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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shaohua Su Yan Sun |
| Copyright Year | 2011 |
| Description | Author affiliation: School of Software, Shanghai Jiao Tong University, China (Shaohua Su; Yan Sun) |
| Abstract | Computer-aided diagnosis (CAD) of liver diseases as an early non-invasive diagnosis is of great significance. This paper presents an automated diagnostic system for liver disease based on multi-phase CT images. The region of the liver is first extracted from a CT image using improved watershed algorithm. After the registration of liver regions, which uses the SIFT algorithm, the operation of extracting the ROI based on Gabor wavelet transformation would be followed. Besides using image texture metric as the feature vector, we also designed a temporal and sacttergram-based lesion enhancement pattern descriptor to quantify the different lesions. Then, in the designing of classifier module, we convert a 4 classes classifying problem into 3 binary classify problems by using artificial neural network. Finally, we obtained the best classification accuracy of 0.9797, 0.9851 and 0.9753 for normal-abnormal, cyst-otherdisease and carcinoma-haemangioma sub problems respectively. |
| Starting Page | 1921 |
| Ending Page | 1927 |
| File Size | 438428 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424493043 |
| e-ISBN | 9781424493067 |
| DOI | 10.1109/CISP.2011.6100642 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-10-15 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wavelet transforms Wavelet Transform Computer-Aided Diagnosis Computed tomography sattergram Liver Watershed Feature extraction Lesions Cancer Liver Lesion |
| Content Type | Text |
| Resource Type | Article |
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