Name
Abstract | ||
|---|---|---|
The visual analysis of peripheral blood samples is an important test for blood illnesses diagnosis, like leukaemia or malaria. Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome these issues, numerous methods of automatic malaria diagnosis have been proposed so far. Unfortunately, no public image dataset is available to test and compare such algorithms. The aim of this paper is to present the first public dataset of blood samples afflicted by malaria, specifically designed to evaluate and compare algorithms for segmentation and classification of malaria parasite species. Every image is provided with its related ground truth and parasite’s classification of type and stage of life. Our purpose is to offer a new comparative test tool to the image processing and pattern matching communities, in order to encourage and improve computer-aided malaria parasites analysis. |
Year | Venue | Field |
|---|---|---|
2018 | samba: sipaim – miccai biomedical workshop. | Blood stained,Visual inspection,Pattern recognition,Computer science,Segmentation,Image processing,Ground truth,Malaria,Artificial intelligence,Image database,Pattern matching |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cecilia Di Ruberto | 1 | 183 | 21.39 |
| Andrea Loddo | 2 | 12 | 5.43 |