Results 51 to 60 of about 340,403 (177)

PENERAPAN CITRA TERKOMPRESI PADA SEGMENTASI CITRA MENGGUNAKAN ALGORITMA K-MEANS

JUTEI (Jurnal Terapan Teknologi Informasi), 2018
In the development of an image not only as a documentation of events. One area that requires image processing is in the field of medicine is radiology. In radiology there is a medical image required by doctors and researchers to be processed for patient ...
Angga Wijaya Kusuma, Rossy Lydia Ellyana
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AutoSegNet: An Automated Neural Network for Image Segmentation

IEEE Access, 2020
Neural Architecture Search (NAS) has drawn significant attention as a tool for automatically constructing deep neural networks. The generated neural networks are mainly applied for image classification, and natural language processing. However, there are
Zhimin Xu, Si Zuo, Edmund Y. Lam, Byoungho Lee, Ni Chen   +4 more
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Medical Image Segmentation

International Research Journal on Advanced Engineering Hub (IRJAEH)
Medical image segmentation is a critical component in the development of computer-aided diagnosis and treatment planning systems. This paper provides a comprehensive survey of recent advances in segmentation techniques applied to various imaging modalities, including Magnetic Resonance Imaging (MRI).
null Arpit Mohankar, null Aishwarya Nagpure, null Sania Shaikh, null Khushi Singh, null Firdous Jahan Shaikh   +4 more
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Deep learning for medical image segmentation: State-of-the-art advancements and challenges

Informatics in Medicine Unlocked
Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence of deep learning (DL) techniques. The use of layers in deep neural networks, like object form recognition in
Md. Eshmam Rayed, S.M. Sajibul Islam, Sadia Islam Niha, Jamin Rahman Jim, Md Mohsin Kabir, M.F. Mridha   +5 more
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Improved Multistage Edge-Enhanced Medical Image Segmentation Network of U-Net [PDF]

Jisuanji gongcheng
Medical image segmentation accuracy plays a key role in clinical diagnosis and treatment. However, because of the complexity of medical images and diversity of target regions, existing medical image segmentation methods are limited to incomplete edge ...
HU Shuai, LI Hualing, HAO Dechen
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Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation

Diagnostics, 2023
Medical image analysis plays an important role in clinical diagnosis. In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image ...
Peilun Shi, Jianing Qiu, Sai Mu Dalike Abaxi, Hao Wei, Frank P.-W. Lo, Wu Yuan   +5 more
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Interactive Segmentation for Medical Images Using Spatial Modeling Mamba

Information
Interactive segmentation methods utilize user-provided positive and negative clicks to guide the model in accurately segmenting target objects. Compared to fully automatic medical image segmentation, these methods can achieve higher segmentation accuracy
Yuxin Tang, Yu Li, Hua Zou, Xuedong Zhang   +3 more
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A novel framework for segmentation of small targets in medical images

Scientific Reports
Medical image segmentation represents a pivotal and intricate procedure in the domain of medical image processing and analysis. With the progression of artificial intelligence in recent years, the utilization of deep learning techniques for medical image
Longxuan Zhao, Tao Wang, Yuanbin Chen, Xinlin Zhang, Hui Tang, Fuxin Lin, Chunwang Li, Qixuan Li, Tao Tan, Dezhi Kang, Tong Tong   +10 more
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Research on Medical Image Segmentation Based on SAM and Its Future Prospects

Bioengineering
The rapid advancement of prompt-based models in natural language processing and image generation has revolutionized the field of image segmentation.
Kangxu Fan, Liang Liang, Hao Li, Weijun Situ, Wei Zhao, Ge Li   +5 more
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Medical image segmentation methods overview

Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï, 2018
This article provides an overview of the modern medical image segmentation methods. The most popular methods such as multi-atlas based methods and deep learning approach are considered in more details.
Bohdan V. Chapaliuk, Yuriy P. Zaychenko
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