Seungwook Yang1, Eung Yeop Kim2, Min-Oh Kim1, Yoonho Nam1, Jaeseok Park3, Dong-Hyun Kim1
1Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seoul, Korea, Republic of; 2Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea, Republic of; 3Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea, Republic of
The purpose of this study was to develop a computer-aided detection (CAD) system for brain metastases detection from magnetic resonance (MR) black blood images and to assess the applicability of MR black-blood imaging to CAD. Twenty-six patients with brain metastases of various sizes were imaged with a contrast-enhanced, three dimensional, whole brain MR black blood pulse sequence. The CAD system uses 3D template matching which measures normalized cross-correlation coefficient (NCCC) to generate possible metastases candidates from the patient data. Various image features were extracted from each candidate, then principal component analysis (PCA) was performed to determine dominant features. Artificial neural network (ANN) training and testing scheme were incorporated for classification.