Medical Imaging Process & Technology

ISSN:

2578-160X (Online)

Journal Abbreviation:

Med. Imag. Proc. Tech.

The purpose of Medical Imaging Process & Technology (MIPT) is to act as a platform for the exchange of research results concerning medical imaging technology in medical detection, prevention, health, etc. Articles published in MIPT will be of interest of medical imaging system and medical image processing, and the scope welcomes the manuscripts from all the medical imaging aspects including imaging diagnostics, radiology, endoscopy, medical thermal imaging technology, medical photography and microscope. The original research is highly regarded, such as visualization, data structure and representation, pattern matching and recognition, object detection, change detection, motion estimation and related methods.


 

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As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.

  1. The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
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  3. Where available, URLs for the references have been provided.
  4. The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines, which is found in About the Journal.
  5. If submitting to a peer-reviewed section of the journal, the instructions in Ensuring a Blind Review have been followed.
 

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Article Processing Charges (APCs)

Medical Imaging Process & Technology is an Open Access Journal under EnPress Publisher. All articles published in Medical Imaging Process & Technology are accessible electronically from the journal website without commencing any kind of payment. In order to ensure contents are freely available and maintain publishing quality, Article Process Charges (APCs) are applicable to all authors who wish to submit their articles to the journal to cover the cost incurred in processing the manuscripts. Such cost will cover the peer-review, copyediting, typesetting, publishing, content depositing and archiving processes. Those charges are applicable only to authors who have their manuscript successfully accepted after peer-review.

Journal TitleAPCs
Medical Imaging Process & Technology$1000

We encourage authors to publish their papers with us and don’t wish the cost of article processing fees to be a barrier especially to authors from the low and lower middle income countries/regions. A range of discounts or waivers are offered to authors who are unable to pay our publication processing fees. Authors can write in to apply for a waiver and requests will be considered on a case-by-case basis.

 

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Vol 7, No 1 (2024)

Table of Contents

Open Access
Article
Article ID: 3001
PDF
by Mariem Jarjar, Abid Abdellah, Hicham Rrghout, Mourad Kattass, Abdellatif Jarjar, Abdellhamid Benazzi
Med. Imag. Proc. Tech. 2024 , 7(1);    285 Views
Abstract The purpose of this research is to develop a new method for encrypting multiple superimposed or side-by-side images. The process begins by extracting the red, green, and blue channels from each image and converting them into vectors that combine to produce a single image that undergoes an advanced pixel-level Vigenere transform. In the next step, a pseudorandom transition occurs at the nucleotide, followed by a passage to codons for genetic crossover implementation specifically designed for image scrambling. The latter process is controlled by many random tables developed from selected chaotic maps, which ensures a high degree of flexibility and security in the encryption method. To evaluate the effectiveness and security of this innovative multi-image encryption algorithm, extensive simulations were performed using a large number of images randomly selected from the database. The simulation results prove the reliability and robustness of the method.
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Open Access
Review
Article ID: 6279
PDF
by Sahand Karimzadhagh, Elahe Abbaspour
Med. Imag. Proc. Tech. 2024 , 7(1);    145 Views
Abstract Radiomics, a quantitative approach to medical imaging, employs computational methods to extract features from the images, revealing hidden characteristics of specific regions. This emerging field leverages advanced techniques to analyze a spectrum of features from modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans, aiming to decode tissue characteristics, disease progression, and treatment responses. The radiomics workflow integrates image acquisition, segmentation, feature selection, and data integration, utilizing advanced techniques such as deep learning, machine learning, and data mining. Radiomics demonstrates considerable potential in cancer detection and management, exhibiting high sensitivity and specificity in distinguishing between benign and malignant tumors and predicting outcomes. However, challenges such as imaging protocol variability, overfitting, and standardization requirements impede its broad clinical adoption. Innovations in multi-modal radiomics, deep learning, and genomics integration strive to mitigate these constraints. This review elucidates radiomics’ capabilities, current applications, benefits, challenges, and future directions in oncology.
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Open Access
Review
Article ID: 7227
PDF
by Stefano Palazzo, Giovanni Zambetta, Roberto Calbi
Med. Imag. Proc. Tech. 2024 , 7(1);    25 Views
Abstract Advancements in Medical Image Segmentation have revolutionized clinical diagnostics and treatment planning. This review explores a wide range of segmentation techniques applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images, emphasizing their clinical implications and future directions. CT segmentation techniques, including U-Net and its variant nnU-Net, are essential in oncology for precise tumor delineation, in cardiology for coronary artery analysis, and in pulmonology for lung lesion detection. These methods enhance radiotherapy targeting, surgical planning, and overall diagnostic accuracy. The nnU-Net, known for its self-configuring nature, is particularly notable for setting new benchmarks in medical image segmentation tasks. MRI segmentation benefits from superior soft tissue contrast. Techniques like Mask Region-based Convolutional Neural Network (R-CNN) excel in identifying brain lesions, assessing musculoskeletal injuries, and monitoring soft tissue tumors. These methods support detailed visualization of internal structures, improving diagnosis and guiding targeted interventions. U-Net architectures also play a critical role in MRI segmentation, demonstrating high efficacy in various applications such as brain and prostate imaging. A systematic review of the literature reveals performance metrics for various segmentation techniques, such as accuracy, sensitivity, specificity, and processing time. Traditional methods like thresholding and edge detection are contrasted with advanced deep learning and machine learning approaches, highlighting the strengths and limitations of each. The review also addresses methodological approaches, including data collection, analysis, and evaluation metrics. Future prospects include integrating 3D and 4D segmentation, multimodal data fusion, and enhancing AI explain ability. These innovations aim to refine diagnostic processes, personalize treatments, and improve patient outcomes. Clinical applications of these segmentation techniques demonstrate significant advantages in radiology, oncology, and cardiology, though challenges such as data variability and noise persist. Emerging strategies like data augmentation and transfer learning offer potential solutions to these issues. The continuous evolution of medical image segmentation techniques promises to enhance diagnostic accuracy, efficiency, and the personalization of patient care, ultimately leading to better healthcare outcomes.
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Announcements

 

The Conflict of Interest Policy statement

Conflicts of interest may exist when professional judgements concerning a primary interest has the possibility of being influenced by a secondary interest (e.g.: financial gains). It is to be noted that even perceptions of conflicts of interest are as important as the actual conflicts of interest.

Posted: 2022-09-13 More...
 

The 33rd Annual ASE Scientific Sessions will be held on 10th -13th June, 2022

The 33rd Annual ASE Scientific Sessions will be held on 10-13 June, 2022 in Seattle, Washington, USA. The Scientific Sessions is the premier international conference dedicated to the use of cardiovascular ultrasound in clinical patient care.

Posted: 2022-04-10 More...
 

Summary of recommended conferences in medical imaging field

In the field of medical imaging, there are several recommended conferences as followed:

  1. The Medical Image Computing and Computer Assisted Intervention Society (MICCAI)
  2. Information Processing in Medical Imaging (IPMI)
  3. IEEE International Symposium on Biomedical Imaging (ISBI)
  4. SPIE Medical Imaging
  5. The International Conference on Medical Imaging with Deep Learning (MIDL)
Posted: 2021-06-26 More...
 
More Announcements...