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Content Provider | IET Digital Library |
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Author | Nanthagopal, A. Padma Sukanesh, R. |
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. |
Starting Page | 25 |
Ending Page | 32 |
Page Count | 8 |
ISSN | 17519659 |
Volume Number | 7 |
e-ISSN | 17519667 |
Issue Number | Issue 1, Feb (2013) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/7/1 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2012.0073 |
Journal | IET Image Processing |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Benign Tumour Slices Classification Benign Tumour Slices Segmentation Biology And Medical Computing Biomedical Imaging/measurement Brain Brain Computed Tomography Image Cancer Classification Accuracy Computer Software System Computer Vision And Image Processing Technique Computerised Tomography Discrete Wavelet Transform Feature Extraction Function Theory And Analysis Image Classification Image Denoising Image Segmentation Image Texture Integral Transforms K-fold Cross-validation Method Knowledge Engineering Technique Malignant Tumour Slice Classification Malignant Tumour Slice Segmentation Medical Image Processing Neural Computing Technique Neural Nets Noise Removal Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation PNN Classifier Probabilistic Neural Network Probability Probability Theory Radiography And Computed Tomography Segmentation Accuracy Segmentation Error Sensitivity Analysis Statistical Analysis Statistical Testing Statistics Stochastic Linearised SCUC Student T-test Support Vector Machine SVM Classifier Tumour Image Tumours Two-dimensional Discrete Wavelet Decomposition Wavelet Approximation Tumour Region Wavelet Statistical Texture Feature-based Classification Wavelet Statistical Texture Feature-based Segmentation X-Ray Technique X-Rays And Particle Beam |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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