A Three-Step Approach with Adaptive Additive Magnitude Selection for the Sharpening of Images by Lih-Jen Kau, Tien-Lin Lee

“…Extensive experiments on natural images as well as medical images with subjective and objective evaluations will be given to demonstrate the usefulness of the proposed approach.…”
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Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images by Macarena Boix, Begoña Cantó

“…In this work we present an efficient segmentationmethod for medical image analysis. In particular, with this methodblood cells can be segmented. …”
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Quantifying Optical Microangiography Images Obtained from a Spectral Domain Optical Coherence Tomography System by Roberto Reif, Jia Qin, Lin An, Zhongwei Zhi, Suzan Dziennis, Ruikang Wang

“…Therefore, a quantitative method for characterizing the angiography obtained from medical images would have several clinical applications. …”
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MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images by Tianhu Zhao, Tianhu Zhao, Yong Yue, Hang Sun, Jingxu Li, Yanhua Wen, Yudong Yao, Wei Qian, Yubao Guan, Shouliang Qi, Shouliang Qi

“…The model performs best on medical images with a 50% mask ratio, showing a 1.4% increase in accuracy compared to the optimal 75% mask ratio on natural images.DiscussionThe proposed MAEMC-NET effectively distinguishes between benign and malignant solitary pulmonary nodules and holds significant potential to assist radiologists in improving the diagnostic accuracy of PGN and SLA.…”
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An Optical Model for Translucent Volume Rendering and Its Implementation Using the Preintegrated Shear-Warp Algorithm by Bin Li, Lianfang Tian, Shanxing Ou

“…In order to efficiently and effectively reconstruct 3D medical images and clearly display the detailed information of inner structures and the inner hidden interfaces between different media, an Improved Volume Rendering Optical Model (IVROM) for medical translucent volume rendering and its implementation using the preintegrated Shear-Warp Volume Rendering algorithm are proposed in this paper, which can be readily applied on a commodity PC. …”
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Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration by Lotta M. Ellingsen, Jerry L. Prince

“…Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. …”
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3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images by Zhuliang Qian, Lifeng Xie, Yisheng Xu

“…Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. …”
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Artificial intelligence for chest X-ray image enhancement by Liming Song, Hongfei Sun, Haonan Xiao, Sai Kit Lam, Yuefu Zhan, Ge Ren, Jing Cai

“…Recently, the successful application of deep learning (DL) algorithms in medical image analysis has prompted researchers to propose and design DL-based CXR image enhancement algorithms. …”
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Automated Techniques for the Interpretation of Fetal Abnormalities: A Review by Vidhi Rawat, Alok Jain, Vibhakar Shrimali

“…Ultrasound (US) image segmentation methods, focusing on techniques developed for fetal biometric parameters and nuchal translucency, are briefly reviewed. Ultrasound medical images can easily identify the fetus using segmentation techniques and calculate fetal parameters. …”
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EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS by S Sujatha, Pramod Pandey, D Gnana Rajesh

“…It is a specialized convolutional neural network that has shown significant potential in accurately segmenting intricate structures captured in medical images. It uses U-Net to precisely define the blood vessels from retinal images, facilitating accurate identification of macula regions for early AMD detection. …”
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Artificial intelligence in high-dose-rate brachytherapy treatment planning for cervical cancer: a review by Junyue Shi, Junyue Shi, Jun Chen, Gaokui He, Qinghe Peng

“…Recent advancements in artificial intelligence (AI), particularly in medical image processing, offer significant potential for automating and standardizing treatment planning in HDR-BT. …”
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Fast two dimensional to three dimensional registration of fluoroscopy and CT-scans using Octrees on segmentation maps by Luca Bertelli, Frédéric Gibou

“…We introduce a computationally efficient approach to the generation of Digital Reconstructed Radiographs (DRRs) needed to perform three dimensional to two dimensional medical image registration and apply this algorithm to virtual surgery. …”
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Thickness Mapping of Eleven Retinal Layers Segmented Using the Diffusion Maps Method in Normal Eyes by Raheleh Kafieh, Hossein Rabbani, Fedra Hajizadeh, Michael D. Abramoff, Milan Sonka

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Landmark Optimization Using Local Curvature for Point-Based Nonlinear Rodent Brain Image Registration by Yutong Liu, Balasrinivasa R. Sajja, Mariano G. Uberti, Howard E. Gendelman, Tammy Kielian, Michael D. Boska

“…To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). …”
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A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach by Mohammed Benjelloun, Saïd Mahmoudi, Fabian Lecron

“…We propose a medical image segmentation approach based on the Active Shape Model theory. …”
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Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising by Rudy Gunawan, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, Rifai Chai

“…There is limited information about using Blind/No Reference (NR) quality evaluators in the medical image area. This paper shows the previously utilized Natural Image Quality Evaluator (NIQE) in CT scans; this NIQE is commonly used as a photolike image evaluator and provides an extensive assessment of the optimum NIQE setting. …”
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Alzheimer's disease image classification based on enhanced residual attention network. by Xiaoli Li, Bairui Gong, Xinfang Chen, Hui Li, Guoming Yuan

“…To address these issues, this study proposes a deep learning model to detect Alzheimer's disease; it is called Enhanced Residual Attention Network (ERAN) that can classify medical images. By combining residual learning, attention mechanism, and soft thresholding, the feature representation ability and classification accuracy of the model have been improved. …”
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Exploring the Impact of Large Language Models on Disease Diagnosis by Ibrahim Almubark

“…This review shows that LLMs have utilized a variety of medical data sources, including general medical databases, specialized documents, medical images, and genomic data. Moreover, the focus of these LLMs spans a broad spectrum of healthcare fields, addressing chronic conditions, respiratory diseases, cancer, and rare diseases. …”
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AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection by Zhiyang Chen, Tingting Xie, Shuting Chen, Zhenhui Li, Su Yao, Xuanjun Lu, Wenfeng He, Chao Tang, Dacheng Yang, Shaohua Li, Feng Shi, Huan Lin, Zipei Li, Anant Madabhushi, Xiangtian Zhao, Zaiyi Liu, Cheng Lu

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Mathematical Representation of Color Spaces and Its Role in Communication Systems by Riyadh M. Al-saleem, Baraa M. Al-Hilali, Izz K. Abboud

“…The development of color systems has an impact on visual communication such as television broadcasting systems, medical image processing, and video signal processing, as well as in the field of computer such as graphic equipment and printing.…”
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