一种新的乳腺超声图像感兴趣区域定位算法

S. Michahial, B. A. Thomas
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引用次数: 1

摘要

乳腺癌是印度女性常见的癌症。主要目标是通过识别感兴趣的区域来实现乳腺癌检测过程的自动化。本文提出了一种自动识别和初始化乳腺超声图像感兴趣区域的新方法。我们在预处理阶段提出了混合改进的散斑减少各向异性扩散滤波器,然后提出了一种新的算法来提取感兴趣的区域。将高斯模糊滤波器和改进的SRAD滤波器相结合,设计了预处理中的混合滤波器。为了提取感兴趣的区域,提出了黑帽变换和简单阈值算法。将改进的SRDA应用于高斯模糊图像,减少了图像的散斑,增强了图像,找到了ROI。我们采用了黑帽变换和简单阈值两种方法来寻找ROI。在这项工作中使用了180张不同的BUS图像,其中78张是恶性的,102张是良性的。将所提出的方法应用于这些图像,准确率达到96%。提出的工作使现有的CAD系统通过在BUS图像中找到感兴趣的区域来实现自动化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel algorithm for locating region of interest in breast ultra sound images
Breast Cancer is a common cancer found in Indian women. The main objective is to automate the process of Breast Cancer detection by identifying the Region of interest. In this paper a novel approach to identify and initialize the region of interest automatically in a Breast Ultrasound image is proposed. We propose Hybrid Improved Speckle reducing anisotropic diffusion filter in the pre-processing stage followed by a novel algorithm to extract the region of interest. Hybrid filter in preprocessing is designed by combining Gaussian blur filter and Modified SRAD filter. To extract the region of interest Black Hat Transform and Simple threshold algorithm is proposed. Modified SRDA is applied on the Gaussian blur image which reduces the speckle and enhances the image to find the ROI. We deploy 2 methods to find the ROI, black hat-transform and Simple Threshold. In this work 180 different BUS images where used out of which 78 where malignant and 102 was benign. Proposed work was applied on these images and got an accuracy of 96%. The proposed work makes the existing CAD system automatic by finding the Region of interest in BUS images.
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