Journal ArticleDOI
Application of artificial neural networks for the classification of liver lesions by image texture parameters
TLDR
In this article, a multilayered back-propagation neural network was used for liver lesion classification using B-scan ultrasound images for normal, hemangioma and malignant livers.Abstract:
Ultrasound imaging is a powerful tool for characterizing the state of soft tissues; however, in some cases, where only subtle differences in images are seen as in certain liver lesions such as hemangioma and malignancy, existing B-scan methods are inadequate. More detailed analyses of image texture parameters along with artificial neural networks can be utilized to enhance differentiation. From B-scan ultrasound images, 11 texture parameters comprising of first, second and run length statistics have been obtained for normal, hemangioma and malignant livers. Tissue characterization was then performed using a multilayered backpropagation neural network. The results for 113 cases have been compared with a classification based on discriminant analysis. For linear discriminant analysis, classification accuracy is 79.6% and with neural networks the accuracy is 100%. The present results show that neural networks classify better than discriminant analysis, demonstrating a much potential for clinical application.read more
Citations
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Proceedings ArticleDOI
Improving the Textural Model of the Hepatocellular Carcinoma Using Dimensionality Reduction Methods
TL;DR: This paper enhances the imagistic textural model of HCC, by using dimensionality reduction methods, the final purpose being that of obtaining an improvement of the classification process.
Proceedings ArticleDOI
The role of the superior order GLCM and of the generalized cooccurrence matrices in the characterization and automatic diagnosis of the hepatocellular carcinoma, based on ultrasound images
TL;DR: This work analyzes the role that the superior order Gray Level Cooccurrence Matrices (GLCM) and the Edge Orientation Co Occurrence Matrix (EOCM) have concerning the improvement of HCC characterization and automatic diagnosis, and determines the best spatial relation between the pixels that leads to the highest performances.
Journal ArticleDOI
Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion.
Naoki Matsumoto,Masahiro Ogawa,Masahiro Kaneko,Mariko Kumagawa,Yukinobu Watanabe,Midori Hirayama,Hiroshi Nakagawara,Ryota Masuzaki,Tatsuo Kanda,Mitsuhiko Moriyama,Tadatoshi Takayama,Masahiko Sugitani +11 more
TL;DR: The aim of this study was to clarify the correlation between the features from a US image analysis and the histologic grade and microvascular invasion of hepatocellular carcinoma (HCC) and differentiation of HCC smaller than 2 cm from borderline lesions.
Journal ArticleDOI
A Characterization Approach for the Review of CAD Systems Designed for Breast Tumor Classification Using B-Mode Ultrasound Images
TL;DR: An exhaustive review of machine learning and deep learning based computer aided diagnostic (CAD) system designs has been conducted and brain storming diagrams have been used to indicate the characterization approaches for each stage i.e. datasets, pre-processing methods, data augmentation methods, segmentation methods, feature extraction methods and evaluation metrics.
Proceedings ArticleDOI
Enhanced classification of focal hepatic lesions in ultrasound images using novel texture features
TL;DR: Novel texture features that allow providing enhanced classification accuracy for focal hepatic lesions by taking advantage of the rotation and scale invariant nature of Gabor wavelets, as well as the gray-level co-occurrence matrix (GLCM) for analyzing the spatial distribution of the pixel intensity in the lesion.
References
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A Comparative Study of Texture Measures for Terrain Classification
TL;DR: In this paper, three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively.
Journal ArticleDOI
Use of gray value distribution of run lengths for texture analysis
TL;DR: The gray value distribution of the runs is proposed to be used to define two new features, viz., low gray level run emphasis ( LGRE) and high gray levelrun emphasis ( HGRE).