Detection Of Dysplasia From Endoscopic Images Using Daubechies 2 Wavelet Lifting Wavelet Transform
Detection Of Dysplasia From Endoscopic Images Using Daubechies 2 Wavelet Lifting Wavelet Transform
复制标题
使用 Daubechies 2 小波提升小波变换检测内窥镜图像中的不典型增生
DOI：
10.1109/icwapr48189.2019.8946452
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Minamoto Teruya
中科院分区：
文献类型：
--
作者：
Takeda Hiroaki;Minamoto Teruya
本文提出了一种基于提升小波变换的特征提取方法，用于内窥镜图像的异型增生检测。在所提出的方法中，输入的内窥镜图像被转换到色调饱和度值的颜色空间，和S空间的图像被使用。使用Daubechies 2（db2）小波提升小波变换从该图像学习异常区域的模式。使用学习的滤波器对检测到的图像执行提升小波变换。每个频率分量都是使用这种方法获得的。从高频分量的总和生成的检测图像被划分为小块。本文确定静态阈值以获得二值图像。离散小波变换用于排除平滑区域。V空间图像用于排除暗区域，如阴影。这强调了异常部分的轮廓。最后，从轮廓所包围的区域也是异常的这一观点出发，有限度地运用生命游戏来强调异常区域。我们详细描述了特征提取，并提出了实验结果表明，我们的方法是有用的发展从内窥镜图像的异型增生检测。
We propose herein a new feature extraction method based on the lifting wavelet transform for dysplasia detection from an endoscopic image. In the proposed method, the input endoscopic image is converted into the hue-saturation-value color space, and the S space image is used. The pattern of the abnormal area is learned from this image using Daubechies 2 (db2) wavelet lifting wavelet transform. The lifting wavelet transform is performed on the detected image using the learned filter. Each frequency component is obtained using this method. The detected image generated from the sum of the high-frequency components is divided into small blocks. A static threshold is determined herein to obtain a binary image. Discrete wavelet transform is used to exclude smooth areas. V space images are used to exclude dark areas, such as shadows. This emphasizes the contour of the abnormal part. Finally, from the idea that the area surrounded by the outline is also abnormal, the life game is limitedly applied to emphasize the abnormal area. We describe the feature extraction in detail and present the experimental results demonstrating that our method is useful for the development of dysplasia detection from an endoscopic image.