Detection of Unruptured Aneurysms in Brain MRA Images Using Ring Type Gradient Concentration Filter and Texture Analysis

Abstract

Detection of intracranial unruptuered aneurysms is important because their rupture is a main course of subaracnoid hemorrhage. The purpose of this study is to develop a computer-aided diagnosis scheme for the detection of unruptured aneurysms in order to assist radiologists' image interpretation. The vessel regions were first segmented by using region growing technique for limiting the search areas of unruptured aneurysms. For determining the initial candidate regions of aneurysms, ring type gradient concentration filters were applied to the segmented regions. Fourteen threedimensional shape and texture features were obtained from the candidate regions. Rule-based schemes and random forest with these features were employed for distinguishing unruptured aneurysms and false positives（FPs）. Our proposed method was evaluated by using 25 cases. The sensitivity for the detection of unruptured aneurysms was 88.0% with 1.76 FPs per patient. Therefore, our proposed method would be useful for the detection of unruptured aneurysms in MRA images.