An Adaptive Utilization of Convolutional Matrix Methods on Sliced Hippocampal Neuron Cell Segmentation with an Application Interface


Neeraj Rattehalli1 and Ishan Jain2, 1Menlo-Atherton High School, USA, 2Mission San Jose High School, USA


Current methods of image analysis and segmentation on hippocampal neuron bodies contain excess and unwanted information like unnecessary noise. In order to clearly analyze each neural stain like DAPI, Cy5, TRITC, FITC and start the segmentation process, it is pertinent to preemptively denoise the data and create masked regions that accurately capture the ROI in these hippocampal regions. Unlike traditional edge detection algorithms like the Canny methods available in OpenCv libraries, we employed a more targeted approach based on pixel color intensities to segment out hippocampal neurons from the background. Using the R, G, and B value thresholds, our algorithm checks if a cell is a boundary point by doing neighboring pixel level comparisons. Combined with a seamless GUI interface for cropping the highlighted ROI, the algorithms efficiently work at creating general outlines of neuron bodies. With user modularity from the various thresholding values, the outlining and denoising presents clean data ready for analysis with object detection algorithms like FRCNN and YOLOv3.


Convolutional Matrix, Computer Vision, Machine Learning, Deep Learning, Automation Interface.

Full Text  Volume 10, Number 9