PERBANDINGAN METODE SEGMENTASI MENGGUNAKAN ALGORITMA SEEDED REGION GROWING DAN K-MEANS CLUSTERING PADA CITRA PARASIT MALARIA

Malaria is an disease who cause by plasmodiun inside mosquito’s body.
Plasmaodium can stayed inside human body, especially on liver before its attack red
blood cell that transmitted by anopeles’s mosquitos. Malaria is a disease caused by
single cell’s parasite that is protozoa genius plasmodium. Human can be infected by
that single cell’s parasite through blood vessels. Based on WHO’s data, on 2016 there
are 212 million malaria cases in the world and there were about 429.000 has died.
Method that used by WHO should be qualify and should be tested on laboratory so they
have a result with high accuration and it checked by insightful medical staff. But, on
the process, it takes a lot of time to detect a symptom because they were doing it
manually. Although, there were a failed identification caused by human error. Based
on this problem, there must a research by computer to identify a disease on red blood
cell. On this research, identify malaria disease will using an Seeded Region and K –
Means Clustering Method. Result of this research will show that 2 method be able to
segment the image quite well from all three types of plasmodium images. From 90
image who segimented, the result from Seeded Region Growing got 58,88% accuration
and the average of time are about 45,6s, and for K-Means Clustering isterong method
got 87,77% acccuration and 97,74s for the time average.