Abstract-Liver diseases are life-threatening, it's important to detect it tumor in early stages. So, for tumor detection Segmentation of the liver is a first and significant stride. Segmentation of the liver is a yet difficult undertaking in view of its intra patient variability in intensity, shape and size of the liver. The aim of this paper is to assemble a wide assortment of techniques and used CT scan dataset information for liver segmentation that will provide a decent beginning to the new researcher. There are different strategies from basic to advance like thresholding, active contour, region growing to graph cut is briefly abridge to give an outline of existing segmentation strategies. We review the concept of particular strategies and review their original ideas. Our idea is to provide information under which condition a chosen strategy will work or utilize.

Human action recognition is a vibrant area of research with multiple application areas in human machine interface. In this work, we propose a human action recognition based on spatial graph kernels on 3D skeletal data. Spatial joint features are extracted using joint distances between human joint distributions in 3D space. A spatial graph is constructed using 3D points as vertices and the computed joint distances as edges for each action frame in the video sequence. Spatial graph kernels between the training set and testing set are constructed to extract similarity between the two action sets. Two spatial graph kernels are constructed with vertex and edge data represented by joint positions and joint distances. To test the proposed method, we use 4 publicly available 3D skeletal datasets from G3D, MSR Action 3D, UT Kinect and NTU RGB+D. The proposed spatial graph kernels result in better classification accuracies compared to the state of the art models.