Analysis of Endoscopic Electronic Image of Intramucosal Gastric Carcinoma Using a Software Program for Calculating Hemoglobin Index

Hemoglobin is the predominent pigment in the gastrointestinal mucosa, and the development of electronic endoscopy has made it possible to quantitatively measure the mucosal hemoglobin volume, by using a hemoglobin index (IHb). The aims of this study were to make a software program to calculate the IHb and then to investigate whether the mucosal IHb determined from the electronic endoscopic data is a useful marker for evaluating the color of intramucosal gastric carcinoma with regard to its value for discriminating between the histologic types. We made a software program for calculating the IHb in the endoscopic images. By using this program, the mean values of the IHb for the carcinoma (IHb-C) and those of the IHb for the surrounding non-cancerous mucosa (IHb-N) were calculated in 75 intestinal-type and 34 diffuse-type intramucosal gastric carcinomas. We then analyzed the ratio of the IHb-C to the IHb-N (C/N ratio). The C/N ratio in the intestinal-type carcinoma group was higher than that in the diffuse-type carcinoma group (p<0.001). In the diffuse-type carcinoma group, the C/N ratio in the body was lower than that in the antrum (p=0.022). The accuracy rate, sensitivity, specificity, and the positive and negative predictive values for the differential diagnosis of the diffuse-type carcinoma from the intestinal-type carcinoma were 94.5%, 94.1%, 94.7%, 88.9% and 97.3%, respectively. IHb is useful for making quantitative measurement of the endoscopic color in the intramucosal gastric carcinoma, and the C/N ratio by using the IHb would be helpful for distinguishing the diffuse-type carcinoma from the intestinal-type carcinoma.

A study on Recognition of Bronchogenic Cancer Cell using Fuzzy Neural Networks / 대한의료정보학회지

A fuzzy neural network is an approach to mimic the structure and function of our brain. This method is widely applied in character recognition, but not in medical image recognition. The area of medical image recognition is challenging to our interest and can be maximally utilized the advantage of fuzzy neural networks in practice. In this paper we propose and new neural algorithm which is integrated both fuzzy self-organized and supervised learning methods. The proposed algorithm is applied for bronchogenic cancer diagnosis. The experimental results show that the correct recognition rate of our algorithm is superior to that of other neural networks.