%0 Journal Article %1 noauthororeditor %A Rattehalli, Neeraj %A Jain, Ishan %D 2020 %J A UTILIZATION OF CONVOLUTIONAL MATRIX METHODS ON SLICED HIPPOCAMPAL NEURON REGION IMAGES FOR CELL SEGMENTATION %K Automation Computer Convolutional Interface Matrix Vision %N 1/2/3 %P 01-09 %T A UTILIZATION OF CONVOLUTIONAL MATRIX METHODS ON SLICED HIPPOCAMPAL NEURON REGION IMAGES FOR CELL SEGMENTATION %V 9 %X Present methodologies for cell segmentation on hippocampal neuron regions contain excess information leading to the creation of unwanted noise. To distinctly draw boundaries around the cells in each of the channels like DAPI, Cy5, TRITC, FITC, it is pertinent to start off by denoising the present data and cropping the relevant ROI for analysis to remove excess background information. Present edge detection methodologies like Canny Edge Detection create black and white outputs. It is difficult to accurately do edge detection with color throughout an entire image. As such, we utilized a more involved approach that uses pixel level comparisons to determine the existence of an edge points. By extrapolating all the available edge points, our algorithms are able to detect general edges throughout an imagine. To streamline the process, it has been accompanied with a GUI interface which allows for freehand crops. This information is stored in a downloadable txt file, which provides the necessary input for the thresholding and final cropping. Together, the interface works to create clean data which is ready for further analysis with algorithms likes FRCNN and YOLOv3

@article{noauthororeditor, abstract = {Present methodologies for cell segmentation on hippocampal neuron regions contain excess information leading to the creation of unwanted noise. To distinctly draw boundaries around the cells in each of the channels like DAPI, Cy5, TRITC, FITC, it is pertinent to start off by denoising the present data and cropping the relevant ROI for analysis to remove excess background information. Present edge detection methodologies like Canny Edge Detection create black and white outputs. It is difficult to accurately do edge detection with color throughout an entire image. As such, we utilized a more involved approach that uses pixel level comparisons to determine the existence of an edge points. By extrapolating all the available edge points, our algorithms are able to detect general edges throughout an imagine. To streamline the process, it has been accompanied with a GUI interface which allows for freehand crops. This information is stored in a downloadable txt file, which provides the necessary input for the thresholding and final cropping. Together, the interface works to create clean data which is ready for further analysis with algorithms likes FRCNN and YOLOv3 }, added-at = {2022-03-18T05:12:57.000+0100}, author = {Rattehalli, Neeraj and Jain, Ishan}, biburl = {https://www.bibsonomy.org/bibtex/28c3f10b03b5cc9e5ed0bd8b65253e0c0/leninsha}, interhash = {247669cc828fb3920eec8dc08aa970f4}, intrahash = {8c3f10b03b5cc9e5ed0bd8b65253e0c0}, issn = {2319-1015}, journal = {A UTILIZATION OF CONVOLUTIONAL MATRIX METHODS ON SLICED HIPPOCAMPAL NEURON REGION IMAGES FOR CELL SEGMENTATION}, keywords = {Automation Computer Convolutional Interface Matrix Vision}, number = {1/2/3}, pages = {01-09}, timestamp = {2022-03-18T05:17:10.000+0100}, title = {A UTILIZATION OF CONVOLUTIONAL MATRIX METHODS ON SLICED HIPPOCAMPAL NEURON REGION IMAGES FOR CELL SEGMENTATION }, volume = 9, year = 2020 }