基于Top-hat变换的OSAHS图像边缘检测算法

提出了一种基于多方向、多尺度Top-hat变换的图像边缘检测方法,应用于阻塞性睡眠呼吸暂停低通气综合症(Obstructive Sleep Apnea Hypopnea Syndrome, OSAHS)早期病理图像的边缘检测及诊断。首先,构造不同方向、不同尺度的Top-hat算子增强图像的对比度,利用形态学梯度进行边缘检测,然后把各个算子检测到的图像边缘按照一定的权重进行组合,得到理想的边缘,以便准确地获得病理图像的相关参数,进而实现医学电子诊断。本文以口腔图像、咽喉声带处图像、鼻道内部图像的处理为例,这三组图像的处理结果表明,与传统的边缘算子相比较,该方法能使图像的边缘信息更完整、更准确,图像的边缘闭合度可达到97.67%。

OSAHS Image Edge Detection Algorithm Based on Top-hat Operator[JZ)][HS)]

This paper proposes an image edge detection method based on multi-directional, multi-scale Top-hat operators, and applies the method to the edge detection and diagnosis of OSAHS (Obstructive Sleep Apnea Hypopnea Syndrome, OSAHS) early pathological images. Firstly, construct multi-directional, multi-scale Top-hat operators, and they are used to Enhance the contrast of images. Use morphological gradient to detect the edge. Then the ideal image edge is obtained by combining the edges of the image detected by each operator according to a certain weight, so that I can obtain relevant parameters of pathology images accurately, and then achieve electronic medical diagnosis. Taking oral image, vocal cords oral image and inside nose image as examples, the processing results of images show that the operator proposed in this paper can make the edge information of the image more complete and accurate, compared with conventional edge operator, and edge closure of image is 97.67%.