Walker Ross F, Ishikawa Kanako, Kumagai Michio
Lake Biwa Research Institute, 1-10 Uchidehama, Otsu, Shiga 520-0806, Japan.
J Microbiol Methods. 2002 Oct;51(2):149-62. doi: 10.1016/s0167-7012(02)00057-x.
We exploit a property of microalgae-that of their ability to autofluoresce when exposed to epifluorescence illumination-to tackle the problem of detecting and analysing microalgae in sediment samples containing complex scenes. We have added fluorescence excitation to the hardware portion of our microalgae image processing system. We quantitatively measured 120 characteristics of each object detected through fluorescence excitation, and used an optimized subset of these characteristics for later automated analysis and species classification. All specimens used for training and testing our system came from natural populations found in Lake Biwa, Japan. Without the use of fluorescence excitation, automated analysis of images containing algae specimens in sediment is near impossible. We also used fluorescence imaging to target microalgae in water samples containing large numbers of obtrusive nontargeted objects, which would otherwise slow processing speed and decrease species analysis and classification accuracy. Object drift problems associated with the necessity to use both a fluorescence and greyscale image of each microscope scene were solved using techniques such as template matching and a novel form of automated seeded region growing (SRG). Our system proved to be not only user-friendly, but also highly accurate in classifying two major genera of microalgae found in Lake Biwa-the cyanobacteria Anabaena spp. and Microcystis spp. Classification accuracy was measured to be over 97%.
我们利用微藻的一种特性——即它们在落射荧光照明下自发荧光的能力——来解决在包含复杂场景的沉积物样本中检测和分析微藻的问题。我们在微藻图像处理系统的硬件部分增加了荧光激发功能。我们通过荧光激发对检测到的每个物体的120个特征进行了定量测量，并使用这些特征的优化子集进行后续的自动分析和物种分类。用于训练和测试我们系统的所有标本均来自日本琵琶湖中的自然种群。如果不使用荧光激发，对沉积物中含有藻类标本的图像进行自动分析几乎是不可能的。我们还使用荧光成像来定位含有大量干扰性非目标物体的水样中的微藻，否则这些物体会降低处理速度并降低物种分析和分类的准确性。通过模板匹配和一种新型的自动种子区域生长（SRG）等技术，解决了与每个显微镜场景都需要使用荧光和灰度图像相关的物体漂移问题。我们的系统不仅证明对用户友好，而且在对琵琶湖发现的两种主要微藻属——蓝藻鱼腥藻属和微囊藻属进行分类时非常准确。测量的分类准确率超过97%。
