Abstract: Breast cancer is one of malignant tumours in women，the key to reduce the death rate of patients is early diagnosis and treatment.The Microcalcification Cluster（MCC） is one of the important symptoms of the early stage in breast cancer，so the first step to diagnose successfully is detecting microcalcifications quickly and accurately.At present，many methods which have their own advantages and shortcomings have been proposed for detecting microcalcifications，the typical Laplacion-of-a-Gaussian （LOG） method is one of the effective methods among them，however，it is inefficient though it can accurately detect the locations of microcalcifications.Cascading the morphology and LOG methods can improve the efficiency of detection，but it still can not satisfy the need of the large scale general survey of breast cancer.In this paper，an efficient and real-time detecting algorithm is proposed by cascading 1 dimensional and 2 dimensional LOGs and the results of experiments show that the detection method is efficient and effective.

摘要： 乳腺癌是妇女常见恶性肿瘤之一，早期诊断和早期治疗是降低乳腺癌患者死亡率的关键。微钙化是乳腺癌早期的一个重要标志，因此，快速准确地找出乳腺X光片中的钙化点成为成功诊断的第一步。现有多种方法能用于检测钙化点并各有优缺点，其中典型的高斯-拉普拉斯算子（LOG）是有效方法之一，尽管其能较精确地检出钙化点的位置但检测效率低。级联形态学滤波算子的LOG改进了LOG的效率，但仍无法满足大规模普查的高效率要求。通过提出一维和二维LOG相级联的方法来实现高效实时的钙化点的检测，并通过实验证实了所提检测方法的有效性。