Automatic identification of Head and Neck Swellings in MRI images using Support Vector Machines based on cepstral analysis

Swellings identification from Head and Neck computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in the appearance of tumor tissues among different patients. In many cases, the task of identifying the location of the tumor in Head and Neck is very complicated. The abnormal tissues have to be removed without causing any lacerations in the adjacent structures during entry or exit from the area subject to the tumor removal operation. This paper deals with a new identification technique for extracting the brain tumors in computed tomography images using Support Vector Machine (SVM). In the training phase, the SVM is used as an identifier to identify the tumor from the brain MRI images. The 2-D images are lexicographic ordered to 1-D signals, and then the Mel Frequency Cepstral Coefficients (MFCCs) are extracted from these 1-D signals or from their transforms. The SVM is then used to match the extracted features in the testing phase to those of the training phase. The method is applied to a set of real data of 20 MRI images with normal and abnormal tumors. Experimental results show that the recognition rate for features extracted from the Discrete Sine Transform (DST) of images contaminated by Additive White Gaussian Noise (AWGN), features extracted from images plus the Discrete Cosine Transform (DCT) of images contaminated by impulsive noise, and features extracted from images plus the DST of images contaminated by speckle noise achieve better performance compared to the existing techniques.