Microscopic Image Segementing and Classification with RBF Neural Network

Abstract

Segmenting and interesting objects from microscopic images and classifying microscopic images are very important for biomedical researching work, which help diagnosis and further biomedical research. However, conventional approaches don't behavior as well as expected when they are applied to solve the problem. We hence propose two methods, radial basis function neural network with fuzzy initialization and graph-based discrete approach, for microscopic image segmenting and classification. The results show that RBF neural network has advantages such as easy to configure and implement, and the training procedure being very fast. In addition, RBF neural network employs fuzzy mean algorithm to accelerate the procedure of parameters and structure selection. Meanwhile, graphed-based discrete approach, which depends on the general formulation of discrete functional regularization on weighted graph, can be used to address cellular extraction segmentation problem.

A Novel Medical Image Segmentation and Classification Using Combined Feature Set and Decision Tree Classifier

“…Hybrid Extreme Rotation Forest classifier is developed for segmenting 3D CTA images which is a group of classifier composing of extreme learning machine and decision trees [4].Biomedical research has been extended by segmenting the interested objects from the microscopic images and classifying them. It is achieved using RBF network combined with fuzzy and graph based discrete approach [5]. With the rapid increase in the number of images that are stored in the database there is a problem with image annotation which cannot be manually done.…”