Segmentation of tumor using PCA based modified fuzzy C means algorithms on MR brain images

Abstract

In the field of medical sciences, automatic detection of tumor using magnetic resonance (MR) brain images is a major research area. The goal of the proposed work is to identify the tumors in MR images using segmentation methods and to locate the affected regions of the brain more accurately. Medical images have vast information but they are difficult to examine with lesser computational time. An innovative process is proposed to extract tumor cells using the discrete wavelet transform (DWT). After extracting features with DWT feature reduction is carried out with the principal component analysis (PCA). Modified fuzzy C means (MFCM) technique is used for segmenting the tumor cells. The efficiency of the proposed method to identify different abnormalities in real MR images for intracranial neoplasm detection, tuberculoma, and bilateral thalamic fungal granulomas identification is tested. The results obtained are shown in‐terms of Accuracy, Dice Similarity Index (DSI), and Jaccard Index (JI) measures. The performance of the proposed method is tested in terms of performance measures like Accuracy, DSI, and JI. These results are compared with the conventional fuzzy C means (FCM) method.

Comparison of Fuzzy Subtractive Clustering and Fuzzyc-Means

“…A few research related with FCM had been analyzed by Zhang et al [9] concerning in image clustering and Qiao & Yang [10] related to FCM application in solving the optimal scale function problem by combining dolphin swarm algorithm. Another research in medical sector also done by Yepuganti, Saladi, and Narasimhulu [11] to do a segmentation of tumor disease through FCM application. The data applied in that research are the magnetic resonance (MR) of brain image data which transformed by applying wavelet discreet transformation and feature reduction.…”