25 April 1997 Neural net classification of liver ultrasonogram for quantitative evaluation of diffuse liver disease
Author Affiliations +
Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997); doi: 10.1117/12.274078
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States
Abstract
There have been a number of studies on the quantitative evaluation of diffuse liver disease by using texture analysis technique. However, the previous studies have been focused on the classification between only normal and abnormal pattern based on textural properties, resulting in lack of clinically useful information about the progressive status of liver disease. Considering our collaborative research experience with clinical experts, we judged that not only texture information but also several shape properties are necessary in order to successfully classify between various states of disease with liver ultrasonogram. Nine image parameters were selected experimentally. One of these was texture parameter and others were shape parameters measured as length, area and curvature. We have developed a neural-net algorithm that classifies liver ultrasonogram into 9 categories of liver disease: 3 main category and 3 sub-steps for each. Nine parameters were collected semi- automatically from the user by using graphical user interface tool, and then processed to give a grade for each parameter. Classifying algorithm consists of two steps. At the first step, each parameter was graded into pre-defined levels using neural network. in the next step, neural network classifier determined disease status using graded nine parameters. We implemented a PC based computer-assist diagnosis workstation and installed it in radiology department of Seoul National University Hospital. Using this workstation we collected 662 cases during 6 months. Some of these were used for training and others were used for evaluating accuracy of the developed algorithm. As a conclusion, a liver ultrasonogram classifying algorithm was developed using both texture and shape parameters and neural network classifier. Preliminary results indicate that the proposed algorithm is useful for evaluation of diffuse liver disease.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dong Hyuk Lee, JongHyo Kim, Hee Chan Kim, Yong Woo Lee, Byong Goo Min, "Neural net classification of liver ultrasonogram for quantitative evaluation of diffuse liver disease", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274078; https://doi.org/10.1117/12.274078
PROCEEDINGS
6 PAGES


SHARE
KEYWORDS
Liver

Algorithm development

Neural networks

Evolutionary algorithms

Information operations

Radiology

Spleen

Back to Top