Wavelet statistical texture features‐based segmentation and classification of brain computed tomography images. Issue 1 (1st February 2013)

• Record Type: Journal Article
• Authors: Padma Nanthagopal, A.; Sukanesh, R.
• Is Part Of: IET image processing. Volume 7:Issue 1(2013)
• Page Start: 25
• Page End: 32
• Publication Date: 2013-02-01
• Subjects: brain -- computerised tomography -- discrete wavelet transforms -- feature extraction -- image classification -- image denoising -- image segmentation -- image texture -- medical image processing -- neural nets -- statistical testing -- support vector machines -- tumours; segmentation error -- segmentation accuracy -- k‐fold cross‐validation method -- classification accuracy -- PNN classifier -- probabilistic neural network -- SVM classifier -- student t‐test -- feature extraction -- noise removal -- tumour image -- two‐dimensional discrete wavelet decomposition -- support vector machine -- wavelet approximation tumour region -- malignant tumour slice classification -- malignant tumour slice segmentation -- benign tumour slices classification -- benign tumour slices segmentation -- computer software system -- brain computed tomography image -- wavelet statistical texture features‐based classification -- wavelet statistical texture features‐based segmentation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2012.0073
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : A computer software system is designed for segmentation and classification of benign and malignant tumour slices in brain computed tomography images. In this study, the authors present a method to select both dominant run length and co‐occurrence texture features of wavelet approximation tumour region of each slice to be segmented by a support vector machine (SVM). Two‐dimensional discrete wavelet decomposition is performed on the tumour image to remove the noise. The images considered for this study belong to 208 tumour slices. Seventeen features are extracted and six features are selected using Student's t‐test. This study constructed the SVM and probabilistic neural network (PNN) classifiers with the selected features. The classification accuracy of both classifiers are evaluated using the k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and segmentation error. The proposed system provides some newly found texture features have an important contribution in classifying tumour slices efficiently and accurately. The experimental results show that the proposed SVM classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.