Ciecholewski Marcin
Faculty of Mathematics and Computer Science, Jagiellonian University, ul. Łojasiewicza 6, 30-348, Kraków, Poland.
J Digit Imaging. 2017 Apr;30(2):172-184. doi: 10.1007/s10278-016-9923-8.
This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512 × 512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones. The experiments carried out yielded the following average values of the measured indices: 80.5% (similarity index), 75.7% (overlap fraction), 70.8% (overlap value), and 19.8% (extra fraction). The average time of executing all steps of the methods used for a single ROI amounted to 0.83 s.
本出版物介绍了一种用于在乳腺钼靶图像中分割微钙化的计算机方法。它利用形态学变换,由两部分组成。第一部分从形态学上检测微钙化,从而确定其出现的大致区域,改善对比度,并减少乳腺钼靶图像中的噪声。在第二部分中,对微钙化进行分水岭分割。本研究是在一个测试集上进行的,该测试集包含200个大小为512×512像素的感兴趣区域(ROI),取自数字乳腺筛查数据库(DDSM)的乳腺钼靶图像,包括100例显示恶性病变的病例和100例显示良性病变的病例。所进行的实验得出了以下测量指标的平均值:80.5%(相似性指数)、75.7%(重叠分数)、70.8%(重叠值)和19.8%(额外分数)。用于单个ROI的方法的所有步骤的平均执行时间为0.83秒。