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Journal ArticleDOI

Application of artificial neural networks for the classification of liver lesions by image texture parameters

01 Jan 1996-Ultrasound in Medicine and Biology (Elsevier)-Vol. 22, Iss: 9, pp 1177-1181
TL;DR: 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.
Citations
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Proceedings ArticleDOI
17 Sep 2003
TL;DR: The multiple classification scheme using the five sets of texture features results in significantly enhanced performance, as compared to the classification performance of the individual primary classifiers.
Abstract: In this paper, a Computer Aided Diagnosis (CAD) system for the characterization of hepatic tissue from Computed Tomography (CT) images is presented. Regions of Interest (ROI's) corresponding to normal liver, cyst, hemangioma, and hepatocellular carcinoma, are drawn by an experienced radiologist on abdominal nonenhanced CT images. For each ROI, five distinct sets of texture features are extracted using the following methods: first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. If the dimensionality of a feature set is greater than a predefined threshold, feature selection based on a Genetic Algorithm (GA) is applied. Classification of the ROI is then carried out by a system of five neural networks (NNs), each using as input one of the above feature sets. The members of the NN system (primary classifiers) are 4-class NNs trained by the backpropagation algorithm with adaptive learning rate and momentum. The final decision of the CAD system is based on the application of a voting scheme across the outputs of the individual NNs. The multiple classification scheme using the five sets of texture features results in significantly enhanced performance, as compared to the classification performance of the individual primary classifiers.

50 citations

Proceedings ArticleDOI
22 Oct 2007
TL;DR: This paper summarizes the various statistical and spectral texture parameter extraction processes, optimal feature selection techniques and automated classification procedures involved in the possibilities of automatic classification of the ultrasound liver images into four classes-normal, cyst, benign and malignant masses.
Abstract: Ultrasound Medical Imaging is currently the most popular modality for diagnostic application. This imaging technique has been used for the detecting abnormalities associated with abdominal organs like liver, kidney, uterus etc. In this paper, the possibilities of automatic classification of the ultrasound liver images into four classes-normal, cyst, benign and malignant masses, using texture features are explored. These texture features are extracted using the various statistical and spectral methods. The optimal feature selection process is carried out manually to pick the best discriminating features from the extracted texture parameters. Also, the method of principal component analysis is used to extract the principal features or directions of maximum information from the data set there by automatically selecting the optimal features. Using these optimal features, a final combined feature set is formed and is employed for classification of the liver lesions into respective classes. K-means clustering and neural network based automatic classifiers are employed in this process. The classifier design and its performance are studied. This paper summarizes the various statistical and spectral texture parameter extraction processes, optimal feature selection techniques and automated classification procedures involved in our work.

49 citations

Journal ArticleDOI
TL;DR: This paper considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors, and improved the computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images.
Abstract: The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue.

47 citations

Journal ArticleDOI
Yoo Na Hwang1, Juhwan Lee1, Ga Young Kim1, Yuan Yuan Jiang1, Sung Min Kim1 
TL;DR: The results of the experiment indicate that it is possible for the proposed method to be applied clinically, with a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst versus. malignant, and hemang ioma vs.malignant) on ultrasound images.
Abstract: This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

46 citations

Journal ArticleDOI
TL;DR: Texture analysis can identify carotid plaques associated with a neurological event, improving the diagnostic value of echolucency measures and could be applied to natural history studies.
Abstract: We estimated the value of objective, computerized texture analysis of ultrasonic images in distinguishing carotid plaques associated with neurological ipsilateral symptoms (amaurosis fugax [AmF; n ...

45 citations

References
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Journal ArticleDOI
01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

20,442 citations

Journal ArticleDOI
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Abstract: Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets.

7,798 citations

Book
01 Jan 1974
TL;DR: The objective is to establish an experimental procedure and show direct AFM progression from EMT to EMT using a simple, straightforward, and reproducible procedure.
Abstract: Pathologic basis of disease , Pathologic basis of disease , کتابخانه دیجیتالی دانشگاه علوم پزشکی و خدمات درمانی شهید بهشتی

5,162 citations

Journal ArticleDOI
01 Apr 1976
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.
Abstract: 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. Feature sets of these types, all designed analogously, were used to classify two sets of terrain samples. It was found that the Fourier features generally performed more poorly, while the other feature sets all performned comparably.

1,379 citations

Journal ArticleDOI
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).

443 citations