Semi Automated Tumor Segmentation from MRI Images Using Local Statistics Based Adaptive Region Growing
Keywords:
Adaptive region growing, MRI, pixel run length, region growing, tumorAbstract
Segmentation of anatomical regions of a brain is
the fundamental problem in pattern recognition in medical images. It is very challenging due to ambiguity in understanding tumor boundary. Lots of work has been reported, showing various level of accuracy in segmenting the boundary of an anatomy or tumor. The motivation for our work came from the fact of accurate delineation of the contour of a tumor from Magnetic Resonance Images (MRI) with high level of precision.
We have developed an algorithm by modifying the existing Region Growing (RG) algorithm, by considering the local statistics of the pixels along with Pixel Run Length (PRL) parameter. PRL based Adaptive Region Growing (ARG) algorithm gave satisfactory result with good level of accuracy.The segmented tumor is quantified by area, perimeter and form factor, which in turn helps us to classify the different shape and contour of tumor. This algorithm is a semi - automated method
and it will help the radiologist and neurologist to perform the diagnosis more effectively and accurately.
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