Implementation of SSFCM in Cross Sectional Views of Paediatric Male and Female Brain MR Images for the Diagnosis of ADHD

Attention deficit hyperactivity disorder is a neuropsychiatric disorder. It t can be seen as a disorder of life time that occurs in preschool age and continue fully or partially throughout the adulthood. Diagnosis of this disorder is usually not done in early age and this lead to delay in diagnosis and treatment. There are certain differences in condition as well as factors of paediatric male and paediatric female ADHD. Only few researches have done to point out the paediatric gender differences. So it becomes necessary for a good method to diagnose the ADHD in early years for overcoming the negative effects of this disorder. The foremost important part in the diagnosis of ADHD is to find out the exact area of the grey matter (gm), white matter (wm) and cerebrospinal fluid (csf). In this proposed paper, the differences between paediatric male and paediatric female taking cross sectional views of MR images are analyzed in detail. A novel hybrid technique, called Super Spectral Fuzzy C-Means (SSFCM) is proposed. In this new method, the optimal fuzzy is used for clustering and spectral segmentation is done. For the classification purpose, the Artificial Neural Network (ANN) technique is used. The experiment is performed on the cross sectional views of paediatric male and female brain MR images, collected from various Imaging centers. The proposed method is compared with existing techniques and results showed that this new one is accurate and faster than existing methods.