DocumentCode :
2204120
Title :
Histology Image Classification Using Supervised Classification and Multimodal Fusion
Author :
Meng, Tao ; Lin, Lin ; Shyu, Mei-Ling ; Chen, Shu-Ching
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Miami, Coral Gables, FL, USA
fYear :
2010
fDate :
13-15 Dec. 2010
Firstpage :
145
Lastpage :
152
Abstract :
The fast development of microscopy imaging techniques nowadays promotes the generation of a large amount of data. These data are very crucial not only for theoretical biomedical research but also for clinical usage. In order to decrease the inter-intra observer variability and save the human effort on labeling and classifying these images, a lot of research efforts have been devoted to the development of algorithms for biomedical images. Among such efforts, histology image classification is one of the most important areas due to its broad applications in pathological diagnosis such as cancer diagnosis. To improve classification accuracy, most of the previous work focuses on extracting more features and building algorithms for a specific task. This paper proposes a framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification. In the proposed framework, a cell image is first divided into 25 blocks to reduce the spatial complexity of computation, and one C-RSPM model is built on each block set which contains blocks in the same location from different images. For each testing image, our proposed framework first classifies each of its blocks using the C-RSPM classification model built for that block set, and then applies a multimodal late fusion algorithm with a weighted majority voting strategy to decide the final class label of the whole image. Experimenting using three-fold cross validation with three benchmark histology data sets shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.
Keywords :
biological tissues; feature extraction; image classification; image fusion; medical image processing; probability; set theory; benchmark histology data sets; cell image; collateral representative subspace projection modeling; feature extraction; histology image classification; inter-intra observer variability; microscopy imaging techniques; multimodal fusion; multimodal late fusion algorithm; pathological diagnosis; supervised classification model; testing image; three-fold cross validation; weighted majority voting strategy; C-RSPM; histology image classification; multimodal fusion; weighted majority voting algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia (ISM), 2010 IEEE International Symposium on
Conference_Location :
Taichung
Print_ISBN :
978-1-4244-8672-4
Electronic_ISBN :
978-0-7695-4217-1
Type :
conf
DOI :
10.1109/ISM.2010.29
Filename :
5693834
Link To Document :
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