Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Dual-Signal Direct Time-of-Flight Method for Long-Range Groundwater Level Monitoring in Observation Wells.

Sensors (Basel, Switzerland)·2026
Same author

Lightweight Multimodal Fusion for Urban Tree Health and Ecosystem Services.

Sensors (Basel, Switzerland)·2026
Same author

Lightweight early detection of knee osteoarthritis in athletes.

Scientific reports·2025
Same author

Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram Equalization Algorithm.

Sensors (Basel, Switzerland)·2025
Same author

Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection.

Sensors (Basel, Switzerland)·2025
Same author

PixMed-Enhancer: An Efficient Approach for Medical Image Augmentation.

Bioengineering (Basel, Switzerland)·2025
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
查看所有相关文章
  1. 首页
  3. 提高医疗图像分段和分类使用模糊驱动方法.
  1. 首页
  3. 提高医疗图像分段和分类使用模糊驱动方法.

相关实验视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

提高医疗图像分段和分类使用模糊驱动方法.

Akmal Abduvaitov1, Abror Shavkatovich Buriboev2,3,4, Djamshid Sultanov2

  • 1Department of Information Technologies, Samarkand Branch of Tashkent University of Information Technologies, Samarkand 140100, Uzbekistan.

Sensors (Basel, Switzerland)
|September 27, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

这项研究引入了一个基于模糊的12步图像增强管道,以改善医疗图像质量,用于自动瘤细分和疾病分类. 该方法显著提高了CT,MRI和X射线模式的准确性.

关键词:
连接在一起的CNN和CNN.模糊的方法模糊的方法.图像增强 图像增强 图像增强

更多相关视频

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.2K

相关实验视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.2K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 用于瘤细分和疾病分类的医学图像分析面临着由于噪音,低对比度和模糊性的挑战.
  • 现有的增强技术可能无法充分解决各种成像模式的这些局限性.

研究的目的:

  • 开发和评估一种基于模糊的12步图像增强管道,以提高医疗图像质量.
  • 评估管道在提高CT,MRI和X射线数据集中瘤和疾病的细分和分类准确性的有效性.

主要方法:

  • 开发了一种基于模糊的12步增强管道,利用模糊的,模糊的标准偏差和直方图扩散函数.
  • 该管道应用于CT (KiTS19),MRI (BraTS2020) 和X射线 (胸部X射线肺炎) 数据集.
  • 改进的数据集被用来训练一个连锁CNN (CCNN) 进行细分和分类任务,并与基线和CLAHE方法进行比较.

主要成果:

  • 该管道显著降低了数据集中的BRISQUE分数,表明图像质量有所改善 (例如,KiTS19:28.8至21.7).
  • CCNN实现了高性能:99.60%的子系数用于瘤细分 (KiTS19) 和大脑瘤 (BraTS2020) 和肺炎 (胸部X射线) 的高细分/分类精度.
  • 性能指标显示出优于基线和CLAHE增强方法的优势.

结论:

  • 拟议的基于模糊的增强管道有效地提高了CT,MRI和X射线模式的医疗图像质量.
  • 提高图像质量可以显著改善自动瘤细分和疾病分类准确度.
  • 该管道为先进的临床诊断提供了一个可扩展和可解释的基础.