Every day over 100 Person will be diagnosed with a primary brain tumor and
many more will be diagnosed with a cancer. Medical image processing is the
most defy and invention field specially MRI imaging technique that scans
and capture the internal structure of human brain. where specific type
diagnoses of brain tumor can be a complicated affair, making confirmation of
its diagnosis essential. In this paper, initially, brain images are filtered to
remove unwanted particles, then a new method for automatic segmentation
of lesion area is carried out based on mean and standard deviation.
Combining both solidity property and morphological operation to detection
only tumor from segmented image. Mathematical morphology such as close
used to join narrow breaks regions in an object, fill the small holes and
remove small objects. Employed wavelet transform to extract features from
images, followed by applying principle component analysis (PCA) to reduce
the dimensions of features. Extraction 13 feature of statistical and textural
features to use them as input to the Artificial Neural Network (ANN) for
classification. The algorithm is trained with 20 of brain MRI images and
tested with 45 brain MRI images. Accuracy for this method was encourage
and reach nearly 100% in identifying normal and abnormal tissues from brain
MRI images. The proposed algorithm compared with other simila works and
their results was better.
many more will be diagnosed with a cancer. Medical image processing is the
most defy and invention field specially MRI imaging technique that scans
and capture the internal structure of human brain. where specific type
diagnoses of brain tumor can be a complicated affair, making confirmation of
its diagnosis essential. In this paper, initially, brain images are filtered to
remove unwanted particles, then a new method for automatic segmentation
of lesion area is carried out based on mean and standard deviation.
Combining both solidity property and morphological operation to detection
only tumor from segmented image. Mathematical morphology such as close
used to join narrow breaks regions in an object, fill the small holes and
remove small objects. Employed wavelet transform to extract features from
images, followed by applying principle component analysis (PCA) to reduce
the dimensions of features. Extraction 13 feature of statistical and textural
features to use them as input to the Artificial Neural Network (ANN) for
classification. The algorithm is trained with 20 of brain MRI images and
tested with 45 brain MRI images. Accuracy for this method was encourage
and reach nearly 100% in identifying normal and abnormal tissues from brain
MRI images. The proposed algorithm compared with other simila works and
their results was better.
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