The automatic cell analysis method is capable of segmenting the cells and can detect the number of live/dead cells present in the body. This study proposed a novel non-linear segmentation model (NSM) for the segmentation and quantification of live/dead cells present in the body. This work also reveals the aspects of electromagnetic radiation on the cell body. The bright images of the hippocampal CA3 region of the rat brain under the resolution of 60 × objective are used to analyze the effects called NISSL-stained dataset. The proposed non-linear segmentation model segments the foreground cells from the cell images based on the linear regression analysis. These foreground cells further get discriminated as live/dead cells and quantified using shape descriptors and geometric method, respectively. The proposed segmentation model is showing promising results (accuracy, 82.82%) in comparison with the existing renowned approaches. The counting analysis of live and dead cells using the proposed method is far better than the manual counts. Therefore, the proposed segmentation model and quantifying procedure is an amalgamated method for cell quantification that yields better segmentation results and provides pithy insights into the analysis of neuronal anomalies at a microscopic level. Graphical Abstract Resultant View of the overall proposed approach.

中文翻译：

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使用非线性分割模型区分和定量活/死大鼠脑细胞。

自动细胞分析方法能够分割细胞，并且可以检测体内存在的活/死细胞的数量。这项研究提出了一种新颖的非线性分割模型（NSM），用于分割和量化体内存在的活/死细胞。这项工作还揭示了细胞体上电磁辐射的各个方面。以60倍物镜分辨率拍摄的大鼠大脑海马CA3区的明亮图像用于分析称为NISSL染色数据集的效果。提出的非线性分割模型基于线性回归分析从细胞图像中分割前景细胞。这些前景单元进一步被区分为活/死单元，并分别使用形状描述符和几何方法进行量化。与现有的著名方法相比，该提议的分割模型显示出令人鼓舞的结果（准确性为82.82％）。使用提出的方法对活细胞和死细胞进行计数分析远胜于人工计数。因此，提出的分割模型和量化程序是一种用于细胞量化的混合方法，可产生更好的分割结果，并为微观水平的神经元异常分析提供精辟的见解。总体建议方法的图形化抽象结果视图。拟议的分割模型和量化程序是一种用于细胞量化的混合方法，可产生更好的分割结果，并为从微观角度分析神经元异常提供精辟的见解。整个建议方法的图形化抽象结果视图。拟议的分割模型和量化程序是一种用于细胞量化的混合方法，可产生更好的分割结果，并为从微观角度分析神经元异常提供精辟的见解。总体建议方法的图形化抽象结果视图。