Automated hemangioma detection using Otsu based binarized Kaze features

This study aims to detect liver hemangioma on CT images by using hybrid image processing methods as well as binarized histogram of gradients based Kaze feature extraction. Using hybrid strategies, automatic hemangioma detector design is the state of art of this study. Our study helps doctors to detect the solid liver masses. Proposed algorithm includes detection of hemangioma using Otsu auto-threshold based Histogram of Gradients (HOG) and Kaze feature extraction implementation. 48 liver CT images, 28 of which are hemangiomas and 20 of which are healthy liver images, are used as the dataset. CT images are obtained by the Department of the Radiology at Fırat University. Presented work was implemented to 48-CT images and 91,66% accuracy was achieved for different shaped and sized hemangiomas. These results show that the developed algorithm could ease the process of detecting liver masses for radiologist and doctors could evaluate their findings easily.