The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Texture features for classification of ultrasonic liver images
Abstract
The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images.
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Citations
Cites
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Cited by
Ultrasound image segmentation: a survey
Noble, J. A., & Boukerroui, D. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.877092 · Cited by 1,157
Radiomics: a new application from established techniques
Parekh, V., & Jacobs, M. A. (2016) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2016.1164013 · Cited by 369
Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery
Loizou, C. P., Pattichis, C. S., Christodoulou, C. I., Istepanian, R. S. H., Pantziaris, M., & Nicolaides, A. (2005) · IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2005.1561621 · Cited by 323
Texture-based classification of atherosclerotic carotid plaques
Christodoulou, C. I., Pattichis, C. S., Pantziaris, M., & Nicolaides, A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.815066 · Cited by 316
Quantification of Pulmonary Emphysema from Lung Computed Tomography Images
UPPALURI, R., MITSA, T., SONKA, M., HOFFMAN, E. A., & McLENNAN, G. (1997) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/ajrccm.156.1.9606093 · Cited by 276
Metadata
- Authors
- Wu, C.-M., Chen, Y.-C., & Hsieh, K.-S.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 1992-06
- Volume
- 11
- Issue
- 2
- Pages
- 141-152
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2167003500
- Cited by
- 476
- References
- 2
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wu, C.-M., Chen, Y.-C., & Hsieh, K.-S. (1992). Texture features for classification of ultrasonic liver images. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/42.141636
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