The New Approach for Medical Enhancement in Texture Classification and Feature Extraction of Lung MRI Images by using Gabor Filter with Wavelet Transform

Magnetic resonance imaging is most widely used radiographic technique in diagnosis, clinical research and studies as well as treatment planning of major diseases. Generally GLCM and PCA technique are used for the feature extraction and classification but this technique increased the complexity and it’s robust in nature. In this paper we used the Gabor filter with wavelet transformation classification technique for analyzing Magnetic response images. In this paper, the detection of abnormality in the patient’s image is automatically recognized using texture analysis. The hybrid technique may work as an automatic classification of abnormalities in lungs MRI images, which can help radiologist in performing an in-depth examination. The texture analyses of both the normal and abnormal images are done. On the bases of the values of abnormal images, the range is calculated and further it is compared with the texture features of normal image. So, to determine the whether the abnormality is there or not in the image, its texture features are observed and the feature lying outside the range finally concludes that image is normal. Five cases are observed, on the bases of their comparison, the result is obtained at the end indicating the whether the presence of abnormality in the image.