An Efficient Logical Average Distance Measure Algorithm (LADMA) to Analyse MRI Brain Images

Abstract

Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. Some of such data mining technique is k means and fuzzy C means algorithms. This work proposes a new hybrid algorithm namely LAMDA, which offers successful identification of tumor and perform well for the segmentation of tissue regions in brain. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. Experimental results show that the proposed approach reduces the number of features and at the same time it achieves high accuracy level. The observed results to achieve high accuracy level using minimum number of selected features.