Automatic Detection of Polyp Using Hessian Filter and HOG Features

An endoscope is a medical instrument that acquires images inside the human body. This paper proposes a new approach for the automatic detection of polyp regions in an endoscope image using a Hessian Filter and machine learning approaches. The approach improves performance of automatic detection of polyp detection with higher accuracy. The approach uses HOG feature as a local feature since the polyp and non-polyp region often have similar color information. The approach also uses Real Adaboost and Random Forests as classifiers which works efficiently even when the dimension of feature vector becomes large. It is suggested that Hessian filter can contribute to reducing the computational time in comparison with the case when only HOG features are used to detect the polyp region. K-means++ is introduced to integrate the detection results in the classification. It is shown that polyp detection with high accuracy is performed in the computer experiments with endoscope images. (C) 2015 The Authors. Published by Elsevier B.V.