tailieunhanh - Liver intensity determination in the 3d abdominal MR image using neural network

This study presents an approach to automatically identify the liver range intensity in the 3D abdominal MR images using neural network. The proposed scheme consists of three main stages. First, the T1-weighted MR images of the liver in the portal-venous phase are reduced noise by applying the anisotropic diffusion algorithm. | Journal of Science and Technology 54 (3A) (2016) 98-105 LIVER INTENSITY DETERMINATION IN THE 3D ABDOMINAL MR IMAGE USING NEURAL NETWORK Le Trong Ngoc1, 2, Kieu Duc Huynh1, Pham The Bao2, Huynh Trung Hieu1, * 1 Industrial University of Ho Chi Minh City, 12 Nguyen Van Bao, Go Vap, Ho Chi Minh city, Vietnam 2 University of Science, Vietnam National University Ho Chi Minh City, 227 Nguyen Van Cu, Ho Chi Minh city, Vietnam Email: hthieu@ Received: 15 June 2016; Accepted for publication: 27 July 2016 ABSTRACT This study presents an approach to automatically identify the liver range intensity in the 3D abdominal MR images using neural network. The proposed scheme consists of three main stages. First, the T1-weighted MR images of the liver in the portal-venous phase are reduced noise by applying the anisotropic diffusion algorithm. The histogram of the 3D reduced image is determined. The function approximation is applied to the computed histogram by using the neural network. The peaks are computed and the peak corresponding to the liver region is determined. This peak plays an important role for a fully automatic liver segmentation. The another salient point of this proposed approach is that the neural network is trained by an effective algorithm called extreme learning machine, this algorithm can offer a good performance with high learning speed in many applications. Keywords: liver segmentation, MR image, neural network, regression problem. 1. INTRODUCTION Liver segmentation from Computerized Tomography (CT) or Magnetic Resonance Imaging (MRI) is very important to accurately evaluate patient-specific liver anatomy for hepatic disease diagnosis, function assessment and treatment decision-making. The manual liver segmentation task is not only time consuming and tedious due to the high number of slices, but also depends on skill and experience. Several approaches have been proposed for liver segmentation on CT images including image-processing techniques [1 - .