Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network

Authors:
Nian Cai;Kuanghu Hu;Haitao Xiong;Shuyu Li;Wanfang Su;Fengsui Zhu
Affiliations:
Lab of Pattern Recognition, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, PR China;Lab of Pattern Recognition, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, PR China;Lab of Pattern Recognition, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, PR China;Lab of Pattern Recognition, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, PR China;Lab of Pattern Recognition, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, PR China;Institute of Crop Breeding and Cultivation, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
Venue:
Pattern Recognition Letters
Year:
2004

Citing 6
Cited 1

Intelligent splitting in the chromosome domain

Pattern Recognition
Maximum likelihood neural networks for sensor fusion and adaptive classification

Neural Networks
Original Contribution: Model-based neural networks

Neural Networks
Geometric Separation of Partially Overlapping Nonrigid Objects Applied to Automatic Chromosome Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Entropy Estimation for Segmentation of Multi-Spectral Chromosome Images

SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Model-based neural network for target detection in SAR images

IEEE Transactions on Image Processing

MRF-MBNN: a novel neural network architecture for image processing

ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II

Quantified Score

Hi-index	0.10

Visualization

Abstract

Successful segmentation of chromosomes is important for their analysis. Isolation of chromosomes is the major topic of the segmentation in previous studies, but it is at low resolution. While the model-based neural network (MBNN), in this paper, is adopted to segment the G bands of Triticum monococcum chromosomes, which is a high-resolution approach. Combined with the MBNN as a core technique, various auxiliary techniques have been used to find out the optimal method. A series of experimental results indicate that the optimal method for segmentation of G bands, here termed as MBNN-3P, is that using the MBNN as a core technique simultaneously aided by all of the three auxiliary techniques. They are presegmenting by the threshold T = 173, preassigning a class number and providing teacher's information. Therefore, it is feasible to apply the MBNN-3P to segment the G band images of T. monococcum chromosomes. This study might be of great significance to improve the accuracy and speed of automated analysis of plant chromosomes.