Kocer Hasan Erdinc, Cevik Kerim Kursat, Sivri Mesut, Koplay Mustafa
Department of Electronics and Computer Education, Technical Education Faculty, Selcuk University, Konya, Turkey.
Department of Computer Programming, Bor Vocational High School, Nigde University, Nigde, Turkey.
Iran J Radiol. 2016 Feb 17;13(3):e25491. doi: 10.5812/iranjradiol.25491. eCollection 2016 Jul.
Developmental dysplasia of the hip (DDH) can be detected with ultrasonography (USG) images. However, the accuracy of this method is dependent on the skill of the radiologist. Radiologists measure the hip joint angles without computer-based diagnostic systems. This causes mistakes in the diagnosis of DDH.
In this study, we aimed to automate segmentation of DDH ultrasound images in order to make it convenient for radiologic diagnosis by this recommended system.
This experiment consisted of several steps, in which pure DDH and various noise-added images were formed. Then, seven different filters (mean, median, Gaussian, Wiener, Perona and Malik, Lee, and Frost) were applied to the images, and the output images were evaluated. The study initially evaluated the filter implementations on the pure DDH images. Then, three different noise functions, speckle, salt and pepper, and Gaussian, were applied to the images and the noisy images were filtered. In the last part, the peak signal to noise ratio (PSNR) and mean square error (MSE) values of the filtered images were evaluated. PSNR and MSE distortion measurements were applied to determine the image qualities of the original image and the output image. As a result, the differences in the results of different noise removal filters were observed.
The best results of PSNR values obtained in filtering were: Wiener (43.49), Perona and Malik (27.68), median (40.60) and Lee (35.35) for the noise functions of raw images, Gaussian noise added, salt and pepper noise added and speckle noise added images, respectively. After the segmentation process, it was seen that applying filtering to DDH USG images had low influence. We correctly segmented the ilium zone with the active contour model.
Various filters are needed to improve the image quality. In this study, seven different filters were implemented and investigated on both noisy and noise-free images.
髋关节发育不良（DDH）可通过超声（USG）图像检测。然而，该方法的准确性取决于放射科医生的技术。放射科医生在没有基于计算机的诊断系统的情况下测量髋关节角度。这导致DDH诊断出现错误。
在本研究中，我们旨在实现DDH超声图像的自动分割，以便通过该推荐系统方便放射学诊断。
本实验包括几个步骤，其中形成了纯DDH图像和各种添加噪声的图像。然后，对图像应用七种不同的滤波器（均值、中值、高斯、维纳、佩罗纳和马利克、李和弗罗斯特），并对输出图像进行评估。该研究首先在纯DDH图像上评估滤波器实现。然后，对图像应用三种不同的噪声函数，即斑点噪声、椒盐噪声和高斯噪声，并对噪声图像进行滤波。在最后一部分，评估滤波后图像的峰值信噪比（PSNR）和均方误差（MSE）值。应用PSNR和MSE失真测量来确定原始图像和输出图像的图像质量。结果，观察到不同去噪滤波器结果的差异。
滤波中获得的PSNR值的最佳结果分别为：对于原始图像、添加高斯噪声、添加椒盐噪声和添加斑点噪声的图像的噪声函数，维纳滤波器（43.49）、佩罗纳和马利克滤波器（27.68）、中值滤波器（40.60）和李滤波器（35.35）。在分割过程之后，可以看出对DDH USG图像应用滤波影响较小。我们使用主动轮廓模型正确分割了髂骨区域。
需要各种滤波器来提高图像质量。在本研究中，在有噪声和无噪声图像上实现并研究了七种不同的滤波器。
