Breast cancer is among the leading cause of death among females. Studies show that early detection allows for a better prognosis. Mammography is one of the successful ways for early detection of breast cancer. It mostly involves manual reading of mammograms, a process that is difficult and error-prone. This paper discusses a classification model for mammograms based on microcalcification characteristics, as a way of helping radiologists make quick and accurate diagnostic decisions by availing to them similar past cases. The images are pre-processed by Gaussian smoothing and median filtering with 5 x 5 and 3 x 3 kernels respectively. Gabor and Haralick features are then extracted to form the image signatures over which similarity measurements are made. Experimental results show an average precision value between 0.5 and 0.61 using Haralick features, 0.49 and 0.57 using Gabor features, and 0.51 and 0.78 using combination of Gabor and Haralick features.
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机译：乳腺癌是女性死亡的主要原因。研究表明，早期发现可以更好地预后。乳房X线照相术是早期发现乳腺癌的成功方法之一。它主要涉及手动读取乳房X线照片，此过程既困难又容易出错。本文讨论了基于微钙化特征的乳房X线照片分类模型，以此作为放射科医生通过利用他们过去的类似病例来做出快速，准确的诊断决策的一种方式。分别通过高斯平滑和中值滤波分别使用5 x 5和3 x 3内核对图像进行预处理。然后提取Gabor和Haralick特征以形成图像签名，并在该图像签名上进行相似性测量。实验结果表明，使用Haralick特征的平均精度值在0.5到0.61之间，使用Gabor特征的平均精度在0.49和0.57之间，使用Gabor和Haralick特征的组合在0.51和0.78之间。
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