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Fluorescence Intensity Positivity Classification of Hep-2 Cells Images Using Fuzzy Logic

机译：使用模糊逻辑的HEP-2细胞图像荧光强度阳性分类

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Indirect Immunofluorescence (IIF) is a good standard used for antinuclear autoantibody (ANA) test using Hep- 2 cells to determine specific diseases. Different classifier algorithm methods have been proposed in previous works however, there still no valid set as a standard to classify the fluorescence intensity. This paper presents the use of fuzzy logic to classify the fluorescence intensity and to determine the positivity of the Hep-2 cell serum samples. The fuzzy algorithm involves the image pre-processing by filtering the noises and smoothen the image, converting the red, green and blue (RGB) color space of images to luminosity layer, chromaticity layer 'a' and 'b' (LAB) color space where the mean value of the lightness and chromaticity layer 'a' was extracted and classified by using fuzzy logic algorithm based on the standard score ranges of antinuclear autoantibody (ANA) fluorescence intensity. Using 100 data sets of positive and intermediate fluorescence intensity for testing the performance measurements, the fuzzy logic obtained an accuracy of intermediate and positive class as 85% and 87% respectively.
机译：间接免疫荧光（IIF）是使用HEP-2细胞进行抗核自身抗体（ANA）测试的良好标准，以确定特定疾病。在以前的工作中提出了不同的分类器算法方法，但是，仍然没有有效的设置为分类荧光强度的标准。本文介绍了模糊逻辑对荧光强度进行分类，并确定HEP-2细胞血清样品的阳性。模糊算法涉及通过过滤噪声并使图像进行平滑，将图像的红色，绿色和蓝色（RGB）颜色空间转换为亮度层，色度层'A'和'B'（实验室）颜色空间通过使用基于抗核自身荧光强度的标准分数范围的模糊逻辑算法提取和分类亮度和色度层'A'的平均值。使用100个数据集的正和中间荧光强度用于测试性能测量，模糊逻辑分别获得中间和正级别的准确性，分别为85％和87％。

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