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研究生(外文):Yu-Min Su
論文名稱(外文):Plasmodium Falciparum Diagnosis System Based On Blood Smear Image
指導教授(外文):Yung-Kuan Chan
口試委員(外文):Kuo-Lung HungWang Chuin-MuYung-Fu ChenMu-Yen Chen
外文關鍵詞:PlasmodiumMalariaImage SegmentImage recognition
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瘧疾是透過蚊蟲叮咬或是血液交換引起的全球性的傳染性疾病,普遍存在於熱帶區域,主要流行於非洲、東南亞、中南美洲等熱帶地區,每年受瘧疾所苦的病患上億也造成大量人口死亡。瘧疾病況變化迅速,早期的診斷及正確的治療是決定病患生命並且控制傳染散播的關鍵,尤其是惡性瘧原蟲的病徵反應特別猛烈,若是不能早期治療會引發多項併發症,病人死亡率大幅提升。若是能辨識出瘧疾原蟲的週期特性,或許可以找到防治甚至消滅的方法,因此本研究將針對惡性瘧原蟲進行分析,找出受感染的紅血球病並且自動辨識其為第幾週期。若是能提供快速並且準確的自動診斷系統,可以加速醫療人員對病患施予治療的流程,專家系統的輔助也可以減少醫療人員的誤判,將可以大幅提提升病患的生存機率並降低醫療成本。本研究中以血液抹片為基礎的惡性瘧疾診斷系統(PFD系統),會自動切割血液抹片影像中受感染的紅血球和瘧疾蟲,最後再結合單顆紅血球與其瘧疾蟲的資訊,利用不同週期有不同形狀的特性,取出各週期具有代表性的特徵做分析比較,將受惡性瘧原蟲感染的週期分類出來。根據實驗結果,本篇方法可以提供 85%以上的準確度。

Malaria is an infectious disease caused by mosquito bites or transmitted through blood transfusions. It is a common disease in Africa, Southeast Asia, and South America where are in the Tropic zone, and causing millions of people died of malaria every year. The control programs should focus on the provision of early diagnosis and prompt treatment, and an immediate, vigorous, and wide-scale response to epidemics. Malaria caused by Plasmodium falciparum (P. Falciparum) is responsible for 85 % of the malaria cases, and is the most dangerous malaria with rapidly progressive severe illness or death. Hence, the urgent initiation of appropriate therapy is especially critical. Knowing the life cycle is helpful in the understanding the properties of P. Falciparum and in the Derivatives for the Prevention and Treatment. In this research, an automatic P. Falciparum diagnosis system (PFD system) based on blood smear images is hence proposed. The PFD system separates the parasites and infected erythrocytes from a blood smear image; it then identifies the life cycle stages of P. Falciparum based on the features derived from the extracted infected erythrocytes and parasites. Experiment results illustrate that the PFD system achieves a recognition rate of over 85%.

摘要 i
Abstract ii
Table of contents iii
List of Tables v
List of Figures vi
Chapter1. Introduction 1
1.1 Motivation 4
1.2 Organization 5
Chapter2. Related work 7
2.1 Otsu’s Algorithm 7
2.2 Mathematical morphology 9
2.2.1 Dilation, Erosion, Opening, Closing 9
2.2.2 Region Filling 11
2.3 Region Labeling 13
2.4 Thinning and Spur Trimming 15
2.5 Weight Sobel Operation and Gradient Direction Weight 16
2.6 Run-Length 18
2.7 Segment Error Measure 18
2.8 MP Detector 19
Chapter3. PFD Segmentation 22
3.1 Information of erythrocyte 22
3.1.1 Enhance erythrocytes of image 22
3.1.2 Image binarization of erythrocytes 24
3.1.3 Single erythrocyte determination 27
3.2 Segmentation of single erythrocyte 29
3.2.1 Regions of interested 29
3.2.2 Erythrocyte edges enhancement 32
3.2.3 Image binarization of erythrocytes edges 36
3.2.4 Contours of infected erythrocytes 40
3.3 Genetic Algorithm 43
Chapter4. PFD identification 46
4.1 Features of P. Falciparum 46
4.2 Identification of growth stages for P. Falciparum 47
4.3 Genetic Algorithm 49
Chapter5. Experiment 50
5.1 Testing data in experiment 50
5.2 Evaluation of experiment 50
5.3 Experiment results and discussions 51
Chapter6. Conclusion 56
Reference 57

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