Title :
Diagnosis Technology Research of Mammographic Masses in Content-Based Image Retrieval
Author :
Song Li-xin ; Wang Qing-yan ; Wang Li
Author_Institution :
Coll. of Electr. & Electron. Eng., Harbin Univ. of Sci. & Technol., Harbin, China
Abstract :
In order to assist doctor to diagnosis of mammo-graphic masses, a method is proposed. 22 features are extracted from each queried region of interest (ROI).A k-nearest neighbor (KNN) algorithm is used to retrieve similar images from database, and further calculate the mutual information (MI) between the queried image and the images which are in the retrieval results, so as to improve the retrieval performance. Finally, the scheme takes the first nine images with the highest MI scores as the final retrieval results. With the purpose of providing available decision-making information of diagnostic aids, we compare and analyze three calculating methods of decision index. The Experiment results show that the method is better than method of using KNN only, and improve the accuracy of diagnosis effectively.
Keywords :
cancer; content-based retrieval; feature extraction; image retrieval; mammography; medical image processing; KNN algorithm; content-based image retrieval; decision-making information; feature extraction; k-nearest neighbor algorithm; mammographic masses; mutual information; region of interest; Content based retrieval; Data mining; Decision making; Feature extraction; Image databases; Image retrieval; Information analysis; Information retrieval; Mutual information; Spatial databases;
Conference_Titel :
Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4712-1
Electronic_ISBN :
2151-7614
DOI :
10.1109/ICBBE.2010.5517464