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Journal articles on the topic 'Medical Images Processing'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

MOSTOVYI, V., and S. HORIASHCHENKO. "SEGMENTATION OF MEDICAL IMAGES." Herald of Khmelnytskyi National University. Technical sciences 289, no. 5 (2020): 51–56. https://doi.org/10.31891/2307-5732-2020-289-5-51-56.

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Segmentation is an integral part of the digital image processing process. It is the division or division of the image into some parts that meet the specified characteristics and characterize these areas and the image as a whole. At the segmentation stage, issues are solved that complement the standard tasks of image processing, namely coding, restoration, quality improvement. The segmentation process is considered an integral part of the tasks of image recognition, classification and identification. That is why segmentation has found its wide application in such areas as microbiology, medicine

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2

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 (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 orig

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Depeursinge, A., and H. Müller. "Sensors, Medical Images and Signal Processing:." Yearbook of Medical Informatics 19, no. 01 (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 or

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Depeursinge, A., and H. Müller. "Sensors, Medical Images and Signal Processing:." Yearbook of Medical Informatics 20, no. 01 (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 partic

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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 (2017): 181–91. http://dx.doi.org/10.5890/jand.2017.06.005.

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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 (1997): 97–115. http://dx.doi.org/10.1016/s0010-4825(96)00042-x.

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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 (2001): 22–27. http://dx.doi.org/10.1118/1.1328079.

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Abboud, Ali J. "Shape Adaptable Medical Multimedia Processing." Open Electrical & Electronic Engineering Journal 13, no. 1 (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

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

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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 (2009): 37–45. http://dx.doi.org/10.26634/jfet.4.3.278.

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Deepak, Kokate, and Nair Jijo. "An Efficient HIM Technique for Tumour Detection from MRI Images." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 2425–30. https://doi.org/10.31142/ijtsrd15627.

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Data mining techniques are widely used for data processing from large data set such as data center and data warehouse. An Image mining technique is a new form of data mining technique in the processing of image data. In the medical field, day by day size of medical images data is increasing. MRI images are one of them. The medical images like as CT scan, MR images are widely used in brain tumor detection, cancer detection from the human body. It is quite challenging and complicated work to detect abnormal cells and tissue such as tumor from MR image data sets. Due to higher importance and dema

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Engelmann, U., and H.-P. Meinzer. "Medical Images in Integrated Health Care Workstations." Yearbook of Medical Informatics 05, no. 01 (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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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 fram

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Singh, Kanishka, and Anshika Anshika. "Medical Image Processing of Alzheimer." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem36886.

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A progressive disease Alzheimer is the leading source of dementia in the world, and its frequency continues to increase, in part due to the aging of the world's population. The process of this disease is naturally characterized by two characteristic pathologies. The diagnosis is constructed on clinical presentation and fluid and imaging biomarkers that meet several criteria. There was a requirement to automatically diagnose definite diseases based on medical images and subjects. This helps doctors and radiologists take further steps to treat the disease. Alzheimer’s disease was chosen for this

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Liu, Chunmao. "Target Image Processing based on Super-resolution Reconstruction and Machine Learning Algorithm." Scalable Computing: Practice and Experience 25, no. 3 (2024): 1332–40. http://dx.doi.org/10.12694/scpe.v25i3.2639.

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This article proposes a target image processing method based on super-resolution reconstruction and machine learning algorithms, which solves the low-resolution problem in medical images during imaging. This method uses nonlocal autoregressive learning based on a medical image super-resolution reconstruction method. The autoregressive model is introduced into the sparse representation-based medical image super-resolution reconstruction model by utilizing medical image data inherent nonlocal similarity characteristics. At the same time, a clustering algorithm is used to obtain a classification

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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 (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 in

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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 (2017): 101–2. http://dx.doi.org/10.1007/s11554-017-0676-5.

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Nisha, Pal*1 &. Sohit Agarwal2. "BRAIN MR IMAGE TUMOR DETECTION USING WATERSHED SEGMENTATION." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 12 (2018): 224–27. https://doi.org/10.5281/zenodo.2526952.

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Today, in the medical field image processing plays a vital role. And medical imaging technology is an evolving and demanding technology. Medical images that helps to diagnose diseases. Brain tumor is a serious and dangerous disease sometimes it causes to persons death. Medical image gives a correct treatment of brain tumors. So many techniques are available for detection of brain tumors from MRI images. All these methods face challenges such as finding the position and proportion of the tumor. Detection of tumor from the brain images that are the most important and difficult part, for this ima

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Mohammed, Rasool Jawad. "Producing of High Quality Colored Images using scalable image processing techniques." LC International Journal of STEM 4, no. 4 (2024): 10–24. https://doi.org/10.5281/zenodo.10594220.

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One of the many digital approaches that came from the image processing domain is picture enhancement. These approaches are employed to enhance the perceptibility of images, or to transform the image into a format more suitable for human or machine analysis, and to highlight intricate elements that might otherwise remain indistinct. The primary topic of this thesis is the utilization of the pseudocolor approach, which is an image enhancement technique, to convert grayscale intensity images into color-coded images. An investigation into the various forms of pseudocolor techniques that have been

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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 (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,

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Chaudhary, Sunita. "An Efficient Medical Image Processing Approach Based on a Cognitive Marine Predators Algorithm." International Journal on Future Revolution in Computer Science & Communication Engineering 8, no. 1 (2022): 08–14. http://dx.doi.org/10.17762/ijfrcsce.v8i1.2084.

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Image processing aims to enhance the image's quality such that it is simple for both people and robots to understand. Medical image processing and Biomedical signal processing have many conceptual similarities. Medical image processing involves evaluation, enhancement, and presentation. The focus of medical imaging is on obtaining photographs for both therapeutic and diagnostic reasons. In the existing Marine Predator Algorithm, different disadvantages are experienced when various automated optimization algorithms are used to the problem of ECG categorization. The proposed method follows the f

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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 (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

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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 (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 syste

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Shruthishree and Harshvardhan Tiwari. "A REVIEW PAPER ON MEDICAL IMAGE PROCESSING." International Journal of Research -GRANTHAALAYAH 5, no. 4RACSIT (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 tr

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Shruthishree, S.H, and Tiwari Harshvardhan. "A REVIEW PAPER ON MEDICAL IMAGE PROCESSING." International Journal of Research - Granthaalayah 5, no. 4 RACSIT (2017): 21–29. https://doi.org/10.5281/zenodo.572290.

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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 tr

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M., Najela Fathin, S. Shajun Nisha Dr., and M. Mohamed Sathik Dr. "Comparative Study on Cancer Images using Watershed Transformation." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2476–80. https://doi.org/10.31142/ijtsrd12767.

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Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this

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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 (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

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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 (2002): 441–70. http://dx.doi.org/10.1006/dspr.2001.0401.

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Rao, K. Prahlad. "Calvarial Bone Segmentation from Medical Images by Image Processing Technique." IJARCCE 4, no. 12 (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 (2021): 012003. http://dx.doi.org/10.1088/1742-6596/1911/1/012003.

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KATSUI, Tadashi, Shigeru TOMITA, Masahiko SAOTOME, et al. "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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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 (2017): 243–58. http://dx.doi.org/10.14257/ijsip.2017.10.1.24.

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Al-Shoukry, Suhad, and Zalili Binti Musa. "An Overview onEffectiveness of Activation Functions in Processing Medical Images." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (2024): 163–80. http://dx.doi.org/10.46649/fjiece.v3.2.13a.22.5.2024.

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This study studies the impact of activation functions in the field of machine learning and deep learning in general and especially on medical images for different aims such as classification, clustering, feature engineering. Important components that add nonlinearity and allow networks to learn intricate patterns are activation extraction, training and etc. The study first starts by explaining various activation function types that are commonly used in NN applications and fields. Subsequently, a comprehensive comparative analysis is conducted, to evaluate how activation functions perform in te

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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 (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

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Hu, Yiran. "Deep learning on medical imaging images." Applied and Computational Engineering 54, no. 1 (2024): 271–76. http://dx.doi.org/10.54254/2755-2721/54/20241686.

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For a considerable amount of time, medical image processing has been an important topic of research in the field of medicine. The advent of deep learning technology has resulted in the development of revolutionary improvements in this particular sector. Because of its remarkable effectiveness in training enormous amounts of data and performing difficult tasks, deep learning has attracted a lot of attention. It has found extensive application in the analysis, diagnosis, and treatment of medical pictures. This work employs the literature review approach to examine and analyze the existing resear

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Patil, Rajesh, and Surendra Bhosale. "Medical Image Denoising Techniques: A Review." International Journal on Engineering, Science and Technology 4, no. 1 (2022): 21–33. http://dx.doi.org/10.46328/ijonest.76.

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Medical imaging means the methods and procedures used for creating pictures of various parts of the human body for numerous clinical objectives. These images are constantly gets dirtied by noise during picture acquisition and transmission, resulting in low quality images. Noise is the unwanted signal which corrupts the important and desirable information. The noises can be categorized into different types based on their nature and origin. e.g. Gaussian, the impulsive and speckle noise etc. The removal of noise is very necessary for proper analysis and diagnosis. Filtering noise helps to recrea

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Ibragimova Iroda Vakhidovna. "Description of the Field of Radiology in Medical Imaging." Texas Journal of Medical Science 21 (June 8, 2023): 1–3. http://dx.doi.org/10.62480/tjms.2023.vol21.pp1-3.

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In medicine, the role of medical images in the field of radiology is very important. Accuracy of medical images and high quality of images are very important to the attending physician in making an accurate diagnosis of the patient. Processing, storage, transformation of medical images, conversion from analog to digital form.

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Bharti Gawali, Chitra Desai, Santosh Gaikwad,. "Discrete Wavelet Transformation based Multimodal Medical Image Fusion for Disease Identification." Power System Technology 48, no. 1 (2024): 620–30. http://dx.doi.org/10.52783/pst.319.

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In medical science, image processing techniques play a significant function. Computational automation of the treatment is the most authentic and prominent method. The disease of the brain is identified using Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Many more scan variations of MRI and PET have been executed for the medical diagnosis. The medical expert needs a solid strain of the computational scan and it’s related for diagnosis. The current era of computer research is turning towards clinical diagnosis and etiological analysis based on multimodal image processi

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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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Putyatin, Ye, P. Oroinskyi, and V. Lyashenko. "Detection of lung nodules on digital medical images." Bionics of Intelligence 2, no. 91 (2018): 48–53. https://doi.org/10.30837/bi.2018.2(91).07.

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This paper presents a study that addresses the issues of digital processing and analysis of medical images. As the medical images considered human lung images that are obtained by computer tomography. Procedure for detecting lung nodules considered. This helps to conduct the diagnosis of lung cancer disease. The efficiency and effectiveness of the proposed procedure is shown.

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John, Siju, and S. N. Kumar. "Medical Image Encryption using Latin Image Cipher Algorithm." Journal of Physics: Conference Series 2327, no. 1 (2022): 012070. http://dx.doi.org/10.1088/1742-6596/2327/1/012070.

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Abstract Image processing has significant applications in the health care sector, medical data processing, analysis, storage, and transfer. The Latin Image Cipher algorithm was proposed in this work for the encryption of medical images. The encryption algorithm proposed in this research work comprises Latin square whitening, substitution, and permutation. The efficiency of the algorithm was also validated by inducing noise in the input images. The performance validation of the proposed algorithm was validated by the histogram analysis and correlation analysis. The information entropy measure a

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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 (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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Abdulsttar Lafta, Sameer, Amaal Ghazi Hamad Rafash, Noaman Ahmed Yaseen Al-Falahi, Hussein Abdulqader Hussein, and Mohanad Mahdi abdulkareem. "Secure Medical Image Retrieval Using Fast Image Processing Algorithms." Scalable Computing: Practice and Experience 25, no. 5 (2024): 4323–34. http://dx.doi.org/10.12694/scpe.v25i5.3126.

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Content Based Image Retrieval (CBIR) is a relatively new idea in the field of real-time image retrieval applications; it is a framework for retrieving pictures from diverse medical imaging sources using a variety of image-related attributes, such as color, texture, and form. Using both single and multiple input queries, CBIR processes semantic data or the same object for various class labels in the context of medical image retrieval. Due to the ambiguity of image search, optimizing the retrieval of a query picture by comparing it across numerous image sources may be problematic. The goal is to

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Gulmirzaeva, G. A. "Using semantic indexing and thesauri in processing dermoscopic images." Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies 5, no. 2 (2025): 3071–76. https://doi.org/10.47813/2782-2818-2025-5-2-3071-3076.

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This article presents an innovative algorithm for automatic semantic indexing of dermoscopic images using clinical metadata (age, gender, anatomical location). The proposed approach integrates advanced natural language processing (NLP) techniques with medical thesauri and ontologies to enhance the efficiency of medical data analysis and structuring. The BioBERT neural language model was employed to generate clinical descriptions for each image, transforming natural language expressions into a vector space. These vector representations were then compared with selected semantic concepts from med

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Thirumoorthi, C., Manikandaprabhu P, and P. V. Praveen Sundar. "Analyzing CT Scan Images towards the Early Detection of Lung Cancer using Medical Images based Edge Feature Preserving CT Scan Medical Image Coder (EZWT - EFPIC)." Mapana Journal of Sciences 22, Special Issue 2 (2023): 261–72. http://dx.doi.org/10.12723/mjs.sp2.14.

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With the current improvements in virtual image processing techniques have received several benefits. Today, all of the scientific techniques produce virtual scientific pictures, through healthcare specialists analyze and diagnose the abnormality. The frequent view of scientific picture processing might also additionally appear simpler; however, it entails many challenges. As the scientific pics are interconnected with human lives, the laptop-aided scientific image processing structures have to be overcautious, if we want to eliminate inaccuracy rates. The utility of medical image processing te

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Maria Riasat. "Research on various image processing techniques." Open Access Research Journal of Chemistry and Pharmacy 1, no. 1 (2021): 005–12. http://dx.doi.org/10.53022/oarjcp.2021.1.1.0029.

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Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that can perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used applications for processing digital images. The image processing techniques play a vital role in image Acquisition, image pre-pro

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Khochare, Gaurav G., Shantanu S. Khopade, Pratik N. Khinde, Sachi D. Khobragade, and Sampada R. Khopade. "Brain Tumor Diagnosis Using CNN and Image Processing." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 2042–47. http://dx.doi.org/10.22214/ijraset.2023.53922.

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Abstract: Among the most tedious and time-consuming tasks in medical image processing is brain tumor identification and segmentation. Magnetic resonance imaging, or MRI, is a medical procedure that radiologists primarily utilize to examine the human body without having to do surgery. The substantial information that MRI provides on human soft tissue aids in the diagnosis of brain tumors. It is crucial to accurately segment MRI images in order to detect a brain tumour utilizing computeraided clinical techniques. Brain tumor identification facilitates determining the precise size and location of

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K P, Rejil. "Role Of Digital Image Processing In Education And Medical Field." IOSR Journal of Computer Engineering 26, no. 5 (2024): 01–08. http://dx.doi.org/10.9790/0661-2605030108.

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Digital image processing is a technique that uses computer algorithms to analyze images and transform images into better ones.It is an important branch of Telecommunication engineering that deals with the improvisation of reliability and accuracy of the digital communication by employing multiple techniques. Image processing involves many processes such as taking images, preprocessing, image enhancement, transformation and analysis. Variations in pixel values are one of the major challenges faced by image processing. Digital Signal Processing is at the core of virtually all of today's informat

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