影像處理及類神經網路於微細胞核自動計數之應用

Application of Image Processing Techniques and Neural Networks to Automatic Micronuclei Scoring

陳靜怡(Ching-Yi Chen)

指導教授 ： 李錫捷 ； 郭文嘉

元智大學/資訊學院/資訊管理學系/碩士(2005年)

摘要

隨著生物醫學應用影像技術來協助判讀的需要增加，以往需要大量顯微鏡操作及人力觀察辨識的工作也日漸需要一套標準化的系統來協助處理，以降低在時間及人力上的大量耗費。以微細胞核（Micronuclei ,MN）為例，在解讀的過程中除了得耗用大量人力判讀外，其辨識的結果也還存在不少變異性，因為這與專業訓練成熟度存在密切關係，研究人員的主觀看法皆可能會影響到最後的診斷結果，因此極需客觀的資訊工具來數位化這些過程，讓第三者也可以在事後以最迅速的方式來再次確認及給予建議。所以本研究主要運用影像技術及類神經網路對細胞影像進行自動化細胞計數，過程中會先將玻片上的影像自動數位化到電腦中，針對取得的玻片影像進行處理，將原影像轉成灰階圖再運用Otsu演算法自動尋找最佳閥值（Optimal Threshold）進行影像分割，並視情況結合形態學（Morphology）的技巧來消除雜訊，保留特徵型態，接著根據取得的輪廓資訊去計算影像形狀特徵值及將此輪廓套回原影像取得內部結構的特徵值，最後再將這些特徵值交由類神經網路的貝氏網路（Bayesian Network）進行型態辨識，找出辨識微細胞核的最佳特徵值組合。簡言之，步驟分別為：（1）玻片數位化，（2）色彩轉換，（3）影像分割，（4）初步細胞個數估算，（5）特徵值擷取，（6）找出最佳的特徵值組合，（7）自動化型態辨識。另外，本研究也提供了玻片影像還原程式，以方便研究人員可以隨時調閱細胞影像檔出來驗證診斷結果，並瀏覽其鄰近的細胞影像。

關鍵字

影像分析 ； 微細胞核 ； 類神經網路 ； 貝氏網路 ； 細胞辨識

並列摘要

In recent decades, demand for applying image-processing techniques to help biomedical diagnosis has increased rapidly. The traditional system processes, take Micronuclei (MN) scoring for example, use human eyes with microscopic which not only take too much time and human resources, but with unstable results for recognition. In addition, the results are highly dependant on the expertise of the researcher who conducted the experiment. As a result, an objective tool to digitize the process and automatic recognition will eliminate the subjective judgment from the researcher. Besides, the digitized slides and results can also be retrieved and verified anytime in the future conveniently.
In this study, we propose to integrate image processing techniques and Neural Networks to the automatic counting of cells. First, we digitize images on the slide automatically and store them to the computer. Then we convert the original images into gray scales and use Otsu algorithm to find the optimal threshold for image segmentation. In some cases, it is also required to apply morphological techniques to eliminate noises and preserve the important features. We then derive the features of shape from the information of the object contours and the internal structure obtained by referencing to the original image. Finally, we take these features as input to the Bayesian Network for the recognition of the Micronuclei. To sum up, the steps are as follows: 1) slide digitization, 2) color space transformation, 3) image segmentation, 4) initially counting of the cells, 5) feature retrieval, 6) optimal features finding, and 7) automatic Micronuclei recognition. In addition, an image restoration program is also developed which makes it more convenient for researchers to find the image files afterwards for further confirming and facilitates the browsing of nearby images.

並列關鍵字

Image Processing ； Micronuclei ； Neural Networks ； Bayesian Network ； Cells Recognition