Construction of Probabilistic Maps for 3D Drosophila Neuron Images using Finite Mixture Models

• Main Authors: Wu, Chi-Hao, 吳啟豪
• Other Authors: 盧鴻興
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/93865000632051237287

碩士 === 國立交通大學 === 應用數學系數學建模與科學計算碩士班 === 99 === The main purpose of this study is to construct the probabilistic graph image (Probabilistic Maps) of Drosophila’s olfactory neurons and develop an automated image segmentation algorithm for the Drosophila olfactory neurons in the olfactory brain regions (Antennal Lobe) which has many different functions of Local Neurons. In the same 3-D image, there are many different functions of local neurons, and these functions appear in different colors and shapes. We use the unsupervised learning (statistical clustering) approach to construct the probabilistic maps and develop an automated color segmentation algorithm. In general, the image data has a lot of redundant information. Thus, we have to use various methods to remove noise. Because fruit flies have thin olfactory nerves, so using spatial filtering alone will often result in breaking the continuity of the nerves. The commonly used frequency domain filtering can reduce the chance of breaking the continuity of the nerves, but such a method requires a large amount of computation. Because of these issues, in this study, we utilized the characteristics of the image data and developed a method to remove noise as well as construct the probabilistic maps. Based on our knowledge of the Brainbow technology, we can know that different nerves appear in different colors, but in the process of obtaining the images, colors in different channels will have the phenomenon of cross talk. This makes extracting the color features much more difficult. In this study, we develop some color extraction methods with mixture models and utilize these features to construct the probabilistic maps and develop an automatic clustering algorithm.