Université de Sousse, Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia.
Université de Sousse, Supérieur d'Informatique et des Techniques de Communication, Hammam Sousse, 4011, Tunisia.
J Digit Imaging. 2022 Dec;35(6):1560-1575. doi: 10.1007/s10278-022-00678-9. Epub 2022 Aug 1.
In this paper, we propose a new collaborative process that aims to detect macrocalcifications from mammographic images while minimizing false negative detections. This process is made up of three main phases: suspicious area detection, candidate object identification, and collaborative classification. The main concept is to operate on the entire image divided into homogenous regions called superpixels which are used to identify both suspicious areas and candidate objects. The collaborative classification phase consists in making the initial results of different microcalcification detectors collaborate in order to produce a new common decision and reduce their initial disagreements. The detectors share the information about their detected objects and associated labels in order to refine their initial decisions based on those of the other collaborators. This refinement consists of iteratively updating the candidate object labels of each detector following local and contextual analyses based on prior knowledge about the links between super pixels and macrocalcifications. This process iteratively reduces the disagreement between different detectors and estimates local reliability terms for each super pixel. The final result is obtained by a conjunctive combination of the new detector decisions reached by the collaborative process. The proposed approach is evaluated on the publicly available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to existing detectors as well as ordinary fusion operators.
在本文中，我们提出了一种新的协作过程，旨在从乳腺图像中检测出大钙化，同时尽量减少假阴性检测。该过程由三个主要阶段组成：可疑区域检测、候选对象识别和协作分类。主要思想是对整个图像进行操作，将其划分为同质区域，称为超像素，用于识别可疑区域和候选对象。协作分类阶段涉及让不同的微钙化检测器的初始结果协作，以产生新的共同决策并减少它们的初始分歧。检测器共享有关其检测到的对象及其相关标签的信息，以便根据其他协作者的信息来完善其初始决策。这种完善包括根据关于超像素和大钙化之间关系的先验知识，对每个检测器的候选对象标签进行局部和上下文分析，从而迭代更新。该过程迭代地减少了不同检测器之间的分歧，并估计了每个超像素的局部可靠性项。最终结果是通过协作过程达到的新检测器决策的联合组合获得的。该方法在公开可用的 INBreast 数据集上进行了评估。实验结果表明，与现有检测器和普通融合算子相比，在提高微钙化检测性能方面取得了收益。
