18-02-2016, 02:26 PM
mri brain tumor segmentation matlab code
Abstract
Detection, diagnosis and evaluation of Brain tumour is an important task in recent days. MRI is the current technology which enables the detection, diagnosis and evaluation. The medical problems are severe if tumour is detected at the later stage. Hence diagnosis is necessary at the earliest. In this work, pulse coupled neural network is applied for enhancing the MR Images. The enhanced images are
segmented and classified using back propagation networks. The Classification involves labelling the images into normal and abnormal (tumor detected). If the input MRI brain images are more in number, the physician could seek the help of this model and the network would help the physician to save time for further analysis. PCNN and BPN are less complex in nature and hence the processing of MRI brain
images is very simple. The term ‘abnormal’ indicates the presence of tumour. The tumour may be benign or malignant and it needs medical support for further classification.
Abstract
Detection, diagnosis and evaluation of Brain tumour is an important task in recent days. MRI is the current technology which enables the detection, diagnosis and evaluation. The medical problems are severe if tumour is detected at the later stage. Hence diagnosis is necessary at the earliest. In this work, pulse coupled neural network is applied for enhancing the MR Images. The enhanced images are
segmented and classified using back propagation networks. The Classification involves labelling the images into normal and abnormal (tumor detected). If the input MRI brain images are more in number, the physician could seek the help of this model and the network would help the physician to save time for further analysis. PCNN and BPN are less complex in nature and hence the processing of MRI brain
images is very simple. The term ‘abnormal’ indicates the presence of tumour. The tumour may be benign or malignant and it needs medical support for further classification.
