Basic Image Analysis GUI for Cell Counting and Segmentation Tri Phan, Ravi Shrivastav, Rubina Narang

Background - Image processing allows the researchers to perform: analysis, segmentation, enhancement, geometric transformation & visualization. - The challenge is to effectively process and analyze the images in order to effectively extract, quantify, and interpret this information. - The general goal is to understand the information and put it to practical use.

Applications - Image processing and analysis has developed into one of the most important fields in biology and medicine for the study of cell structure and its characteristics. - A vital part of the early detection, diagnosis, and treatment of cancer. - Other areas: cell embryology, wound healing, host defense mechanisms and mechanisms of tumor metastasis and invasion.

Problem - The raw microscopic images of cells in biology are often prone to artifacts and imperfections. noise at low light levels uneven illumination - Manual counting procedure could be tedious and confusing at times. - Human vision can be easily biased by preconceived notions of objects and concepts.

Approach - Using Matlab to develop the GUI that: enhances the image quality by adjusting its contrast and brightness. utilizes spatial filtering, image transformation techniques to segment and quantify the cells. - Techniques to be implemented: Grayscale filter, Overlay, Detect centroids, Watershed filter.

DEMO

Input Data -User input: microscopic image of cells. -imread() will read and store image data.

Results

- The final output displays: The final processed image. The number of cells that the GUI can detect. - Export results to excel.

Conclusion -Different possible combinations of algorithms and techniques are necessary for a satisfactory result. -Techniques used might not be optimal for some particular type of cell images. -Different type of cell images requires different combinations of algorithms and parameters for better results.

Challenges - Many algorithms and techniques have been described in the literature. Which combination will yield the best results? - Imoverlay function. - Measure function.

Future works ❖ Analyzing complex overlapped cells in the image (hue saturation value). ❖ Measure function.

References 1.Dougherty, G.: Image analysis in medical imaging: recent advances in selected examples. Biomed. Imaging Interv. J. 6(3), e32 (2010). 2.Gonzalez R.C., Woods R.E., (2001) Digital Image processing, 2nd Edition, Upper Saddle River, Prentice Hall. 3.Gonzalez R.C., Woods R.E., Eddins S.L.(2004) Digital Image processing using MATLAB, Upper Saddle River, Prentice Hall. 4.