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Proceedings Paper

A new approach of objective quality evaluation on JPEG2000 lossy-compressed lung cancer CT images
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Paper Abstract

Image compression has been used to increase the communication efficiency and storage capacity. JPEG 2000 compression, based on the wavelet transformation, has its advantages comparing to other compression methods, such as ROI coding, error resilience, adaptive binary arithmetic coding and embedded bit-stream. However it is still difficult to find an objective method to evaluate the image quality of lossy-compressed medical images so far. In this paper, we present an approach to evaluate the image quality by using a computer aided diagnosis (CAD) system. We selected 77 cases of CT images, bearing benign and malignant lung nodules with confirmed pathology, from our clinical Picture Archiving and Communication System (PACS). We have developed a prototype of CAD system to classify these images into benign ones and malignant ones, the performance of which was evaluated by the receiver operator characteristics (ROC) curves. We first used JPEG 2000 to compress these cases of images with different compression ratio from lossless to lossy, and used the CAD system to classify the cases with different compressed ratio, then compared the ROC curves from the CAD classification results. Support vector machine (SVM) and neural networks (NN) were used to classify the malignancy of input nodules. In each approach, we found that the area under ROC (AUC) decreases with the increment of compression ratio with small fluctuations.

Paper Details

Date Published: 21 March 2007
PDF: 9 pages
Proc. SPIE 6516, Medical Imaging 2007: PACS and Imaging Informatics, 65161G (21 March 2007); doi: 10.1117/12.708902
Show Author Affiliations
Weihua Cai, Shanghai Institute of Technical Physics (China)
Yongqiang Tan, Shanghai Institute of Technical Physics (China)
Jianguo Zhang, Shanghai Institute of Technical Physics (China)


Published in SPIE Proceedings Vol. 6516:
Medical Imaging 2007: PACS and Imaging Informatics
Steven C. Horii; Katherine P. Andriole, Editor(s)

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