrew Paul Monk, and Marco Tien-Yueh Schneider. "Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction." Bioengineering 11, no. 2 (February 15, 2024): 186. http://dx.doi.org/10.3390/bioengineering11020186.

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This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.

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Jian, Zini, Tianxiang Song, Zhihui Zhang, Zhao Ai, Heng Zhao, Man Tang, and Kan Liu. "An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation." Sensors 24, no. 3 (January 31, 2024): 928. http://dx.doi.org/10.3390/s24030928.

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Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.

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Neelakanta, Perambur S., Edward M. Bertot, and Deepti Pappusetty. "Bioinformatics-Inspired Algorithms for 2D-Image Analysis—Application to Medical Images Part II." International Journal of Biomedical and Clinical Engineering 1, no. 1 (January 2012): 49–58. http://dx.doi.org/10.4018/ijbce.2012010104.

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This paper describes a new method of comparing images of circular/near-circular symmetry so as to elucidate the similarity details between them. If one such image is a test-entity and the other is a reference template, the comparison in question will lead to find the unique features (and their locations) in the test-image vis-à-vis the template. The method of comparison and similarity assessment indicated thereof is to use the so-called Needleman-Wunsch (NW) and Smith-Waterman (SW) algorithms commonly adopted in bioinformatic contexts of comparing two linear sequences (like DNA chains). Relevant procedure is extended in this study to address 2D-patterns. It involves first transforming the test-image (of circular symmetry) from polar-plane to a rectangular format. Next, the transformed test-image is digitised and compared against a template (also in digital rectangular format) on row-to-row and column-to-column basis. The resulting alignment of pixel bits in the test-image versus the template leads to an optimal score-of-similarity on the comparisons made. Biomedical applications of the proposed strategy are explored with reference to typical and circular/quasi-circular MRI images, and the associated image recognition, interpretation, and locating of the artefacts are discussed.

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Suzuki, Kenji, Tatsuya Hayashi, Shigeyuki Ikeda, Isao Horiba, Noboru Sugie, and Michio Nanki. "Improving Image Quality of Medical Low-Dose X-ray Image Sequences Using a Neural Filter." IEEJ Transactions on Electronics, Information and Systems 119, no. 11 (1999): 1383–91. http://dx.doi.org/10.1541/ieejeiss1987.119.11_1383.

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Frouin, F., A. De Cesare, Y. Bouchareb, A. Todd-Pokropek, and A. Herment. "Spatial regularization applied to factor analysis of medical image sequences (FAMIS)." Physics in Medicine and Biology 44, no. 9 (August 17, 1999): 2289–306. http://dx.doi.org/10.1088/0031-9155/44/9/315.

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Kim, Gi-Youn, Byoung-Doo Oh, Chulho Kim, and Yu-Seop Kim. "Convolutional Neural Network and Language Model-Based Sequential CT Image Captioning for Intracerebral Hemorrhage." Applied Sciences 13, no. 17 (August 26, 2023): 9665. http://dx.doi.org/10.3390/app13179665.

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Intracerebral hemorrhage is a severe problem where more than one-third of patients die within a month. In diagnosing intracranial hemorrhage, neuroimaging examinations are essential. As a result, the interpretation of neuroimaging becomes a crucial process in medical procedures. However, human-based image interpretation has inherent limitations, as it can only handle a restricted range of tasks. To address this, a study on medical image captioning has been conducted, but it primarily focused on single medical images. However, actual medical images often consist of continuous sequences, such as CT scans, making it challenging to directly apply existing studies. Therefore, this paper proposes a CT image captioning model that utilizes a 3D-CNN model and distilGPT-2. In this study, four combinations of 3D-CNN models and language models were compared and analyzed for their performance. Additionally, the impact of applying penalties to the loss function and adjusting penalty values during the training process was examined. The proposed CT image captioning model demonstrated a maximum BLEU score of 0.35 on the in-house dataset, and it was observed that the text generated by the model became more similar to human interpretations in medical image reports with the application of loss function penalties.

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YAZDI, MEHRAN, and ANDRE ZACCARIN. "INTER-FRAME PREDICTION OF MEDICAL AND VIDEOPHONE SEQUENCES: A DEFORMABLE TRIANGLE-BASED APPROACH." International Journal of Image and Graphics 04, no. 03 (July 2004): 453–67. http://dx.doi.org/10.1142/s0219467804001506.

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Motion compensation using deformable triangle patches has been successfully used for low bit rate coding of videophone sequences. They were also shown to be particularly efficient for interframe coding of MRI sequences, for which the difference between image slices can be well modeled by locally affine deformations. Regular triangular meshes were used in previous works. In this paper, we present a quadtree decomposition algorithm to generate a triangle mesh for which smaller triangles are used in image areas where the motion or deformation is more complex. Grid points are recursively added to areas where the reduction in prediction error is more significant. Results show that using variable size triangular patches increases the PSNR of the motion-compensated image while reducing the number of grid points when compared to a regular triangular mesh.

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Alenezi, Fayadh, and K. C. Santosh. "Geometric Regularized Hopfield Neural Network for Medical Image Enhancement." International Journal of Biomedical Imaging 2021 (January 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/6664569.

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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.

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Trots, Ihor. "Mutually Orthogonal Golay Complementary Sequences in Synthetic Aperture Imaging Systems." Archives of Acoustics 40, no. 2 (June 1, 2015): 283–89. http://dx.doi.org/10.1515/aoa-2015-0031.

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Abstract The main objective of this study is to improve the ultrasound image by employing a new algorithm based on transducer array element beam pattern correction implemented in the synthetic transmit aperture (STA) method combined with emission of mutually orthogonal complementary Golay sequences. Orthogonal Golay sequences can be transmitted and received by different transducer elements simultaneously, thereby decreasing the time of image reconstruction, which plays an important role in medical diagnostic imaging. The paper presents the preliminary results of computer simulation of the synthetic aperture method combined with the orthogonal Golay sequences in a linear transducer array. The transmission of long waveforms characterized by a particular autocorrelation function allows to increase the total energy of the transmitted signal without increasing the peak pressure. It can also improve the signal-to-noise ratio and increase the visualization depth maintaining the ultrasound image resolution. In the work, the 128-element linear transducer array with a 0.3 mm pitch excited by 8-bits Golay coded sequences as well as one cycle at nominal frequencies of 4 MHz were used. The comparison of 2D ultrasound images of the phantoms is presented to demonstrate the benefits of a coded transmission. The image reconstruction was performed using the synthetic STA algorithm with transmit and receive signals correction based on a single element directivity function.

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Zhao, Jie, Fa Ling Yi, and Zhan Peng Huang. "A New Contour Initialization of CT Image Sequences in GVF Model." Applied Mechanics and Materials 321-324 (June 2013): 1225–29. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1225.

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In order to overcome the shortcomings that GVF model is susceptible to structures with slender topology, an improved watershed algorithm is proposed to determine initial contour of GVF Snake model. The organs in medical CT images often were irregular and had deep boundary concavities, and CT serial images were up to hundreds pieces. Firstly, in a CT image the improved watershed algorithm grows an organ from a seed block by the principle of the similarity between gray scale and texture, then using its edge as the start contour of the adjacent CT sequence, with GVF algorithm segment the organ from a sequence of images, the process is repeated until get all slices of the entire abdomen. Experiment results which are the basic of 3-D reconstruction and cancer detection show that the new contour initialization algorithm can obtain segmentation result efficiently, accurately and cost less time.

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Sharma, Urvashi, Meenakshi Sood, and Emjee Puthooran. "Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding." International journal of electrical and computer engineering systems 9, no. 2 (June 6, 2019): 69–79. http://dx.doi.org/10.32985/ijeces.9.2.4.

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The proposed block-based lossless coding technique presented in this paper targets at compression of volumetric medical images of 8-bit and 16-bit depth. The novelty of the proposed technique lies in its ability of threshold selection for prediction and optimal block size for encoding. A resolution independent gradient edge detector is used along with the block adaptive arithmetic encoding algorithm with extensive experimental tests to find a universal threshold value and optimal block size independent of image resolution and modality. Performance of the proposed technique is demonstrated and compared with benchmark lossless compression algorithms. BPP values obtained from the proposed algorithm show that it is capable of effective reduction of inter-pixel and coding redundancy. In terms of coding efficiency, the proposed technique for volumetric medical images outperforms CALIC and JPEG-LS by 0.70 % and 4.62 %, respectively.

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Lin, Liang, Wei Yang, Chenglong Li, Jin Tang, and Xiaochun Cao. "Inference With Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences." IEEE Transactions on Cybernetics 46, no. 12 (December 2016): 2796–809. http://dx.doi.org/10.1109/tcyb.2015.2489719.

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Chen, Yen Sheng, Shao Hsien Chen, and Jeih Jang Liou. "Comparison of Multispectral Image Processing Techniques to Brain MR Image Classification." Applied Mechanics and Materials 182-183 (June 2012): 1998–2002. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1998.

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Brain Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water, proteinaceous fluid, soft tissue and other tissues with a variety of contrast. The abundance fractions of tissue signatures provided by multispectral images can be very useful for medical diagnosis compared to other modalities. Multiple Sclerosis (MS) is thought to be a disease in which the patient immune system damages the isolating layer of myelin around the nerve fibers. This nerve damage is visible in Magnetic Resonance (MR) scans of the brain. Manual segmentation is extremely time-consuming and tedious. Therefore, fully automated MS detection methods are being developed which can classify large amounts of MR data, and do not suffer from inter observer variability. In this paper we use standard fuzzy c-means algorithm (FCM) for multi-spectral images to segment patient MRI data. Geodesic Active Contours of Caselles level set is another method we implement to do the brain image segmentation jobs. And then we implement anther modified Fuzzy C-Means algorithm, where we call Bias-Corrected FCM as BCFCM, for bias field estimation for the same thing. Experimental results show the success of all these intelligent techniques for brain medical image segmentation.

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Kotina, Elena, Ekaterina Leonova, and Viktor Ploskikh. "Displacement Field Construction Based on a Discrete Model in Image Processing Problems." Bulletin of Irkutsk State University. Series Mathematics 39 (2022): 3–16. http://dx.doi.org/10.26516/1997-7670.2022.39.3.

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The problem of a displacement field calculation for an image sequence based on a discrete model is being solved. Algorithms for velocity field (displacement field) construction are in demand in various image processing tasks. These methods are used in motion detection, object movement tracking, analysis of complex images, movement correction of medical diagnostic images in nuclear medicine, radiology, etc. An optimization approach to the displacement field construction based on a discrete model is developed in the paper. The approach explores the possibility of taking into account the brightness change along the trajectories of the system. A linear model is considered. Directed optimization methods based on the analytical representation of the functional gradient are constructed to search for unknown parameters. The algorithm for displacement field construction with image partitioning into regions (neighborhoods) is proposed. This algorithm can be used to process a variety of image sequences. The results of the algorithm operation on test radionuclide images are presented.

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FerniUkrit, M., and G. R. Suresh. "Hybrid Algorithm for Medical Image Sequences using Super-Spatial Structure Prediction with LZ8." International Journal of Computer Applications 86, no. 11 (January 16, 2014): 10–15. http://dx.doi.org/10.5120/15028-3344.

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PARK, SANGHYUN, and WESLEY W. CHU. "SIMILARITY-BASED SUBSEQUENCE SEARCH IN IMAGE SEQUENCE DATABASES." International Journal of Image and Graphics 03, no. 01 (January 2003): 31–53. http://dx.doi.org/10.1142/s0219467803000907.

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This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure as compared to the Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequence without false dismissals. It applies the normalization for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verified that the proposed method significantly outperforms the naïve method and scales well in a large volume of image sequence databases.

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PANIN, GIORGIO. "FAST, MULTI-MODAL AND DISCONTINUITY-PRESERVING IMAGE REGISTRATION USING MUTUAL INFORMATION." International Journal on Artificial Intelligence Tools 22, no. 06 (December 2013): 1360015. http://dx.doi.org/10.1142/s0218213013600154.

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In this paper, we describe a fast and efficient method for multi-modal and discontinuity-preserving image registration, implemented on graphics hardware. Multi-sensory data fusion and medical image analysis often pose the challenging task of aligning dense, non-rigid and multi-modal images. However, also optical sequences or stereo image pairs may present variable illumination conditions and noise. The above problems can be addressed by an invariant similarity measure, such as mutual information. Additionally, when using a regularized approach to deal with the ill-posedness of the problem, one has to take care of preserving discontinuities at the motion boundaries. Our approach efficiently addresses the above issues through a primal-dual convex estimation framework, using an approximated Hessian matrix that decouples pixel dependencies, while being asymptotically correct. At the same time, we achieve a high computational efficiency by means of pre-quantized kernel density estimation and differentiation, as well as a parallel implementation on the GPU. Our approach is demonstrated on ground-truth data from the Middlebury database, as well as medical and visible-infrared image pairs.

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Zhang, Ai Hua, Xing Zhong Zhou, Li Ming Yang, Rong Shen, and Zhe Wei. "Continuous Blood Pressure Measurement Method Based on the Pulse Image Sensor and BP Neural Network." Applied Mechanics and Materials 596 (July 2014): 476–79. http://dx.doi.org/10.4028/www.scientific.net/amm.596.476.

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A continuous pressure measurement method is proposed based the pulse image sensor and BP neural network for continuous measurement of arterial blood pressure. The multi-information synchronous acquisition system is built to collect pulse image sequences, sphygmopalpation pressure, probe internal pressure, and blood pressure of subjects. The feature vector is formed from pulse image sequences, sphygmopalpation pressure, and probe internal pressure to predict continuous blood pressure by BP neural network. The results show that the mean difference (MD) and standard deviation (SD) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) meet the standard of Association for the Advancement of Medical Instrumentation (AAMI). The method could be used to predict continuous blood pressure and provides a new method for arterial continuous blood pressure measurement.

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Liu, Xiaoli, Ruoqi Yin, and Jianqin Yin. "Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation." Applied Sciences 12, no. 8 (April 8, 2022): 3764. http://dx.doi.org/10.3390/app12083764.

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We propose a fully convolutional neural network based on the attention mechanism for 3D medical image segmentation tasks. It can adaptively learn to highlight the salient features of images that are useful for image segmentation tasks. Some prior methods enhance accuracy using multi-scale feature fusion or dilated convolution, which is basically artificial and lacks the flexibility of the model itself. Therefore, some works proposed the 2D attention gate module, but these works process 2D medical slice images, ignoring the correlation between 3D image sequences. In contrast, the 3D attention gate can comprehensively use the information of three dimensions of medical images. In this paper, we propose the Attention V-Net architecture, which uses the 3D attention gate module, and applied it to the left atrium segmentation framework based on semi-supervised learning. The proposed method is evaluated on the dataset of the 2018 left atrial challenge. The experimental results show that the Attention V-Net obtains improved performance under evaluation indicators, such as Dice, Jaccard, ASD (Average surface distance), and 95HD (Hausdorff distance). The result indicates that the model in this paper can effectively improve the accuracy of left atrial segmentation, therefore laying the foundation for subsequent work such as in atrial reconstruction. Meanwhile, our model is of great significance for assisting doctors in treating cardiovascular diseases.

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Sun, Shao Yan, and Lei Chen. "Use of Information Discrepancy Measure to Register Medical Images." Applied Mechanics and Materials 50-51 (February 2011): 790–93. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.790.

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Function of Degree of Disagreement (FDOD), a new measure of information discrepancy, quantifies the discrepancy of multiple sequences. This function has some peculiar mathematical properties, such as symmetry, boundedness and monotonicity. In this contribution, we first introduce the FDOD function to solve the three-dimensional (3-D) medical image registration problem. Numerical experiments illustrate that the new registration method based on the FDOD function can obtain subvoxel registration accuracy, and it is a competitive method with the mutual information based method.

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Thirion, J. P., and G. Calmon. "Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences." IEEE Transactions on Medical Imaging 18, no. 5 (May 1999): 429–41. http://dx.doi.org/10.1109/42.774170.

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Shaou-Gang Miaou, Fu-Sheng Ke, and Shu-Ching Chen. "A Lossless Compression Method for Medical Image Sequences Using JPEG-LS and Interframe Coding." IEEE Transactions on Information Technology in Biomedicine 13, no. 5 (September 2009): 818–21. http://dx.doi.org/10.1109/titb.2009.2022971.

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Ben Ahmed, Kaoutar, Lawrence O. Hall, Dmitry B. Goldgof, and Robert Gatenby. "Ensembles of Convolutional Neural Networks for Survival Time Estimation of High-Grade Glioma Patients from Multimodal MRI." Diagnostics 12, no. 2 (January 29, 2022): 345. http://dx.doi.org/10.3390/diagnostics12020345.

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Glioma is the most common type of primary malignant brain tumor. Accurate survival time prediction for glioma patients may positively impact treatment planning. In this paper, we develop an automatic survival time prediction tool for glioblastoma patients along with an effective solution to the limited availability of annotated medical imaging datasets. Ensembles of snapshots of three dimensional (3D) deep convolutional neural networks (CNN) are applied to Magnetic Resonance Image (MRI) data to predict survival time of high-grade glioma patients. Additionally, multi-sequence MRI images were used to enhance survival prediction performance. A novel way to leverage the potential of ensembles to overcome the limitation of labeled medical image availability is shown. This new classification method separates glioblastoma patients into long- and short-term survivors. The BraTS (Brain Tumor Image Segmentation) 2019 training dataset was used in this work. Each patient case consisted of three MRI sequences (T1CE, T2, and FLAIR). Our training set contained 163 cases while the test set included 46 cases. The best known prediction accuracy of 74% for this type of problem was achieved on the unseen test set.

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Malczewski, Krzysztof. "Semi-PROPELLER Compressed Sensing Image Reconstruction with Enhanced Resolution in MRI." International Journal of Electronics and Telecommunications 61, no. 2 (June 1, 2015): 211–17. http://dx.doi.org/10.1515/eletel-2015-0028.

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Abstract Magnetic Resonance Imaging (MRI) reconstruction algorithm using semi-PROPELLER compressed sensing is presented in this paper. It is exhibited that introduced algorithm for estimating data shifts is feasible when super-resolution is applied. The offered approach utilizes compressively sensed MRI PROPELLER sequences and improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. It is shown that the presented approach improves MR spatial resolution in cases when Compressed Sensing (CS) sequences are used. The application of CS in medical modalities has the potential for significant scan time reductions, with visible benefits for patients and health care economics. These methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. This diagnostic modality struggles with an inherently slow data acquisition process. The use of CS to MRI leads to substantial scan time reductions [7] and visible benefits for patients and economic factors. In this report the objective is to combine Super-Resolution image enhancement algorithm with both PROPELLER sequence and CS framework. The motion estimation algorithm being a part of super resolution reconstruction (SRR) estimates shifts for all blades jointly, utilizing blade-pair correlations that are both strong and more robust to noise.

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Li, Christy Y., Xiaodan Liang, Zhiting Hu, and Eric P. Xing. "Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6666–73. http://dx.doi.org/10.1609/aaai.v33i01.33016666.

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Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descriptions. We propose a novel Knowledge-driven Encode, Retrieve, Paraphrase (KERP) approach which reconciles traditional knowledge- and retrieval-based methods with modern learning-based methods for accurate and robust medical report generation. Specifically, KERP decomposes medical report generation into explicit medical abnormality graph learning and subsequent natural language modeling. KERP first employs an Encode module that transforms visual features into a structured abnormality graph by incorporating prior medical knowledge; then a Retrieve module that retrieves text templates based on the detected abnormalities; and lastly, a Paraphrase module that rewrites the templates according to specific cases. The core of KERP is a proposed generic implementation unit—Graph Transformer (GTR) that dynamically transforms high-level semantics between graph-structured data of multiple domains such as knowledge graphs, images and sequences. Experiments show that the proposed approach generates structured and robust reports supported with accurate abnormality description and explainable attentive regions, achieving the state-of-the-art results on two medical report benchmarks, with the best medical abnormality and disease classification accuracy and improved human evaluation performance.

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Yan, Cui, Junjiao Hu, Yanyu Li, Xingzhi Xie, Zhimin Zou, Qiyu Deng, Xiaoyue Zhou, Xiaoming Bi, Mu Zeng, and Jun Liu. "Motion-corrected free-breathing late gadolinium enhancement combined with a gadolinium contrast agent with a high relaxation rate: an optimized cardiovascular magnetic resonance examination protocol." Journal of International Medical Research 48, no. 10 (October 2020): 030006052096466. http://dx.doi.org/10.1177/0300060520964664.

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Objective This prospective study investigated the feasibility of an optimized cardiovascular magnetic resonance (CMR) examination protocol using the motion-corrected (MOCO), balanced steady-state free precession (bSSFP), phase-sensitive inversion recovery (PSIR) sequence combined with a gadolinium contrast agent with a high relaxation rate in patients who cannot hold their breath. Methods Fifty-one patients with heart disease underwent CMR examinations twice and these were performed with different late gadolinium enhancement (LGE) imaging sequences (fast low-angle shot [FLASH] sequence vs. MOCO sequence) and different gadolinium contrast agents (gadopentetate dimeglumine vs. gadobenate dimeglumine) with a 48-hour interval. LGE image quality, total time spent in the whole study, and time taken to perform LGE imaging were compared for the two CMR examinations. Results LGE images with the MOCO bSSFP PSIR sequence showed significantly higher image quality compared with those with the segmented FLASH PSIR sequence. There was a significant difference between the total scan time for the two examinations and different LGE sequences. Conclusions The MOCO bSSFP PSIR sequence effectively improves the quality of LGE images. Changing the CMR scanning protocol by combining the MOCO bSSFP PSIR sequence with a gadolinium contrast agent with a high relaxation rate effectively shortens the scan time. Clinical trial registration number: ChiCTR-ROC-17013978.

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Luo, Dandan, Daming Qin, Hong Cheng, Meng Zhou, Daoming Zhu, and Cheng Ni. "Comparison of Image Quality of Multiple Magnetic Resonance Imaging Sequences in Multiple Myeloma." Journal of Medical Imaging and Health Informatics 11, no. 2 (February 1, 2021): 497–505. http://dx.doi.org/10.1166/jmihi.2021.3303.

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Multiple myeloma is a refractory malignant disease characterized by clonal hyper proliferation of plasma cells in the bone marrow microenvironment. In recent years, its incidence has gradually increased and it is younger. Magnetic resonance imaging is a medical imaging technology that has developed rapidly in recent years. Its application and promotion have greatly improved the level of medical services and scientific research. It has become one of the most important examination methods for myeloma. Magnetic resonance imaging has a high soft tissue resolution and has a high detection rate for multiple myeloma. However, there are few studies on the MM magnetic resonance scanning protocol, and the initial or follow-up examination methods have not been unified. Therefore, this paper subjectively and objectively evaluates the clinical images of MM patients with multiple sequences of magnetic resonance of different devices, and hopes to provide more advantageous examination methods for clinicians and patients. The experimental results show that magnetic resonance multisequence imaging can be ideal for clinical diagnosis and follow-up of MM patients.

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Shyamala, A., S. Selvaperumal, and G. Prabhakar. "Anfis Classifier Based Moving Object Detection and Segmentation in Indoor and Outdoor Environments." Current Signal Transduction Therapy 14, no. 1 (March 11, 2019): 21–30. http://dx.doi.org/10.2174/1574362413666180226113024.

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Background: Moving object detection in dynamic environment video is more complex than the static environment videos. In this paper, moving objects in video sequences are detected and segmented using feature extraction based Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier approach. The proposed moving object detection methodology is tested on different video sequences in both indoor and outdoor environments. Methods: This proposed methodology consists of background subtraction and classification modules. The absolute difference image is constructed in background subtraction module. The features are extracted from this difference image and these extracted features are trained and classified using ANFIS classification module. Results: The proposed moving object detection methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure. The proposed moving object segmentation methodology is executed on different Central Processing Unit (CPU) processor as 1.8 GHz and 2.4 GHz for evaluating the performance during moving object segmentation. At present, some moving object detection systems used 1.8 GHz CPU processor. Recently, many systems for moving object detection are using 2.4 GHz CPU processor. Hence, CPU processors 1.8 GHz and 2.4 GHz are used in this paper for detecting the moving objects in video sequences. Table 1 shows the performance evaluation of proposed moving object detection on CPU processor 1.8 GHz (100 sequence). Table 2 shows the performance evaluation of proposed moving object detection on CPU processor 2.8 GHz (100 sequence). The average moving object detection time on CPU processor 1.8 GHz for fountain sequence is 62.5 seconds, for airport sequence is 64.7 seconds, for meeting room sequence is 71.6 seconds and for Lobby sequence is 73.5 seconds, respectively, as depicted in Table 3. The average elapsed time for moving object detection on 100 sequences is 68.07 seconds. The average moving object detection time on CPU processor 2.4 GHz for fountain sequence is 56.5 seconds, for airport sequence is 54.7 seconds, for meeting room sequence is 65.8 seconds and for Lobby sequence is 67.5 seconds, respectively, as depicted in Table 4. The average elapsed time for moving object detection on 100 sequences is 61.12 seconds. It is very clear from Table 3 and Table 4; the moving object detection time is reduced when the frequency of the CPU processor increases. Conclusion: In this paper, moving object is detected and segmented using ANFIS classifier. The proposed method initially segments the background image and then features are extracted from the threshold image. These features are trained and classified using ANFIS classification method. The proposed moving object detection method is tested on different video sequences which are obtained from different indoor and outdoor environments. The performance of the proposed moving object detection and segmentation methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure.

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Shirsat, T. G., and V. K. Bairagi. "Lossless Medical Image Compression by Integer Wavelet and Predictive Coding." ISRN Biomedical Engineering 2013 (June 4, 2013): 1–6. http://dx.doi.org/10.1155/2013/832527.

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The future of healthcare delivery systems and telemedical applications will undergo a radical change due to the developments in wearable technologies, medical sensors, mobile computing, and communication techniques. When dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis we required to considered many parameters for telemedical application. E-health was born with the integration of networks and telecommunications. In recent years, healthcare systems rely on images acquired in two-dimensional domains in the case of still images or three-dimensional domains for volumetric video sequences and images. Images are acquired by many modalities including X-ray, magnetic resonance imaging, ultrasound, positron emission tomography, and computed axial tomography (Sapkal and Bairagi, 2011). Medical information is either in multidimensional or multiresolution form, which creates enormous amount of data. Retrieval, efficient storage, management, and transmission of these voluminous data are highly complex. One of the solutions to reduce this complex problem is to compress the medical data without any loss (i.e., lossless). Since the diagnostics capabilities are not compromised, this technique combines integer transforms and predictive coding to enhance the performance of lossless compression. The proposed techniques can be evaluated for performance using compression quality measures.

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Journal articles on the topic 'Medical image sequences'

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