| Summary: | 碩士 === 國立臺中技術學院 === 資訊科技與應用研究所 === 95 === The two-Dimensional electrophoresis gel image (2DE image) is important and popularly used in pathogenesis research. In our previous works, we used techniques such as chain code and vector quantization compression to develop detection and comparison techniques for 2DE images. There are also many other commercial software tools available for the same purpose. However the detection and comparison results of these tools and our past techniques cannot meet the accuracy requirement needed in protein research. For example most techniques require many parameters settings and cannot be tailor-adjusted to match the characteristics of individual protein spot. Since each individual protein spot is characteristically different, the detection process will result in some inaccuracies. Furthermore when comparing two 2DE images, the parameters used in both are quite close and are likely to be identified as images from the same group. Therefore for images with closer characteristics but are from different groups and for images with characteristics that are far apart, the current comparison techniques will result in issues such as incompleteness or slower process time.
For the biologist to be able access 2DE images quickly and accurately, it would be desirable to make improvements to increase the effectiveness and quality of the detection and comparison techniques. The proposed research is to develop improved detect and compare techniques for the 2DE images. The proposed techniques are named multi-layer cut and merge detection (MLCM) and progressive comparison techniques (PCT), respectively.
In the MLMC technique different thresholds are used on the same 2DE image to generate multiple copies of the 2DE binary images. Then on each generated image the detection technique detect for the most optimal parameters for the individual protein spots. The multiple images will then be stacked and the results from the individual generated images will be used to get optimal final detection for the overlapped protein spots. Experimental results on the MLCM technique proved that the detection technique achieved the highest detection accuracies as 95% and average detection accuracies as high as 89.6%.
In the PCT, the generated images from the MLCM technique will be used together with the concentration levels of the individual protein spots. First the proteins are quantized progressively by using a modified form of Euclidean distance equation to calculate the distance of overlapping protein spots between two images. The results are then progressively compared between every two images to get the optimal information on the protein spots. Results showed that more protein spots were can be identified. The highest comparison accuracies were as high as 97%, and average comparison accuracies as 94.2%.
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