Automated Diagnosis System for Malaria Detection and Analysis

Image processing and image analysis is a core area of application in computer science. Several

research work has contributed and consolidated this domain of research. With the progression

and advancement of digital imaging, contribution towards meaningful assessment of images

have gained importance. An image captured by some imaging modalities needs to be

understood and deciphered to gain insight on the image/s and hence extract pertinent

information from the captured image/s.

Microscopic examination of cellular and histological samples are widely used as a basis for

disease detection. However, with the introduction of advanced digital microscope and high

resolution scanners the approach towards pathology had a paradigm shift towards 'virtual

microscopy' as an innovation in diagnostic workflow. Handling of glass slides across the labs

is cumbersome and susceptible to loss of the slide or decreased quality of the specimen. The

associated turnaround time (from the sample collection to report generation) is time consuming.

With the increase in reliability of digital equipment like digital imaging technologies, computer

hardware and software, there has been persistent acceptance of Digital Pathology in the medical

community.

Malaria is the oldest and cumulatively the deadliest of the human infectious diseases and is a

primary cause of child mortality. The disease is predominantly widespread in tropical climatic

regions that are backward and under-developed. Female Anopheles mosquito is the sole vector

for the protozoan infectious disease that affects human population. The disease attains

epidemic proportions in remote rural areas within a very short time. Prevention of the disease

by curtailing the breeding grounds of the vector has proved futile in most areas of the world.

The disease can only be managed with early detection, confirmation of species type, stage and

density of parasite within the human blood.