An image analyzing system using an artificial neural network for evaluating the parenchymal echo pattern of cirrhotic liver and chronic hepatitis.
Fukuda H. Ebara M. Kobayashi A. Sugiura N. Yoshikawa M. Saisho H. Kondo F. Yoshino S. Yahagi T.
First Department of Medicine, School of Medicine, Chiba University, Japan. email@example.com
To objectively evaluate the parenchymal echo pattern of cirrhotic liver and chronic hepatitis, we applied an image analyzing system (IAS) using a neural network. Autopsy specimens in a water tank (n = 13) were used to examine the relationship between the diameter of the regenerative nodule and the coarse score (CS) calculated by IAS. CS was significantly correlated with the diameter of the regenerative nodule (p < 0.0001, r = 0.966). CS is considered to be useful for evaluating the coarseness of the parenchymal echo pattern.
An automatic diagnostic system for CT liver image classification.
Chen EL. Chung PC. Chen CL. Tsai HM. Chang CI.
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.
Computed tomography (CT) images have been widely used for liver disease diagnosis. Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, a CT liver image diagnostic classification system is presented which will automatically find, extract the CT liver boundary and further classify liver diseases. The system comprises a detect-before-extract (DBE) system which automatically finds the liver boundary and a neural network liver classifier which uses specially designed feature descriptors to distinguish normal liver, two types of liver tumors, hepatoma and hemageoma. The DBE system applies the concept of the normalized fractional Brownian motion model to find an initial liver boundary and then uses a deformable contour model to precisely delineate the liver boundary. The neural network is included to classify liver tumors into hepatoma and hemageoma. It is implemented by a modified probabilistic neural network (PNN) [MPNN] in conjunction with feature descriptors which are generated by fractal feature information and the gray-level co-occurrence matrix. The proposed system was evaluated by 30 liver cases and shown to be efficient and very effective.