Kayaaltı, ÖmerAksebzeci, Bekir HakanKarahan, Ökkeş IbrahimDeniz, KemalÖztürk, MenmetYilmaz, BulentAsyali, Musa Hakan2024-07-022024-07-02201220129781467308786https://doi.org/10.1109/HIBIT.2012.6209041https://hdl.handle.net/20.500.12573/2233Middle East Technical University (METU); Inst. Electr. Electron. Eng. (IEEE) Eng. Med. Biol. Soc. (EMBS); TUBITAK; British Council; AKGUN YazilimEven though liver biopsy is critical for evaluating chronic hepatitis and fibrosis, it is an invasive, costly, and difficult to standardize approach. The developments in medical image processing and artificial intelligence methods have advanced the potential of using computer-aided diagnosis techniques in the classification of liver tissues. The aim of this study was to develop a non-invasive, cost-effective, and fast approach to specify fibrosis stage using the texture properties of computed tomography images of liver. Gray level co-occurrence matrix, discrete wavelet transform, and discrete Fourier transform were the image analysis tools in the feature extraction phase. Following dimension reduction of the texture features support vector machines and k-nearest neighbor methods were used in the classification phase of this study. Our results showed that our approach is feasible in fibrosis staging especially in pairwise stage comparisons with success rate of approximately 90%. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessArtificial Intelligence MethodsChronic HepatitisComputed Tomography ImagesCt ImageDimension ReductionGray Level Co-Occurrence MatrixImage Analysis ToolsK-Nearest Neighbor MethodLiver FibrosisLiver TissueTexture FeaturesTexture PropertiesArtificial IntelligenceBioinformaticsComputer Aided DiagnosisDiscrete Fourier TransformsDiscrete Wavelet TransformsFeature ExtractionImage TextureMammographyTexturesTissueComputerized TomographyConference Object10.1109/HIBIT.2012.62090412-s2.0-84862740327