臺灣博碩士論文加值系統

瘧疾是目前盛行於東南亞與非洲之一種寄生蟲急性傳染病，是因人體被一種稱瘧原蟲之原蟲入侵所致。因為瘧原蟲能從病患血液中被發現，當醫生懷疑病患感染瘧疾時，通常會抽取病患的血液以製成抹片，再將抹片置於顯微鏡下觀察，以診斷病患是否感染瘧疾；並計算血液中瘧原蟲的個數，以判斷病患感染瘧疾的嚴重性。不過這種以人眼透過顯微鏡進行檢查的方式，是一項非常耗時又耗人力的工作。隨著操作人員的經驗、體力與所花費時間的多寡，其準確率也會因此受到影響。本論文以影像處理技術為基礎，發展出一自動檢測方法，以偵測出血液抹片影像中是否含有瘧原蟲，以協助醫療人員有效且精確的對瘧疾進行檢測。最常見的瘧原蟲有四類，其外觀皆不盡相同；即使是同種瘧原蟲，也會隨生長時期的不同，其外觀皆有相當大之差異。本論文之另一工作，便是從抹片影像中切割出瘧原蟲之區域，以便後續能對瘧原蟲種類與生長時期進行辨識。本論文將透過影像前處理、增強影像對比以及增強影像邊緣梯度等方法，偵測出影像中瘧原蟲的位置後，再用區域成長、相連區域切割等方法切割出瘧原蟲。實驗結果顯示我們所提出的方法，在偵測與切割瘧原蟲方面皆能獲得高度的正確率。

Malaria is an infectious parasitic disease which is widespread in Africa and South-East Asia. It is caused by a protozoan parasite of the genus Plasmodium. Since malaria parasites can be detected in the blood of infected patient, blood smear is commonly used to diagnose malaria under microscope. However, it is time-consuming and labor-intensive. Moreover, the experience of medical technologist has a great effect upon diagnostic accuracy. In this study, an automatic malaria parasite detector is proposed to perceive the malaria-infected erythrocytes from a blood smear image. This detector can more objectively and efficiently help the doctor diagnose malaria. Humans can be infected by four distinct species of Plasmodium parasites: Plasmodium falciparum (P. falciparum), Plasmodium vivax (P. vivax), Plasmodium ovale (P. ovale), and Plasmodium malariae (P. malariae). In this study, an automatic malaria parasite detector is proposed to diagnose the malaria from a blood smear image. The parasite appears in four stages in blood – ring, trophozoite, schizont, and gametocyte. Identifying the species and stage of the parasite is quite helpful in investigating the properties of malaria, preventing and diagnosing the malaria. Different Plasmodium species at different stage exhibit differences in their morphology and modify the host erythrocyte differently. It is hence feasible to develop an automatic system for identifying the species and the life stage of the parasite. How to segment the infected erythrocytes from a blood smear image and the parasite from the infected erythrocytes is essential for developing the automatic system. The other tasks of this study are hence to cut off the infected erythrocytes and parasite. The experimental results tell that the proposed method can provide impressive performance in detecting the malaria-infected erythrocytes and segmenting the infected erythrocytes and parasites.

Abstract (in Chinese) i
Abstract (in English) ii
Contents iii
List of Figures iv
List of Tables v
Chapter 1 Introduction 1
1.1. Background 1
1.2. Research Organization 5
Chapter 2 Related Works 6
2.1. Otsu’s Thresholding 6
2.2. Sobel Operator 7
2.3. Thinning and Spur Trimming 9
2.4. Genetic Algorithm 11
2.5. Segmentation Error Measure 11
Chapter 3 MP Detector 14
3.1. Image preprocessing 15
3.2. Region extraction 18
3.3. Segmentation of Parasite-Infected Erythrocyte and Parasite 22
3.4. Genetic-Based Parameter Detector (GBPD) 27
Chapter 4 Experiment Results 30
Chapter 5 Conclusions and Future Work 41
References 42

[1]World Health Organization., World malaria report 2009. Geneva: World Health Organization, 2009.
[2]A. Trampuz, M. Jereb, I. Muzlovic, and R. M. Prabhu, "Clinical review: Severe malaria," Crit Care, vol. 7, pp. 315-23, Aug 2003.
[3]P. L. Chiodini, et al., Atlas of medical helminthology and protozoology, 4th ed. Edinburgh ; New York: Churchill Livingstone, 2001.
[4]Caroline Cross, "Malaria control measure," (http://malaria.wellcome.ac.uk/doc_WTD023987.html)
[5]E. V. Perez-Jorge and T. Herchline, "Malaria: eMedicine Infectious Diseases," (http://emedicine.medscape.com/article/221134-overview)
[6]A. H. Moody and P. L. Chiodini, "Methods for the detection of blood parasites," Clinical & Laboratory Haematology, vol. 22, pp. 189-201, 2000.
[7]D. Payne, "Use and limitations of light microscopy for diagnosing malaria at the primary health care level," Bulletin of the World Health Organization, vol. 66, pp. 621-626, 1988.
[8]N. E. Ross, et al., "Automated image processing method for the diagnosis and classification of malaria on thin blood smears," Medical & Biological Engineering & Computing, vol. 44, pp. 427-436, May 2006.
[9]C. Di Ruberto, et al., "Analysis of infected blood cell images using morphological operators," Image and Vision Computing, vol. 20, pp. 133-146, 2002.
[10]S. W. S. Sio, et al., "MalariaCount: An image analysis-based program for the accurate determination of parasitemia," Journal of Microbiological Methods, vol. 68, pp. 11-18, Jan 2007.
[11]K. Zuiderveld, "Contrast limited adaptive histogram equalization," in Graphics gems IV: Academic Press Professional, Inc., 1994, pp. 474-485.
[12]S. Kumar, S. H. Ong, S. Ranganath, T. C. Ong, and F. T. Chew, "A rule-based approach for robust clump splitting," Pattern Recognition, vol. 39, pp. 1088-1098, 2006.
[13]M. T. Le, et al., "A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears," Bmc Cell Biology, vol. 9, Mar 28 2008.
[14]N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems Man and Cybernetics, vol. 9, pp. 62-66, 1979.
[15]R. C. Gonzalez and R. E. Woods, Digital image processing, 3rd ed. Upper Saddle River, N.J.: Prentice Hall, 2008.
[16]J. Canny, "A Computational Approach to Edge Detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PAMI-8, pp. 679-698, 1986.
[17]R. L. Haupt and S. E. Haupt, Practical genetic algorithms, 2nd ed. Hoboken, N.J.: John Wiley, 2004.
[18]E. D. Gelasca, et al., "Evaluation and benchmark for biological image segmentation," in Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on, 2008, pp. 1816-1819.
[19]M. S. Nixon and A. S. Aguado, Feature extraction and image processing, 1st ed. Oxford ; Boston: Newnes, 2002.
[20]M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic Imaging, vol. 13, pp. 146-168, 2004.