Discipline: Technology and Engineering
Subcategory: Biomedical Engineering
Ezra Connell - Lincoln Unversity
Co-Author(s): Doneisha Steele and Aliyah McIlwian, Lincoln University, PA
Images play important roles in human perception. An image is defined as a two-dimensional function f (x, y), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image. When x, y and the amplitude values are finite discrete quantities, the image is then called a digital image. Digital imaging processing encompasses processing whose input and output are images and in addition encompasses processes that extract attributes from images up to and including the recognition of individual objects. Imaging processing toolbox is a collection of MATLAB functions called M functions or M files that extend the capability of the MATLAB environment for the solution of digital imaging processing problems. Neurons are electrically volatile cells in the nervous system that operate as informants to process and transmit information to other nerve cells, muscle or gland cells. A neuron consists of branching dendrites with a projection called an axon, and a cell body, which conduct the nerve signal. Diseases such as amyotrophic lateral sclerosis, Parkinson’s, Alzheimer’s, and Huntington’s occur as a result of the loss of Neurons. Digital Image processing can be used to define the level of neuron activity in the brain as well as to determine the progression of neuron loss. This is valuable information because neurodegeneration diseases have different states of progression. By implementing low, medium and high level processes that are created in Imaging Processing Toolbox (IPT); level of neuron degeneration can be determined in comparison to the healthy neuron image. Low-level processes involve primitive operations such as noise reduction, contrast enhancement, and image sharpening. Mid-level processes involve tasks such as segmentation which represents image partitioning into certain regions of interest which are in our case neurites (dendrites and axons) and cell bodies, description of those objects to reduce them to a form suitable for computer processing and classification (recognition) of individual objects. Final high-level processing involves making sense of ensemble of recognized objects. A thresholding method is applied in the final stage of neuron image analysis when comparing a healthy, intact cortical neuron image and an image of a primary cortical cultured neuron with induced degeneration. Our findings show that image processing is a valuable tool for determining the levels of neuron degeneration. Future research involves being able to automatically analyze more neuron images in order to contribute to more successful neurodegeneration disease study.
References: Cheng, Hsiao-Chun, Christina M. Ulane, and Robert E. Burke. ‘Clinical Progression in Parkinson’s Disease and the Neurobiology of Axons.’ Annals of neurology 67.6 (2010): 715-725. PMC. Web. 12 Oct. 2016. Gonzalez, R. C. and Woods, R. E. . Digital image Processing, 2nd ed., Prentice Hall, Upper Saddle River, NJ.
Funder Acknowledgement(s): NSF/HBCU-UP.
Faculty Advisor: Vesna Zeljkovic, email@example.com
Role: Developing Mathematical and simulations models using Digital image processing techniques in MATLAB.