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The Determination of the Number of Suspicious Clustered Micro Calcifications on ROI of Mammogram Images

机译:乳腺X线照片可疑簇状微钙化数目的确定

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Micro calcifications (MCCs) appear as a small cluster of white spots on mammographic images. Numerous researches have been conducted on this abnormality. However, most of the methods focus on MCCs detection without further processing of the original mammogram image. The purpose of this paper is to detect and determine the number of suspicious MCCs on the mammogram image. In the MCCs detection, the system allows the manipulation of mammogram image by using digital image processing techniques. An automated segmentation cluster of suspicious MCCs is done based on the region of interest (ROI). For MCCs detection and determination, this paper proposes the use of Contrast-Limited Adaptive Histogram Equalization (CLAHE), Morphological Tophat filtering, Sobel edge detection and Morphological operation. The number of MCCs from the ROI mammogram image is determined by using the process of morphological structuring. As a result, the approach has been successfully tested on a number of samples and returns an accurate detection of MCCs on the ROIs of the mammogram image.
机译:微钙化(MCC)在乳腺X射线照片上显示为一簇白色的小斑点。已经对该异常进行了许多研究。但是,大多数方法都将重点放在MCC的检测上,而不需要对原始乳房X线照片进行进一步处理。本文的目的是检测并确定乳房X线照片上可疑MCC的数量。在MCC检测中,该系统允许通过使用数字图像处理技术来处理乳房X线照片。根据关注区域(ROI)进行了可疑MCC的自动细分群集。对于MCC的检测和确定,本文提出使用对比度限制的自适应直方图均衡(CLAHE),形态学Tophat滤波,Sobel边缘检测和形态学运算。通过使用形态结构化过程确定来自ROI乳房X线照片的MCC数量。结果,该方法已经在许多样本上成功测试过,并在乳房X线照片的ROI上返回了对MCC的准确检测。

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