基于融合Canny-SIFT算法的MRI细微特征提取
MRI subtle feature extraction method based on fusion Canny-SIFT algorithm
刘浩宇 1刘孝保 1姚廷强 1申吉泓2
作者信息
- 1. 昆明理工大学机械工程学院,昆明 650500
- 2. 昆明医科大学第一附属医院,昆明 650093
- 折叠
摘要
针对磁共振影像(MRI)中的细微特征难提取、易缺失问题,提出一种基于融合Canny-SIFT算法的磁共振影像细微特征提取方法.该算法首先解决因图像的灰度不均匀以及噪声信号繁杂导致图中纹理、细微特征信息不清晰问题,采用自动Gamma变换增加图像对比度,由泛洪填充和离散余弦变换构成的混合滤波器对背景和观察区域噪声做分别处理;针对重点观测区域细微特征提取不完整问题,根据实际诊断要求进行重要程度划分,通过特征匹配和拓扑关系推理获取重要区域的位置、面积等方面信息,同时完成自适应选取相应So-bel算子;最后通过阈值化分割和二值化处理获得输出图像;实验表明:该方法与已有Canny边缘检测精度有明显提升,结构相似性相较于传统常见方式提升了 38.9％,均方误差下降了 31.33％,与所提及的其他算法相比性能表现为最优.
Abstract
Aiming at the problem that the subtle features in magnetic resonance images(MRI)are difficult to ex-tract and easy to be missing,a method for extracting subtle features of magnetic resonance images based on the fusion of Canny-SIFT algorithm is proposed.The algorithm first solves the problem of unclear texture and subtle feature infor-mation in the image due to the uneven gray level of the image and the complex noise signal.It uses automatic Gamma transformation to increase the image contrast.Separately deal with the noise of the observation area;for the incomplete extraction of subtle features in the key observation area,the importance is divided according to the actual diagnosis re-quirements,and the location,area and other information of the important area are obtained through feature matching and topological relationship reasoning,and the self-adaptation is completed at the same time Select the corresponding Sobel operator;finally,the output image is obtained by thresholding segmentation and binarization;Experiments show that the proposed method has significantly improved the edge detection accuracy compared with the existing Canny method,the structural similarity is increased by 38％,and the mean squared error is reduced by 31.4％,and the per-formance is the best compared with other mentioned algorithms.
关键词
图像处理/特征匹配/磁共振影像/Canny算法Key words
image processing/feature matching/MR image/Canny algorithm引用本文复制引用
基金项目
国家自然科学基金(82260297)
国家自然科学基金(81960133)
云南省高等学校女性盆底功能障碍性疾病研究与应用科技创新团队项目(K1322112)
出版年
2024
NETL
NSTL
万方数据