Morphological Detection And Application Of Atypical Glandular Cell

This paper takes atypical glandular cell(AGC)of the cervical glandular cell type as the main research object,the main purpose of this paper is to detect and locate the atypical glandular cell rapidly and accurately in cancerous samples.The emphasis of this paper is analyze the physical morphology of AGC.Contrasting the vision disparity between AGC and cervical cells and draw the corresponding visual distinction.Select the appropriate image feature to construct the Rough Set Theory Model to detect AGC.Taking into account the complexity and diversity of cervical samples ensure a set of AGC detection process was designed by using decision tree theory,the AGC can achieve rapid detection and identification in the actual detection.(1)Contrasting the physical morphological difference between AGC and cervical cells from a straightforward perspective.Converting the significant difference in contrast vision disparity to the corresponding image disparity based on morphology.Collecting a large number of AGC for experiments and getting the distribution of feature data.Analyze the selected image features to determine whether it has articulable or consistency.According to these image features to construct the Rough Set Theory Model.(2)Validate the accuracy and effectiveness of Rough Set Theory Model.Treat cells of cervical samples as the detection object for testing by the Rough Set Theory Model.Analysis the error rate and false detection rate when using the Rough Set Theory Model of detecting the AGC,and optimize the Rough Set Theory Model to improve the detection accuracy.(3)The AGC has two cellular morphology:clusters and individual particles.The characteristic of AGC is that there are some increased abnormal particles at the edge,calculating the abnormal particles scattered in the sample and according the visual characteristics of abnormal particles to construct the corresponding Rough Set Theory Model.Combining the Cluster Rough Set Theory Model and the scattered abnormal particles Rough Set Theory Model can improve the efficiency of detection and accuracy of AGC.(4)There are a large number of cells in the cervical sample.Which will greatly affect the time and using Rough Set Theory Model testing for each cell efficiency.Based on the decision tree theory,designing a complete set of AGC detection process,which can improve the detection of actual efficiency and also ensure the accuracy of detection.