Texture Classification Using Curvelet Transform
S. Prabha, M. Sasikala · 2013
Abstrat-Brain tumors are due to abnormal growths of tissue in the brain. The most common group is gliomas, followed by meningiomas. Magnetic resonance imaging (MRI) is currently an indispensable diagnostic imaging technique for the early detection of any abnormal changes in tissues and organs. It possesses fairly good contrast resolution for different tissues. It is therefore widely used to provide images which distinguish brain tumours from normal tissues. Although MRI can clearly supply the location and size of tumours, it is unable to classify tumour types, determination of which usually requires a biopsy. However a biopsy is a painful process for patients, and in some cases such as brain stem gliomas, may be too hazardous. These limitations necessative development of new analysis techniques that will improve diagnostic ability. One promising technique is texture analysis, which characterizes tissues to determine changes in functional characteristics of organs at the onset of disease. In this work texture classification based on curvelet transform has been performed. A curvelet based texture feature set is extracted from the region of interest. Texture features set consists of entropy and energy. Fuzzy-c-means algorithm is used as a classifier to classify two sets of brain images, benign tumour and malignant tumour.