Segmentation, Splitting, and Classification of Overlapping Bacteria in Microscope Images for Automatic Bacterial Vaginosis Diagnosis
Segmentation, Splitting, and Classification of Overlapping Bacteria in Microscope Images for Automatic Bacterial Vaginosis Diagnosis
复制标题
显微镜图像中重叠细菌的分割、分裂和分类，用于自动细菌性阴道病诊断
DOI：
10.1109/jbhi.2016.2594239
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发表时间：
2017-07-01
影响因子：
7.7
通讯作者：
Wang, Tianfu
中科院分区：
文献类型：
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作者：
Song, Youyi;He, Liang;Wang, Tianfu
显微镜下细菌形态学的定量分析在细菌性阴道病（BV）的诊断中起着至关重要的作用。然而，该任务存在两个主要挑战：1）由于各种外观、模糊边界、异质形状、与背景的低对比度以及关于图像的小细菌尺寸，识别细菌区域是相当困难的。2)细菌数量众多，相互重叠，这阻碍了我们对单个细菌进行准确的分析。为了克服这些挑战，我们在本文中提出了一种通过定量分析细菌形态型来诊断BV的自动方法，该方法包括三步方法，即，细菌区域分割、重叠细菌分裂和细菌形态类型分类。具体来说，我们首先分割的细菌区域通过显着性切割，同时评估全局对比度和空间加权相干性。然后应用马尔可夫随机场模型对小目标进行高质量的无监督分割。然后，我们将重叠的细菌团块分解成标记，并将像素与标记相关联，以识别最终单个细菌分裂的证据。接下来，我们从每个细菌中提取形态特征来学习描述符，并使用Adaptive Boosting机器学习框架来表征细菌的类型。最后，基于Nugent评分标准实现了BV诊断。实验结果表明，该方法在BV诊断计算中具有较高的准确性和效率。
Quantitative analysis of bacterial morphotypes in the microscope images plays a vital role in diagnosis of bacterial vaginosis (BV) based on the Nugent score criterion. However, there are two main challenges for this task: 1) It is quite difficult to identify the bacterial regions due to various appearance, faint boundaries, heterogeneous shapes, low contrast with the background, and small bacteria sizes with regards to the image. 2) There are numerous bacteria overlapping each other, which hinder us to conduct accurate analysis on individual bacterium. To overcome these challenges, we propose an automatic method in this paper to diagnose BV by quantitative analysis of bacterial morphotypes, which consists of a three-step approach, i.e., bacteria regions segmentation, overlapping bacteria splitting, and bacterial morphotypes classification. Specifically, we first segment the bacteria regions via saliency cut, which simultaneously evaluates the global contrast and spatial weighted coherence. And then Markov random field model is applied for high-quality unsupervised segmentation of small object. We then decompose overlapping bacteria clumps into markers, and associate a pixel with markers to identify evidence for eventual individual bacterium splitting. Next, we extract morphotype features from each bacterium to learn the descriptors and to characterize the types of bacteria using an Adaptive Boosting machine learning framework. Finally, BV diagnosis is implemented based on the Nugent score criterion. Experiments demonstrate that our proposed method achieves high accuracy and efficiency in computation for BV diagnosis.