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HYBRID METHOD ARCHITECTURE DESIGN OF MRI BRAIN TUMORS IMAGE SEGMENTATION
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Image segmentation is still an important and interesting issue for researchers today, and one of MRI issues is image segmentation of brain tumors. MRI images
of brain tumors have quite high complexity both from variable size and structure, low
contrast, bad boundaries, and low homogeneity. In some cases noise appears like blur,
miss focus, and miss alignment of images. Manually image segmentation requires high
accuracy, since each pixel contains important information. In addition, it will potentially
lead to different interpretations of each expert. The tools that are used by experts to
read MRI images of the brain nowadays, can present images into several regions namely
sagittal, coronal, and cross sectional. The division into these three regions is actually
quite helpful during the diagnosis process, since the doctor can see the brain tissue in
the three regions, but the display cannot lead to an object that is found slightly different
from particular area or focus on one particular object or area that shows a difference
or abnormality. The expected outcome of this initial research is an architecture design
for segmenting MRI images of brain tumors by involving two algorithms, MDWT and
CNN. Combination both of MDWT and CNN provides new approaches in medical images
analysis including brain image tumor segmentation. Researchers use literature study and
laboratory experiment as the research methods.
Ridha Sefina Samosir - Personal Name
Edi Abdurachman - Personal Name
Ford Lumban Gaol - Personal Name
Boy Subirosa Sabarguna - Personal Name
Edi Abdurachman - Personal Name
Ford Lumban Gaol - Personal Name
Boy Subirosa Sabarguna - Personal Name
N 1881-803X
NONE
electronic file
English
ICIC Express Letters
2020
Japan
Jil.14,Terbitan 12, Hlm.1177-1184
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