Title :
Malaria Cell Counting Diagnosis within Large Field of View
Author :
Zou, Li-hui ; Chen, Jie ; Zhang, Juan ; García, Narciso
Author_Institution :
Educ. Minist. Key Lab. of Complex Syst. Intell. Control & Decision, Beijing Inst. of Technol., Beijing, China
Abstract :
Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs.
Keywords :
Hough transforms; biomedical optical imaging; blood; cellular biophysics; diseases; image segmentation; medical image processing; Hough transform; erythrocytes; field of view; image mosaicing; image processing algorithms; malaria cell counting diagnosis; parasitaemia; parasitic infections; segmentation; space-time manifolds; thin blood smears; Blood; Brightness; Computer vision; Diseases; Image color analysis; Image motion analysis; Strips; Circle Hough Transform; cell counting; image mosaicing; malaria diagnosis;
Conference_Titel :
Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-8816-2
Electronic_ISBN :
978-0-7695-4271-3
DOI :
10.1109/DICTA.2010.40
