پروژه طبقه بندی تصویر MRI با آنتروپی موجک براساس شبکه عصبی احتمالی PNN توسط MATLAB
Magnetic resonance imaging (MRI) is a non-invasive diagnostic tool very frequently used for brain imaging. The classification of MRI images of normal and pathological brain conditions pose a challenge from technological and clinical point of view, since MR imaging focuses on soft tissue anatomy and generates a large information set and these can act as a mirror reflecting the conditions of the brain. MRI scanners use magnetic fields and radio waves to form images of the body. The technique is widely used in hospitals for medical diagnosis, staging of disease and follow-up without exposure to ionizing radiation. A new approach by integrating wavelet entropy based spider web plots and probabilistic neural network is proposed for the classification of MRI brain images. The spider web plot is a geometric construction drawn using the entropy of the wavelet approximation components and the areas calculated are used as feature set for classification. Probabilistic neural network provides a general solution to the pattern classification problems and the classification accuracy is found to be 100%.