Spondylosis Detection And Classification Of Cervical Images Using ATMFCMC Based Medical Image Segmentation Methods

Abstract

In this work we are trying to implement the segmentation of an x-ray or CT-Scan images for cervical spondylosis detection. This work is used for implementing the detection of cervical spondylosis of x-ray images with machine learning algorithm. The task is based on earlier detection of diseases in the bones of cervical images and then classify using FCM classifier for finding the accuracy parameter. Here the datasets as cervical images are used for this medical imaging related study. This is become one of the major diseases throughout all over the world. Here the noise addition and filtering process is done using median filtering and segmentation is done using thresholding, morphological, FCM based segmentation approach also extraction of features are done using Kirsch's template and morphological closing. Here the FCM approach is used finding a lot more regions of cluster. Here the performance produces by adaptive method is more than 70% of accuracy for detection and classification of diseases from Cervical Images. Also the method provides less execution time period for identification of disease accurately.

Keywords: Cervical Spondylosis, FCM, Image Classification, Filtering, Morphological Operation, Accuracy, Median Filter,CT Scan, Specificity