Evaluation Of Mri Brain Segmentation
Imaging plays a central role in the diagnosis and treatment
planning of brain tumor. An accurate segmentation is
critical, especially when the tumor morphological changes
remain subtle, irregular and difficult to assess by clinical
examination. Traditionally, segmentation is performed
manually in clinical environment that is operator dependent
and very tedious and time consuming labor intensive work.
However, automated tumor segmentation in MRI brain
tumor poses many challenges with regard to characteristics
of an image. A comparison of three different semiautomated methods, viz., modified gradient magnitude
region growing technique (MGRRGT) , level set and a
marker controlled watershed method is undertaken here for
evaluating their relative performance in the segmentation of
tumor. A study on 9 samples using MGRRGT reveals that
all the errors are within 6 to 23% in comparison to other
two methods
planning of brain tumor. An accurate segmentation is
critical, especially when the tumor morphological changes
remain subtle, irregular and difficult to assess by clinical
examination. Traditionally, segmentation is performed
manually in clinical environment that is operator dependent
and very tedious and time consuming labor intensive work.
However, automated tumor segmentation in MRI brain
tumor poses many challenges with regard to characteristics
of an image. A comparison of three different semiautomated methods, viz., modified gradient magnitude
region growing technique (MGRRGT) , level set and a
marker controlled watershed method is undertaken here for
evaluating their relative performance in the segmentation of
tumor. A study on 9 samples using MGRRGT reveals that
all the errors are within 6 to 23% in comparison to other
two methods