Abstract：
BACKGROUND: The growing rate of breast cancer necessitates immediate global attention. Mammography images are used to determine the stage of malignancy. Breast cancer stages must be identified in order to save a person\'s life.
OBJECTIVE: This article\'s main goal is to identify different techniques to obtain the difference between two breast cancer mammography images taken of the same individual at different times. This is the first effort to identify breast cancer in mammography images using change detection techniques. The Mammogram Image Change Detection (ICD) technique is also a recent advancement to prevent breast cancer in the early stage and precancerous level in medical images.
METHODS: The main purpose of this work is to observe the changes between breast cancer images in different screening periods using different techniques. Mammogram Breast Cancer Image Change Detection (MBCICD) methods usually start with a Difference Image (DI) and classify the pixels in the DI into changed and unaffected classes using unsupervised fuzzy c means (FCM) clustering methods based on texture features taken from the log and mean ratio difference pictures. Two operators, mean ratio and log ratio, were used to check the changes in the images. The Gabor wavelet is utilized as a feature extraction technique among several standards. Using the Gabor wavelet ratio operators is a useful method for altering the detection of breast cancer in mammography images. Currently, it is challenging to obtain real malignant images of the same person for testing or training. In this study, two images are utilized. To clearly see the changes, one is an image from the MIAS breast cancer mammography images dataset, and the other is a self-generated change image.
RESULTS: The research aims to examine the image results and other quantitative analysis results of proposed change detection methods on cancer images. The Mean Ratio Accuracy result is 0.9738, and the Log ratio PCC is 0.9737. The classification results are the Log Ratio + Gabor Filter + FCM is 0.9737, and Mean Ratio +Gabor Filter + FCM is 0.9719. The mean Ratio Accuracy result is 0.9738, Log ratio is 0.9737. Log Ratio + Gabor Filter + FCM is 0.9737, Mean Ratio +Gabor Filter + FCM is 0.9719. Comparing the PCC of proposed change detection methods with the FDA-RMG method on the same dataset, the accuracy is 0.9481 only.
CONCLUSIONS: The study concludes that variations in mammography breast cancer images could be successfully identified using the ratio operators with Gabor wavelet features.
OBJECTIVE: This article\'s main goal is to identify different techniques to obtain the difference between two breast cancer mammography images taken of the same individual at different times. This is the first effort to identify breast cancer in mammography images using change detection techniques. The Mammogram Image Change Detection (ICD) technique is also a recent advancement to prevent breast cancer in the early stage and precancerous level in medical images.
METHODS: The main purpose of this work is to observe the changes between breast cancer images in different screening periods using different techniques. Mammogram Breast Cancer Image Change Detection (MBCICD) methods usually start with a Difference Image (DI) and classify the pixels in the DI into changed and unaffected classes using unsupervised fuzzy c means (FCM) clustering methods based on texture features taken from the log and mean ratio difference pictures. Two operators, mean ratio and log ratio, were used to check the changes in the images. The Gabor wavelet is utilized as a feature extraction technique among several standards. Using the Gabor wavelet ratio operators is a useful method for altering the detection of breast cancer in mammography images. Currently, it is challenging to obtain real malignant images of the same person for testing or training. In this study, two images are utilized. To clearly see the changes, one is an image from the MIAS breast cancer mammography images dataset, and the other is a self-generated change image.
RESULTS: The research aims to examine the image results and other quantitative analysis results of proposed change detection methods on cancer images. The Mean Ratio Accuracy result is 0.9738, and the Log ratio PCC is 0.9737. The classification results are the Log Ratio + Gabor Filter + FCM is 0.9737, and Mean Ratio +Gabor Filter + FCM is 0.9719. The mean Ratio Accuracy result is 0.9738, Log ratio is 0.9737. Log Ratio + Gabor Filter + FCM is 0.9737, Mean Ratio +Gabor Filter + FCM is 0.9719. Comparing the PCC of proposed change detection methods with the FDA-RMG method on the same dataset, the accuracy is 0.9481 only.
CONCLUSIONS: The study concludes that variations in mammography breast cancer images could be successfully identified using the ratio operators with Gabor wavelet features.
摘要：
背景：乳腺癌的增长速度需要立即引起全球关注。乳房X线照相术图像用于确定恶性肿瘤的阶段。为了挽救一个人的生命，必须确定乳腺癌的分期。
目的：本文的主要目标是识别不同的技术，以获得在不同时间拍摄同一个体的两个乳腺癌乳房X线摄影图像之间的差异。这是首次使用变化检测技术在乳房X线照相术图像中识别乳腺癌。乳房X线图像变化检测（ICD）技术也是在医学图像中预防早期和癌前水平乳腺癌的最新进展。
方法：这项工作的主要目的是使用不同的技术观察不同筛查时期乳腺癌图像之间的变化。乳房X光检查乳腺癌图像变化检测（MBCICD）方法通常从差异图像（DI）开始，并使用无监督模糊c均值（FCM）聚类方法将DI中的像素分类为变化的和未受影响的类别，该方法基于从对数和平均比率差异图片中获取的纹理特征。两个操作员，平均比率和对数比率，用于检查图像中的变化。Gabor小波被用作几种标准中的特征提取技术。使用Gabor小波比率算子是改变乳房X线照相术图像中乳腺癌检测的有用方法。目前,获得同一人的真实恶性图像进行测试或训练是具有挑战性的。在这项研究中,利用两个图像。为了清楚地看到变化，一个是MIAS乳腺癌乳房X线照相术图像数据集的图像，另一个是自我生成的变化图像。
结果：该研究旨在检查所提出的变化检测方法对癌症图像的图像结果和其他定量分析结果。平均比率准确度结果为0.9738,对数比率PCC为0.9737。分类结果是对数比率+Gabor滤波器+FCM为0.9737,并且平均比率+Gabor滤波器+FCM为0.9719。平均比率精度结果为0.9738,对数比率为0.9737。对数比率+Gabor滤波器+FCM为0.9737,平均比率+Gabor滤波器+FCM为0.9719。将提出的变化检测方法的PCC与相同数据集上的FDA-RMG方法进行比较，精度仅为0.9481。
结论：该研究得出的结论是，使用具有Gabor小波特征的比率算子可以成功地识别乳房X光检查乳腺癌图像的变化。
目的：本文的主要目标是识别不同的技术，以获得在不同时间拍摄同一个体的两个乳腺癌乳房X线摄影图像之间的差异。这是首次使用变化检测技术在乳房X线照相术图像中识别乳腺癌。乳房X线图像变化检测（ICD）技术也是在医学图像中预防早期和癌前水平乳腺癌的最新进展。
方法：这项工作的主要目的是使用不同的技术观察不同筛查时期乳腺癌图像之间的变化。乳房X光检查乳腺癌图像变化检测（MBCICD）方法通常从差异图像（DI）开始，并使用无监督模糊c均值（FCM）聚类方法将DI中的像素分类为变化的和未受影响的类别，该方法基于从对数和平均比率差异图片中获取的纹理特征。两个操作员，平均比率和对数比率，用于检查图像中的变化。Gabor小波被用作几种标准中的特征提取技术。使用Gabor小波比率算子是改变乳房X线照相术图像中乳腺癌检测的有用方法。目前,获得同一人的真实恶性图像进行测试或训练是具有挑战性的。在这项研究中,利用两个图像。为了清楚地看到变化，一个是MIAS乳腺癌乳房X线照相术图像数据集的图像，另一个是自我生成的变化图像。
结果：该研究旨在检查所提出的变化检测方法对癌症图像的图像结果和其他定量分析结果。平均比率准确度结果为0.9738,对数比率PCC为0.9737。分类结果是对数比率+Gabor滤波器+FCM为0.9737,并且平均比率+Gabor滤波器+FCM为0.9719。平均比率精度结果为0.9738,对数比率为0.9737。对数比率+Gabor滤波器+FCM为0.9737,平均比率+Gabor滤波器+FCM为0.9719。将提出的变化检测方法的PCC与相同数据集上的FDA-RMG方法进行比较，精度仅为0.9481。
结论：该研究得出的结论是，使用具有Gabor小波特征的比率算子可以成功地识别乳房X光检查乳腺癌图像的变化。
