An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation
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An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation D. Maruthi Kumar 1 & D. Satyanarayana 2 & M. N. Giri Prasad 1 Received: 14 August 2019 / Revised: 8 July 2020 / Accepted: 13 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Image processing is significant in the medical field which provides detailed information about medical images and image segmentation is an essential part of medical image processing. In the medical field, various modalities have been utilized such as X-ray, CT scan and MRI, etc. MRI provides accurate results than other techniques. Our proposed technique is highly focused on tumor identification using MRI image segmentation. The proposed methodology consists of five stages namely, pre-processing, feature extraction, feature selection, classification, and segmentation. Initially, input MRI images are given to the preprocessing stage to fit the images for further processing. In this preprocessing phase, the input images are converted into a transform domain with the aid of Improved Gabor Wavelet Transform (IGWT). Then, GLCM related features are extracted and important features are selected with the help of the Oppositional fruit fly algorithm (OFFA). Then, the selected features are given to the support vector machine (SVM) classifier to classify an image as normal or abnormal. After the classification process, the abnormal images are selected and given to the segmentation process. For segmentation, in this paper, we utilized an effective rough k-means algorithm. The performance of the proposed methodology is evaluated in terms of Sensitivity, Specificity, and Accuracy. The experimental results show that our proposed method attained better results compared to existing work. Keywords Image segmentation . Preprocessing . Feature extraction . Gabor wavelet transform . Oppositional fruit fly algorithm . Rough K-means
* D. Maruthi Kumar [email protected]
1
Department of ECE, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India
2
Department of ECE, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh, India
Multimedia Tools and Applications
1 Introduction Image processing and its segmentation is one of the interesting areas of medical science. In medical image technology, both MRI and CT scan (Computerized Tomography) used to create the pictures of the inside of the body, from that MRI provides accurate visualization of anatomical structures of tissues. When compared to a CT scan, MRI is better because it does not affect the human body [29]. The human body is made up of several types of cells. The brain is a highly specialized and sensitive organ of the human body. The brain tumor is a very harmful disease for human beings [5].In medical science, MRI (magnetic resonance imaging) is a tool that can produce detailed pictures of parts of the body and also to detect the Brian tumor and its segmentation from an image [8]
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