정규 상호상관도 및 이진화 기법을 이용한 뇌종양 세포의 형광 현미경 영상 스티칭 (Image Stitching Using Normalized Cross-Correlation and the Thresholding Method in a Fluorescence Microscopy Image of Brain Tumor Cells)

서지정보

· 발행기관 : 한국멀티미디어학회
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· 저자명 : 서지현, 강미선, 김현정, 김명희

초록

This paper, which covers a fluorescence microscopy image of brain tumor cells, looks at drug reactions by treating different types and concentrations of drugs on a plate of 24x16 wells. Due to the limitation of the field of view, a well was taken into 9 field images, and each has an overlapping area with its neighboring fields. To analyze more precisely, image stitching is needed. The basic method is finding a similar area using normalized cross-correlation (NCC). The problem is that some overlapping areas may not have any duplicated cells that help to find the matching point. In addition, the cell objects have similar sizes and shapes, which makes distinguishing them difficult. To avoid calculating similarity between blank areas and roughly distinguishing different cells, thresholding is added. The thresholding method classifies background and cell objects based on fixed thresholds and finds the location of the first seen cell. After getting its location, NCC is used to find the best correlation point. The results are compared with a simple boundary stitched image. Our proposed method stitches images that are connected in a grid form without collision, selecting the best correlation point among areas that contain overlapping cells and ones without it.

영어초록

This paper, which covers a fluorescence microscopy image of brain tumor cells, looks at drug reactions by treating different types and concentrations of drugs on a plate of 24x16 wells. Due to the limitation of the field of view, a well was taken into 9 field images, and each has an overlapping area with its neighboring fields. To analyze more precisely, image stitching is needed. The basic method is finding a similar area using normalized cross-correlation (NCC). The problem is that some overlapping areas may not have any duplicated cells that help to find the matching point. In addition, the cell objects have similar sizes and shapes, which makes distinguishing them difficult. To avoid calculating similarity between blank areas and roughly distinguishing different cells, thresholding is added. The thresholding method classifies background and cell objects based on fixed thresholds and finds the location of the first seen cell. After getting its location, NCC is used to find the best correlation point. The results are compared with a simple boundary stitched image. Our proposed method stitches images that are connected in a grid form without collision, selecting the best correlation point among areas that contain overlapping cells and ones without it.

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