By the development of the microscope, it is now possible to observe the moving APP-GFPs in cells. By observing their movement, the elucidation of causes of diseases, such as Alzheimer, is expected. Presently, quantitative analysis is performed manually with the microscopes and eyes, consuming much effort of researchers. Therefore, in this report, we attempt the detection of APP-GFPs in cells as the first step of the movement analysis of APP-GFPs. Specifically, we perform preprocessing to the cell image for background noise removal, and try the 2-class classification between background and APP-GPP in each pixel. For the classification, we use 1-class support vector machine (OCSVM), which has been often used for pattern detection problems. Through several experimental results, we will observe the difficulties of the detection problem and consider possible remedies for future research.
|Translated title of the contribution||Detection of Granular Objects in Cell by Learning|
|Number of pages||6|
|Journal||IEICE technical report|
|Publication status||Published - Dec 2 2010|