Automated and unsupervised detection of malarial parasites in microscopic images.

ABSTRACT:

Background

Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria.

Method

A met

SUBMITTER: Purwar Y 

PROVIDER: S-EPMC3254597 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature