Extraction of Texture Features and Classification of Renal Masses from Kidney Images

  • Authors

    • Hima Bindu G
    • Prasad Reddy Pvgd
    • Ramakrishna Murty M
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.17907
  • Classification, DWT transform, Feature Extraction, First-order statistics(FOS), Gray level co-occurrence matrix, K Nearest Neighbors, PCA, Second-order Statistics(SOS), Support Vector Machine, Texture Analysis

  • In this work, texture based feature analysis is done on renal masses obtained from kidney CT scan images to determine the type of kidney abnormality. From segmented renal masses, two sets of features are extracted. One set is extracted using first-order statistics(FOS) and second-order statistics(SOS). The second set is extracted using Discrete Wavelet Transformation (DWT). PCA is used as dimensionality reduction technique on DWT feature vector. The classification techniques, SVM Support Vector Machine and K Nearest Neighbors with different parameters (sub classifiers) are used individually on both the features sets. Classification models are trained on training datasets and the models are used for classification of renal masses from CT images. Performance of both the features sets and the classifiers on the CT kidney images are evaluated. It is observed that texture features extracted using FOS and SOS on the image produced better results than wavelet features and Cubic SVM sub classifier produced higher classification accuracy amongst all sub classifiers of SVM and KNN.

     

     

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    Bindu G, H., Reddy Pvgd, P., & Murty M, R. (2018). Extraction of Texture Features and Classification of Renal Masses from Kidney Images. International Journal of Engineering & Technology, 7(2.33), 1057-1063. https://doi.org/10.14419/ijet.v7i2.33.17907