Image-Based Amoebozoa Detection

• Main Authors: Kuei-Wen Kuo, 郭魁文
• Other Authors: 詹永寬
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/54301961439697936885

碩士 === 國立中興大學 === 資訊管理學系所 === 104 === The amoeba can cause people to infect many diseases. For example, the famous amebic dysentery, each year estimated half a billion people worldwide infected with amoebic dysentery, 100,000 people died of amoebic dysentery. The diagnostic modality of amoebic diseases relies on pathologists use microscopy. However, this method is heavily impacted by human’s behaviors such as physical factors, mental factors, and professional knowledge, etc. That can lead to diagnostic errors, reduce diagnostic quality and efficiency. If the computer technique can assist pathologist to diagnose, that can improve diagnostic efficiency, accuracy and lessen the impact of human factors. Let pathologists obtain information about the diagnosis of the disease quickly, and in the treatment of the illness can be more focused. Therefore, in this paper, we propose an amoeba detection methods. Our proposed method is divided into two parts. In the first part, we process the entire image. In the second part, we do Region Labeling against the processed images, then have further processing on those incomplete regions. First, we convert color images into grayscale images, enhance the images contrast and contour. Then we convert the images into binarization and segment the images. We process images after segmentation by Region Labeling and then confirm whether every region segment sufficiently through shapes. If not, this region corresponds to the original grayscale image, enhance the region contrast and contour. Then we convert the region into binarization, segment the region and remove noise. The remaining regions are amoebae. In this paper, we use a total of 115 images, which contains 146 amoebae. We use Precision, Recall and F-Measure to evaluate the results of this paper. It can be seen our proposed method can detect amoebae, but the result is not as expected. So, we offer some challenges and difficulties in this paper.