A Novel M-ACA-Based Tumor Segmentation and DAPP Feature Extraction with PPCSO-PKC-Based MRI Classification

Abstract

In medical image processing, segmentation and classification plays a significant part in prediction of affected region from given input image. The novel segmentation and classification model to segment tumor region and to identify the abnormality category from MR brain image is projected. In preprocessing, image filtering by distribution-based adaptive median filtering technique to prove smoothness to the image and to eliminate the noise component is provided. Further the skull region is removed by using adaptive threshold-based edge detection with canny method. In the segmentation, a novel multiangle cellular automata model to predict the region of interest, i.e., tumor spot is discussed. The classification performance is improved by novel texture extraction and optimal feature selection method named as dynamic angle projection pattern and priority particle cuckoo search optimization, respectively. These optimized features are given to support vector machine (SVM) and pointing kernel classifier (PKC) to classify the abnormality level of segmented image. This work can be compared with existing systems for the parameters of sensitivity, specificity, accuracy, FPR, TPR, and ROC. Our proposed classifier PKC’s performance is good when compared with other technique. The sensitivity, specificity, and accuracy of 85.9155, 94.3396, 89.5161, and 95.7746%, 100, and 97.5806% are obtained in SVM and PKC, respectively. SVMs are linear up to 0.8 for higher values of FPR and reaches the stable point prior to the maximum value. But, the stable operation is achieved in proposed PKC at 0.9. Result shows that the improvement in performance of proposed PKC.