Texture feature analysis for the liver cancer diseases using statistical based feature extraction technique
Gururaj L. Kulkarni, Sanjeev S. Sannakki, Vijay S. Rajpurohit · 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · 2020
Feature extraction is one of the important steps in image processing because the accuracy of the system would completely depend on this step. Human soft tissues are diagnosed by a diverse set of image scanning techniques namely Sonographer, Tomography and Magnetic resonance imaging (MRI). All these Imaging techniques are applied depending on the nature of the disease and type of organ. Texture analysis of the liver can be used for classifying the liver into normal and diseased. The texture is a fusion of repeated patterns that have regular or irregular frequency [5]. Texture visualization feature helps in the classification of the disease. Different textural analysis techniques have been developed for extracting texture features of the liver for classification such as structure base, statistical-based, model-based, transform-based and further classification is done by using single classifier or combination of classifiers. Here, the Microscopic images of the liver are used to classify it into the normal liver and diseased liver. The texture features are extracted using the 1st order and 2nd order feature extraction techniques. The texture analysis helps in classifying the liver cancer into benign or malignant.