Detecting and diagnosing brain tumour types quickly and accurately is essential to any effective treatment. The general brain tumour diagnosis procedure, biopsy, not only causes a great deal of pain to the patient but also raises operational difficulty to the clinician. In this thesis, a non-invasive brain tumour diagnosis system based on MR images is proposed. The first part is image preprocessing applied to original MR images from the hospital. Non-uniformed intensity scales of MR images are standardized relying on their statistic characteristics without requiring prior or post templates. It is followed by a non-brain region removal process using morphologic operations and a contrast enhancement between white matter and grey matter by means of histogram equalization. The second part is image segmentation applied to preprocessed MR images. A new image segmentation algorithm named IFCM is developed based on the traditional FCM algorithm. Neighbourhood attractions considered in IFCM enable this new algorithm insensitive to noise, while a neural network model is designed to determine optimized degrees of attractions. This extension can also estimate inhomogenities. Brain tissue intensities are acquired from segmentation. The final part of the system is brain tumour classification. It extracts hidden diagnosis information from brain tissue intensities using a fuzzy logic based GP algorithm. This novel method imports a fuzzy membership to implement a multi-class classification directly without converting it into several binary classification problems as with most other methods. Two fitness functions are defined to describe the features of medical data precisely. The superiority of image analysis methods in each part was demonstrated on synthetic images and real MR images. Classification rules of three types and two grades of brain tumours were discovered. The final diagnosis accuracy was very promising. The feasibility and capability of the non-invasive diagnosis system were testified comprehensively.