Retrieval and classification of pneumoconiosis chest radiograph images using multiscale AM-FM methods

Murray, Víctor; Pattichis, Marios S.; Solíz, Peter

doi:10.1109/acssc.2009.5470191

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Based on conventional image retrieval methods, Mira et al proposed an approach which takes eigen-value of each ROI on lung field image with energy spectrum and Quasi-Gabor filter, in order to fill the holes caused by segmentation and quantize the abnormal tissue [3]. Victor Murray et al proposed a multi-resolution frequency and scaling method to represent features of image, this method is driven by harmonizing the frequency of consine function and changes of images in gray scales [4]. The methods mentioned above extract the features with high dimension, high redundancy as well as correlation between features, high computational complexity and low recognition rate [5].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction of X-ray chest image based on KPCA

Cui

Chen

et al. 2012

Proceedings of 2012 2nd International Conference on Computer Science and Network Technology

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In view of the nonlinear image information loss and lack of characteristics which is caused by principal component analysis in the feature extraction process, an X-ray chest image feature extraction method based on KPCA is proposed. Original feature space is mapped by kernel function to a new space where dimension reduction is implemented and features are extracted, and then nonlinear information is converted to linear information in the feature space. This method reduces feature dimension considerably while it maintains adequate original Xray chest image information. Experimental results show that this method can enhance retrieval accuracy and has better performance than principal component analysis.

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Section: Introductionmentioning

confidence: 99%