Segmentation of choroid blood vessels in 3D images from optical coherence tomography

Tato práce se zabývá problematikou segmentace cév ve vrstvě cévnatky ve 3D obrazech z optické koherentní tomografie. První část je věnovaná teoretickému popisu lidského oka. Druhá část popisuje základní informace o optické koherentní tomografii. Ve třetí části jsou popsány dvě metody, které je možno využít na segmentaci cév. Poslední část je praktická. Zabývá se zpracováním snímků v programu OCTSEG a vytvořením vlastního algoritmu na segmentaci cév.This project deals with segmentation of choroid blood vessels in 3D images from optical coherence tomography. The first part is dedicated to theoretical description of the human eye. The second part describes basic information about the optical coherence tomography. In the third part two techniques, which are used on the blood vessels segmentation are described. The last part is practical and deals with an image processing in software OCTSEG and with own created algorithm for blood vessel segmentation.

재귀 신경망이 적용된 역 셀룰러 오토마타 학습법을 활용한 세포 분지(分枝) 예측

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 전누리.Biological processes are very complex so the predicting of such processes is considered to be very difficult. When it comes to the image-based prediction, one of the main reason of the predicting difficulty comes from the cost that gathering enough visual data which is extremely expensive. In this thesis, we introduce a novel method to reduce the cost of the prediction of cellular branching with Inverse Cellular Automata (ICA). This method assumes the biological image sequence as the set of cellular automata rules between t and t+1. With this assumption, single biological image sequence can provide width * height * frame number of cellular automata set of rule samples so they can be used as traditional Recurrent Neural Network(RNN) based machine learning. Proposed method overcomes the problem of lack of samples for machine learning in biological image predicting. We first briefly review the traditional cellular automata and introduce the concept of the ICA. And we will check the learnability with existing automata image rules. Finally, we show the prediction images with proposed method.Chapter 1. Introduction 1 Chapter 2. Inverse Cellular Automata Training 15 Chapter 3. Learnability of ICA 32 Chapter 4. Biological Image Sequence Application 42 Chapter 5. Conclusion 47 Bibliography 48 Abstract in Korean 50Docto