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Journal articles on the topic 'Medical Images Processing'

Author: Grafiati

Published: 28 June 2021

Last updated: 4 February 2022

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1

Bento, Tiago, Duarte Val´erio, Pedro Teodoro, and Jorge Martins. "Fractional Order Image Processing of Medical Images." Journal of Applied Nonlinear Dynamics 6, no. 2 (June 2017): 181–91. http://dx.doi.org/10.5890/jand.2017.06.005.

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Basha, S. Saheb, and K. Satya Prasad. "Segmentation of Medical Images Using Morphological Image Processing." i-manager's Journal on Future Engineering and Technology 4, no. 3 (April 15, 2009): 37–45. http://dx.doi.org/10.26634/jfet.4.3.278.

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3

Jeong, Eun Kee. "Simple post-processing of medical images." Yonsei Medical Journal 36, no. 1 (1995): 77. http://dx.doi.org/10.3349/ymj.1995.36.1.77.

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Depeursinge, A., and H. Müller. "Sensors, Medical Images and Signal Processing:." Yearbook of Medical Informatics 18, no. 01 (August 2009): 81–83. http://dx.doi.org/10.1055/s-0038-1638643.

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Summary Objectives To summarize current excellent research in the field of medical sensor, signal and imaging informatics. Method Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2009. Results Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. Conclusions The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field. Imaging and particularly multi-dimensional imaging has had in 2008 by far the largest number of publications compared to signals and sensors.

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Depeursinge, A., and H. Müller. "Sensors, Medical Images and Signal Processing:." Yearbook of Medical Informatics 19, no. 01 (August 2010): 43–46. http://dx.doi.org/10.1055/s-0038-1638687.

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Summary Objectives To summarize current excellent research in the field of medical sensor, signal and imaging informatics. Method: Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2010. Results: Current research in the field of sensor, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. Conclusions: The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field. Research published in 2009 was characterized by the emergence of mature computerized diagnosis aid frameworks with assessment of input and output quality. The purpose of these systems is clearly to bring new image and signal interpretation tools to clinicians.

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Depeursinge, A., and H. Müller. "Sensors, Medical Images and Signal Processing:." Yearbook of Medical Informatics 20, no. 01 (August 2011): 92–95. http://dx.doi.org/10.1055/s-0038-1638744.

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SummaryTo summarize excellent research in the field of medical sensor, signal and imaging informatics published in the year 2010.Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2011.Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. When compared to research on sensors and signals, imaging research represent the majority of published papers in 2010. Research published in 2010 was characterized by an increased participation of the clinicians in the study design, implementation and validation of computerized diagnosis aid tools.The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field.

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Rao, K. Prahlad. "Calvarial Bone Segmentation from Medical Images by Image Processing Technique." IJARCCE 4, no. 12 (December 30, 2015): 491–94. http://dx.doi.org/10.17148/ijarcce.2015.412115.

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Prabu Shankar, K. C., and S. Prayla Shyry. "A Survey of image pre-processing techniques for medical images." Journal of Physics: Conference Series 1911, no. 1 (May 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/1911/1/012003.

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Shivajirao Shinde, Bhausaheb. "The Origins of Digital Image Processing & Application areas in Digital Image Processing Medical Images." IOSR Journal of Engineering 1, no. 1 (November 2011): 66–71. http://dx.doi.org/10.9790/3021-0116671.

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Higgins, William E., and Roderick D. Swift. "Distributed system for processing 3D medical images." Computers in Biology and Medicine 27, no. 2 (March 1997): 97–115. http://dx.doi.org/10.1016/s0010-4825(96)00042-x.

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Abboud, Ali J. "Shape Adaptable Medical Multimedia Processing." Open Electrical & Electronic Engineering Journal 13, no. 1 (January 31, 2019): 1–18. http://dx.doi.org/10.2174/1874129001913010001.

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Introduction:Electronic medical healthcare systems are becoming the backbone of health organizations over the world. The huge amounts of medical multimedia produced by these systems especially images and videos are transmitted by the computer networks that connect these systems. The variability in the shape and texture of transferred medical multimedia data needs adaptable procedures to process these data efficiently. In other words, these procedures must adjust automatically based on the shape of region of interests in the medical multimedia images to cope with fast changes in the healthcare environments. In this paper, we have proposed shape adaptable watermarking approaches for medical multimedia processing systems. The medical images generated by X-rays, MRI and CT modalities are used in our experiments to test proposed approaches. In addition, these approaches were tested under different kinds of signal processing and geometric attacks. The comparative comparison of our proposed approaches with state-of-art approaches proved the superiority and capability of our approaches to adjust the number of selected subands of medical cover image to embed and extract the hospital watermark logos.Background and Objective:The development of an adjustable approach to process medical multimedia signals for healthcare system. The aim of this research is to select adaptably the number of subands of cover image to hide the information of hospital logo watermark inside them such that embedded watermarks can resist different kinds of attacks.Method:The proposed adjustable approach consists of suband selection method, criterion, embedding and extraction procedures, DWT transform, attacks, evaluation metrics,etc.Results & Conclusion:It provides robust and adjustable method to embed and extract watermark logo at different resolution levels of cover medical image and uses with images of different sizes and modalities.

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Panchangam, Appaji, K. V. L. N. Sastry, D. V. G. L. N. Rao, B. S. DeCristofano, B. R. Kimball, and M. Nakashima. "Processing of medical images using real-time optical Fourier processing." Medical Physics 28, no. 1 (January 2001): 22–27. http://dx.doi.org/10.1118/1.1328079.

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Mustafa, Wan Azani, Nurshahira Mohd Salleh, Syed Zulkarnain Syed Idrus, Mohd Aminudin Jamlos, and Mohamad Nur Khairul Hafizi Rohani. "Overview of Segmentation X-Ray Medical Images Using Image Processing Technique." Journal of Physics: Conference Series 1529 (April 2020): 042017. http://dx.doi.org/10.1088/1742-6596/1529/4/042017.

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Goyal, Bhawna, Ayush Dogra, Sunil Agrawal, and B. S. Sohi. "A Survey on the Image Denoising to Enhance Medical Images." Biosciences, Biotechnology Research Asia 15, no. 3 (September 3, 2018): 501–7. http://dx.doi.org/10.13005/bbra/2655.

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While acquisition and transmission of images, all recording devices have physical limitations and traits which make them prone to noise. Noise manifests itself in the form of signal perturbation leading to deterred image observation, image analysis and image assessment. Image denoising is fundamental to the world of image processing. Thus any progress made in image denoising forms a stepping stone in our understanding of image processing and statistics. The basic fundamental for denoising of an image includes suppression of the noisy pixels while preserving as many information pixel as possible.. The manuscript provides the reader’s a typical foundation for image denoising.

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SATO, TAKASHI, MAKOTO MATSUOKA, and HIDEKI TAKAYASU. "FRACTAL IMAGE ANALYSIS OF NATURAL SCENES AND MEDICAL IMAGES." Fractals 04, no. 04 (December 1996): 463–68. http://dx.doi.org/10.1142/s0218348x96000571.

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We construct color map images of fractal dimension distribution from natural scenes and medical images by applying the box-counting method locally. The map images clearly show the difference between clouds and rocks, as well as between cancer parts and normal tissue in the colon. The method is simple and may be expected to be applicable to a real-time video-data processing.

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Viswanathan, Tamilarasu. "Medical Images Processing using Effectiveness of Walsh Function." Bioscience Biotechnology Research Communications 13, no. 11 (December 25, 2020): 70–72. http://dx.doi.org/10.21786/bbrc/13.11/16.

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Ramya, H. R., and B. K. Sujatha. "Real Time Image Fusion Based Technique for Medical Images." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4500–4508. http://dx.doi.org/10.1166/jctn.2020.9105.

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To tackle the cost of storage and storage space with fast-growing technologies, the image fusion is playing an important role in several image-processing areas such as medical-imaging and satelliteimaging. This fused picture is appropriate for machine perception, human visual analysis or further analysis assignment. Recently the computing method such as fuzzy logic model has been extensively used in the field of image-processing due to the uniqueness of handling uncertain modeling. The fuzzy logic based image-fusion model generally performed better with respect to other existing image fusion models. In this paper, we considered type-2 fuzzy logic, which has similar function to earlier fuzzy logic technique but consist more functionality that allows optimized management of higher degrees under uncertainty. Interval type-2 fuzzy-logic-system (IT2FLS) are widely used fuzzy sets due to their ease of use and computational simplicity. A real time image fusion (RTIF) technique that is based on the IT2FLS is used to overcome the excess computation time and nonlinear uncertainties, which is present in the medical images. In the result simulation section, we have shown that our proposed model has taken less computation time and provided better quality assessment matrices with respect to existing system.

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Khor, Hui Liang, Siau-Chuin Liew, and Jasni Mohd Zain. "Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment." International Journal of Biomedical Imaging 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9583727.

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With the advancement of technology in communication network, it facilitated digital medical images transmitted to healthcare professionals via internal network or public network (e.g., Internet), but it also exposes the transmitted digital medical images to the security threats, such as images tampering or inserting false data in the images, which may cause an inaccurate diagnosis and treatment. Medical image distortion is not to be tolerated for diagnosis purposes; thus a digital watermarking on medical image is introduced. So far most of the watermarking research has been done on single frame medical image which is impractical in the real environment. In this paper, a digital watermarking on multiframes medical images is proposed. In order to speed up multiframes watermarking processing time, a parallel watermarking processing on medical images processing by utilizing multicores technology is introduced. An experiment result has shown that elapsed time on parallel watermarking processing is much shorter than sequential watermarking processing.

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Benkö, I., G. J. Köteles, and G. Németh. "Image processing of medical infrared images for monitoring local conditions during radiotherapy." Imaging Science Journal 48, no. 1 (January 2000): 9–13. http://dx.doi.org/10.1080/13682199.2000.11784340.

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Rodrigues, Pedro, Manuel João Ferreira, and João Luís Monteiro. "Quantum Computation Perspectives in Medical Image Processing." International Journal of Nanotechnology and Molecular Computation 2, no. 2 (April 2010): 16–46. http://dx.doi.org/10.4018/978-1-61520-670-4.ch006.

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The need to increase the complexity of computational methods to produce improvements in functional performance, particularly in medical image processing applications, leads to find suitable physical devices. This chapter describes two ways of adapting the techniques of image processing to quantum devices. This kind of computing can achieve, for some problems, unparalleled performance as compared to classic computing. In the first method, using the quantum Grover’s algorithm how to implement image processing techniques under quantum rules is shown. In the second method, using diffraction and interference, the possibility of using less complex quantum devices for processing digital images is treated. Using leucocytes images, that mode is tested.

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Engelmann, U., and H.-P. Meinzer. "Medical Images in Integrated Health Care Workstations." Yearbook of Medical Informatics 05, no. 01 (August 1996): 87–94. http://dx.doi.org/10.1055/s-0038-1638049.

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AbstractThe difference between an invention and a discovery is discussed, before turning to the sources of medical images. Next, the ongoing integration of image modalities in clinical routine is reviewed, as well as improvements in diagnosis and therapy planning with the help of better images in inter-connected distributed systems. Current shortcomings of image processing, and the attempts to overcome these shortcomings are presented. Examples of image processing are given, together with a vision on future systems and procedures.

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Akil, Mohamed, and Mohamed Hédi Bédoui. "Special issue on real-time processing of medical images." Journal of Real-Time Image Processing 13, no. 1 (March 2017): 101–2. http://dx.doi.org/10.1007/s11554-017-0676-5.

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23

Kishore, V. Vijaya, and R. V. S. Satyanarayana. "A Multi-Functional Interactive Image Processing Tool for Lung CT Images." International Journal of Biomedical and Clinical Engineering 2, no. 1 (January 2013): 1–11. http://dx.doi.org/10.4018/ijbce.2013010101.

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An important requirement for clinical diagnosis and treatment is a platform for medical image processing technique that is more flexible and accurate. An interactive image processing method that can be used as a Computer Aided Detection (CAD) tool for medical image processing is proposed in this paper. The tool is developed in MATLAB to read images of different formats like tif, jpg, DICOM etc. The tool is also capable of displaying information about the loaded image of the selected format, read and save images from and to workspace. In addition to this user can use functional tools like determining the value of pixel in the image, obtain histogram, horizontal and vertical profiles of selected lines of the image, color maps of CT window that include CT bone, CT spine, CT mediastinal, auto adjustment of global intensity and selective intensity, image smoothening, manual and auto thresholding. All the mentioned functions are integrated in Graphical User Interface which is user friendly.

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Golubev, Alexandr, Peter Bogatencov, and Grigore Secrieru. "DICOM data processing optimization in medical information systems." Scalable Computing: Practice and Experience 19, no. 2 (May 10, 2018): 189–201. http://dx.doi.org/10.12694/scpe.v19i2.1399.

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The problem of storage and visualization of medical images collected by various medical equipment is actual for latest 10 years for every medical institution. On the other hand, access to the medical investigation datasets and solving the problem of personal patient data security is important for scientific community and institutions that require this data. "DICOM Network" project was developed for solving these problems for different actors in the system based on the various customized roles. This article describes the problems and possible solutions for optimization of medical images storing, providing stable and secure access, based on the distributed warehouse for huge volumes of data with different levels of access. .

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A. Karthika, R., K. Dhinakaran, D. Poorvaja, and A. V. Shanbaga Priya. "Cloud Based Medical Image Data Analytics in Healthcare Management." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 135. http://dx.doi.org/10.14419/ijet.v7i3.27.17744.

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In today’s world, the images form a huge amount of unstructured data from the public and the corporate sector. As a result of the growth of these types of data, modern analytical systems need to interpret and assimilate images. This brings in the need of image processing which involves the transformation from images to analytically organized and structured data. It performs required operation on the given input image and returns the related outputs based on the query. Digital image processing has pushed the envelope for the appraisal in various domains such as healthcare, defense and security, remote sensing, robotic visions, pattern recognitions and satellites. The complication involved in the healthcare domain makes it suitable to explore and induce the concept of image processing and increases the potential for prescriptive analytics. The cloud combined with the Image Processing provides the best environment for analyzing these images using the proposed technique. The proposed work involves processing the input images using the cloud data analytics that provides a user-friendly environment and retrieves the relevant images as well as text for the given user query which produces the outputs that are more efficient in terms of parameters like time, size, security and speed comparatively with the existing data mining process.

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Mattes, J., D. Trystram, and J. Demongeot. "Parallel Image Processing Using Neural Networks: Applications in Contrast Enhancement of Medical Images." Parallel Processing Letters 08, no. 01 (March 1998): 63–76. http://dx.doi.org/10.1142/s0129626498000092.

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This paper describes the implementation of a parallel image processing algorithm, the aim of which is to give good contrast enhancement in real time, especially on the boundaries of an object of interest defined by a grey homogeneity (for example, an object of medical interest having a functional or morphologic homogeneity, like a bone or tumor). The implementation of a neural network algorithm which does this contrast enhancement has been done on a SIMD massively parallel machine (a MasPar of 8192 processors) and the communication between its processors has been optimized.

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Šajn, Luka, and Matjaž Kukar. "Image processing and machine learning for fully automated probabilistic evaluation of medical images." Computer Methods and Programs in Biomedicine 104, no. 3 (December 2011): e75-e86. http://dx.doi.org/10.1016/j.cmpb.2010.06.021.

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ENDO, M. "Image Filing and Processing System for Medical Images(Medicine with Multimedia Technology(2))." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 65, no. 7 (July 1, 1995): 332–38. http://dx.doi.org/10.4286/ikakikaigaku.65.7_332.

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Shruthishree and Harshvardhan Tiwari. "A REVIEW PAPER ON MEDICAL IMAGE PROCESSING." International Journal of Research -GRANTHAALAYAH 5, no. 4RACSIT (April 30, 2017): 21–29. http://dx.doi.org/10.29121/granthaalayah.v5.i4racsit.2017.3344.

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Biomedical image processing has experienced dramatic expansion, and has been an interdisciplinary research field attracting expertise from applied mathematics, computer sciences, engineering, statistics, physics, biology and medicine. Computer-aided diagnostic processing has already become an important part of clinical routine. Accompanied by a rush of new development of high technology and use of various imaging modalities, more challenges arise; for example, how to process and analyze a significant volume of images so that high quality information can be produced for disease diagnoses and treatment. The principal objectives of this course are to provide an introduction to basic concepts and techniques for medical image processing and to promote interests for further study and research in medical imaging processing.The rapid progress of medical science and the invention of various medicines have benefited mankind and the whole civilization. Modern science also has been doing wonders in the surgical field. But, the proper and correct diagnosis of diseases is the primary necessity before the treatment. The more sophisticate the bio-instruments are, better diagnosis will be possible. The medical image plays an important role in clinical diagnosis and therapy of doctor and teaching and researching etc. Medical imaging is often thought of as a way to represent anatomical structures of the body with the help of X-ray computed tomography and magnetic resonance imaging. But often it is more useful for physiologic function rather than anatomy. With the growth of computer and image technology medical imaging has greatly influenced medical field. As the quality of medical imaging affects diagnosis the medical image processing has become a hotspot and the clinical applications wanting to store and retrieve images for future purpose needs some convenient process to store those images in details.

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Fadil, Yousra Ahmed, Baidaa Al-Bander, and Hussein Y. Radhi. "Enhancement of medical images using fuzzy logic." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1478. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1478-1484.

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Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.

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Sindhu, Sindhu, and V. Vaidhehi. "Classification of Human Organ Using Image Processing." Oriental journal of computer science and technology 10, no. 2 (April 11, 2017): 333–37. http://dx.doi.org/10.13005/ojcst/10.02.11.

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The collection of large database of digital image has been used for efficient and advanced way for classifying and intelligent retrieval of medical imaging. This research work is to classify human organs based on MRI images. The various MRI images of organ have been considered as the data set. The main objective of this research work is to automate the medical imaging system. Digital images retrieved based on its shape by Canny Edge Detection and is clustered together in one class using K-Means Algorithm. 2564 data sets related to brain and heart is considered for this research work. The system was trained to classify the image which results in faster execution in medical field, also helped in obtain noiseless and efficient data.

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Jyothi, K., G. Sai Pavitra, K. V. N. M. Sri Pragjna, M. Yaswanth, and A. Narayana Murthy. "Image Enhancement Techniques for Acute Leukemia Images." International Journal for Modern Trends in Science and Technology 6, no. 6 (June 30, 2020): 99–103. http://dx.doi.org/10.46501/ijmtst060621.

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Leukemia is a malignant disease (cancer) that affects people in any age either they are children or adults over 50 years old. Nowadays, there are screening system guidelines for leukemia patients. The screening result from looking at a sample of patient blood, can determine the abnormal levels of white blood cells, which may suggest leukemia for further diagnostic stage. Therefore, medical professional using medical images to diagnose leukemia. However, there are blurness and effects of unwanted noise on blood leukemia images that sometimes result in false diagnosis. Thus image pre-processing such as image enhancement techniques are needed to improve this situation. This study proposes several contrast enhancement techniques which are local contrast stretching, global contrast stretching, partial contrast stretching, bright and dark contrast stretching. All techniques are applied on the leukemia images

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Kaur, L., R. C. Chauhan, and S. C. Saxena. "Adaptive compression of medical ultrasound images." IEE Proceedings - Vision, Image, and Signal Processing 153, no. 2 (2006): 185. http://dx.doi.org/10.1049/ip-vis:20045168.

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Tolba, A. S. "Wavelet Packet Compression of Medical Images." Digital Signal Processing 12, no. 4 (October 2002): 441–70. http://dx.doi.org/10.1006/dspr.2001.0401.

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KATSUI, Tadashi, Shigeru TOMITA, Masahiko SAOTOME, Toshio SAITO, Masaru OHSHIMA, Hitoshi TAKAMORI, Yasuo KUMAZAWA, and Noboru SONOYAMA. "Digital processing of medical images. An outline of the systems." Japanese Journal of Oral & Maxillofacial Surgery 32, no. 3 (1986): 386–92. http://dx.doi.org/10.5794/jjoms.32.386.

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Müller, H., A. Foncubierta-Rodriguez, and A. Depeursinge. "Sensors, Medical Images and Signal Processing: Ubiquitous Personalized Health Monitoring." Yearbook of Medical Informatics 21, no. 01 (August 2012): 100–103. http://dx.doi.org/10.1055/s-0038-1639438.

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SummaryTo summarize excellent research in the field of medical sensor, signal and imaging informatics published in the year 2011.Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2012 through a manual initial selection and a peer review process to find the best paper in this domain published in 2011.Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. An increased number of systems with embedded signal processing where sensors include signal processing were observed in 2011. In all domains, pragmatic solutions with the goal of clinical impact have grown, including in developing countries where simple, robust techniques are combined to address primary and simple medical problems with potentially high impact. Finally, recent advances in image and signal processing are moving towards patient-based modeling.The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field in the year 2012.

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Qamar, Fatima, and Mehreen Sirshar. "A Comparative Study of Testing Parameters of Medical Images Processing." International Journal of Signal Processing, Image Processing and Pattern Recognition 10, no. 1 (January 31, 2017): 243–58. http://dx.doi.org/10.14257/ijsip.2017.10.1.24.

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Shah, Hassan, Noor Badshah, Fahim Ullah, Asmat Ullah, and Matiullah. "A new selective segmentation model for texture images and applications to medical images." Biomedical Signal Processing and Control 48 (February 2019): 234–47. http://dx.doi.org/10.1016/j.bspc.2018.09.017.

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Vartak, Akshay, and Vijay Mankar. "Image Processing Techniques for Contrast Enhancement with Poor Lighting on Social and Medical Images." International Journal of Computer Applications 123, no. 9 (August 18, 2015): 49–55. http://dx.doi.org/10.5120/ijca2015905643.

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Huang, Xilang, Sang Joon Lee, Chang Zoo Kim, and Seon Han Choi. "An automatic screening method for strabismus detection based on image processing." PLOS ONE 16, no. 8 (August 3, 2021): e0255643. http://dx.doi.org/10.1371/journal.pone.0255643.

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Purpose This study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility. Materials and methods The proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facial landmarks to extract the eye region for a frontal facial image. Second, Otsu’s binarization and the HSV color model are applied to the image to eliminate the influence of eyelashes and canthi. Then, the method samples all of the pixel points on the limbus and applies the least square method to obtain the coordinate of the pupil center. Lastly, we calculated the distances from the pupil center to the medial and lateral canthus to measure the deviation of the positional similarity of two eyes for strabismus screening. Result We used a total of 60 frontal facial images (30 strabismus images, 30 normal images) to validate the proposed method. The average value of the iris positional similarity of normal images was smaller than one of the strabismus images via the method (p-value<0.001). The sample mean and sample standard deviation of the positional similarity of the normal and strabismus images were 1.073 ± 0.014 and 0.039, as well as 1.924 ± 0.169 and 0.472, respectively. Conclusion The experimental results of 60 images show that the proposed method is a promising automatic strabismus screening method for people living in remote areas with poor medical accessibility.

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Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (October 19, 2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.

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Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.

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Sun, Yinlong, Bartek Rajwa, and J. Paul Robinson. "Adaptive image-processing technique and effective visualization of confocal microscopy images." Microscopy Research and Technique 64, no. 2 (2004): 156–63. http://dx.doi.org/10.1002/jemt.20064.

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Tang, Min, and Feng Chen. "Three Dimensional Visualization Toolbox for Medical Images Based on IDL." International Journal of Signal Processing, Image Processing and Pattern Recognition 6, no. 5 (October 31, 2013): 143–52. http://dx.doi.org/10.14257/ijsip.2013.6.5.13.

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Tran, Linh Duy, and Linh Quang Huynh. "IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING." Science and Technology Development Journal 13, no. 4 (December 30, 2010): 20–27. http://dx.doi.org/10.32508/stdj.v13i4.2165.

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Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

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Zhang, Junjie, Yong Xia, Hengfei Cui, and Yanning Zhang. "Pulmonary nodule detection in medical images: A survey." Biomedical Signal Processing and Control 43 (May 2018): 138–47. http://dx.doi.org/10.1016/j.bspc.2018.01.011.

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El Ayachi, R., M. Gouskir, and M. Baslam. "Application of Haar Wavelets on Medical Images." Journal of Electronic Commerce in Organizations 13, no. 2 (April 2015): 41–49. http://dx.doi.org/10.4018/jeco.2015040104.

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Recently, the information processing approaches are increased. These methods can be used for several purposes: compressing, restoring, and information encoding. The raw data are less presented and are gradually replaced by others formats in terms of space or speed of access. This paper is interested in compression, precisely, the image compression using the Haar wavelets. The latter allows the application of compression at several levels. The subject is to analyze the compression levels to find the optimal level. This study is conducted on medical images.

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Juliet, Sujitha, Elijah Blessing Rajsingh, and Kirubakaran Ezra. "A novel image compression method for medical images using geometrical regularity of image structure." Signal, Image and Video Processing 9, no. 7 (February 25, 2014): 1691–703. http://dx.doi.org/10.1007/s11760-014-0625-8.

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Li, Fengxia, Zhi Qu, and Ruiling Li. "Medical Cloud Computing Data Processing to Optimize the Effect of Drugs." Journal of Healthcare Engineering 2021 (March 19, 2021): 1–15. http://dx.doi.org/10.1155/2021/5560691.

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In recent years, cloud computing technology is maturing in the process of growing. Hadoop originated from Apache Nutch and is an open-source cloud computing platform. Moreover, the platform is characterized by large scale, virtualization, strong stability, strong versatility, and support for scalability. It is necessary and far-reaching, based on the characteristics of unstructured medical images, to combine content-based medical image retrieval with the Hadoop cloud platform to conduct research. This study combines the impact mechanism of senile dementia vascular endothelial cells with cloud computing to construct a corresponding data retrieval platform of the cloud computing image set. Moreover, this study uses Hadoop’s core framework distributed file system HDFS to upload images, store the images in the HDFS and image feature vectors in HBase, and use MapReduce programming mode to perform parallel retrieval, and each of the nodes cooperates with each other. The results show that the proposed method has certain effects and can be applied to medical research.

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Pourasad, Yaghoub, and Fausto Cavallaro. "A Novel Image Processing Approach to Enhancement and Compression of X-ray Images." International Journal of Environmental Research and Public Health 18, no. 13 (June 22, 2021): 6724. http://dx.doi.org/10.3390/ijerph18136724.

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At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.

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Figueroa Maza, Kevin Jessid, and Luis Enrique Mendoza. "Platform for processing medical ultrasound obstetric images enabled in the cloud." Sistemas y Telemática 13, no. 32 (March 30, 2015): 9–25. http://dx.doi.org/10.18046/syt.v13i32.2014.

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Fetal monitoring using noninvasive imaging has been developing over the last thirty years. These studies enable future problems to be detected and prevented. This paper presents a software that uses ultrasound imaging to detect the nuchal translucency area. This area is important for the detection of trisomy 21 or Down syndrome. For this, the software implements techniques of morphological operations and the Watershed transformation. Additionally, a web platform that allows remote access to the software was developed. Finally, the detection of the area is demonstrated using the Watershed transformation.

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