A Hybrid Multilayer Filtering Approach for Thyroid Nodule Segmentation on Ultrasound Images
A Hybrid Multilayer Filtering Approach for Thyroid Nodule Segmentation on Ultrasound Images
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DOI：
10.1002/jum.14731
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发表时间：
2019-03-01
影响因子：
2.3
通讯作者：
Shiran, Mohammad Bagher
中科院分区：
文献类型：
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作者：
Ardakani, Ali Abbasian;Bitarafan-Rajabi, Ahmad;Shiran, Mohammad Bagher
目的斑点噪声是影响超声图像对比度和分割失败的主要因素。确定有效的滤波器可以减少斑点噪声并提高分割性能。本研究的目的是定义一个有用的过滤器，以改善分割结果。方法采用中值、混合中值（Hmed）、傅立叶巴特沃思、傅立叶理想、小波（Wlet）、同态傅立叶巴特沃思、同态傅立叶理想、同态小波（Hmp_Wlet）、Frost、各向异性扩散、概率块基（PPB）和均匀区域滤波器等12种滤波器，寻找最佳滤波器进行甲状腺结节分割。接受者工作特征（ROC）分析用于结节分割过程中的滤波器评估。因此，从分割的区域测量10个形态参数，以找到预测分割性能的最佳参数。结果对比度受限自适应直方图均衡化、Wlet、Hmed、Hmp_Wlet和PPB滤波器4种混合滤波器的分割效果最好。与原始图像相比，这些滤波器的ROC曲线下面积范围为0.900至0.943，曲线下面积为0.685。在10个形态学参数中，面积、凸面积、等效直径、实体度和范围可以评价分割性能。结论对比度受限自适应直方图均衡化、Wlet、Hmed、Hmp_Wlet和PPB滤波器的混合滤波器具有很高的潜力，为超声图像中的甲状腺结节分割提供了良好的条件。除了ROC分析之外，分割区域的形态测量可以用于评估分割性能。
Objectives Speckle noise is the main factor that degrades ultrasound image contrast and segmentation failure. Determining an effective filter can reduce speckle noise and improve segmentation performances. The aim of this study was to define a useful filter to improve the segmentation outcome. Methods Twelve filters, including median, hybrid median (Hmed), Fourier Butterworth, Fourier ideal, wavelet (Wlet), homomorphic Fourier Butterworth, homomorphic Fourier ideal, homomorphic wavelet (Hmp_Wlet), frost, anisotropic diffusion, probabilistic patch-based (PPB), and homogeneous area filters, were used to find the best filter(s) to prepare thyroid nodule segmentation. A receiver operating characteristic (ROC) analysis was used for filter evaluation in the nodule segmentation process. Accordingly, 10 morphologic parameters were measured from segmented regions to find the best parameters that predict the segmentation performance. Results The best segmentation performance was reached by using 4 hybrid filters that mainly contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters. The area under the ROC curve for these filters ranged from 0.900 to 0.943 in comparison with the original image, with an area under the curve of 0.685. From 10 morphologic parameters, the area, convex area, equivalent diameter, solidity, and extent can evaluate segmentation performance. Conclusions Hybrid filters that contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters have a high potential to provide good conditions for thyroid nodule segmentation in ultrasound images. In addition to an ROC analysis, morphometry of a segmented region can be used to evaluate segmentation performances.