Microcalcifications (MCs) are considered as the first indicator of breast cancer development. Their morphology, in terms of shape and size, is considered as the most important criterion that determines their malignity degrees. Therefore, the accurate delineation of MC is a cornerstone step in their automatic diagnosis process. In this paper, we propose a new conditional region growing (CRG) approach with the ability of finding the accurate MC boundaries starting from selected seed points. The starting seed points are determined based on regional maxima detection and superpixel analysis. The region growing step is controlled by a set of criteria that are adapted to MC detection in terms of contrast and shape variation. These criteria are derived from prior knowledge to characterize MCs and can be divided into two categories. The first one concerns the neighbourhood searching size. The second one deals with the analysis of gradient information and shape evolution within the growing process. In order to prove the effectiveness and the reliability in terms of MC detection and delineation, several experiments have been carried out on MCs of various types, with both qualitative and quantitative analysis. The comparison of the proposed approach with state-of-the art proves the importance of the used criteria in the context of MC delineation, towards a better management of breast cancer.

微钙化 (MC) 被认为是乳腺癌发展的第一个指标。就形状和大小而言，它们的形态被认为是决定其恶性程度的最重要标准。因此，准确勾画 MC 是其自动诊断过程的基石。在本文中，我们提出了一种新的条件区域增长 (CRG) 方法，能够从选定的种子点开始找到准确的 MC 边界。基于区域最大值检测和超像素分析确定起始种子点。区域生长步骤由一组标准控制，这些标准适用于对比度和形状变化方面的 MC 检测。这些标准来自先前的表征 MC 的知识可以分为两类。第一个涉及邻域搜索大小。第二个涉及对生长过程中的梯度信息和形状演变的分析。为了证明MC检测和勾画的有效性和可靠性，已经对各种类型的MC进行了多项实验，并进行了定性和定量分析。将所提出的方法与最先进的方法进行比较，证明了在 MC 描绘的背景下使用的标准对更好地管理乳腺癌的重要性。