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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
01 Nov 2006
TL;DR: An automatic liver diseases diagnostic system is implemented for early detection of liver diseases and can be used as a second opinion system to aid the diagnosis of liver disease.
Abstract: Ultrasound is a widely used medical imaging technique. Tissue characterization with ultrasound has become important topic since computer facilities have been available for the analysis of ultrasound signals. Automatic liver tissue characterizations from ultrasonic scans have been long the concern of many researchers. Different techniques has been used ranging from processing the RF signals received by the transducer to using neural networks to analyze images based on image texture. In this paper, an automatic liver diseases diagnostic system is implemented for early detection of liver diseases. The proposed system classification accuracy is 96.125%. The system advantage is its high accuracy and its computation simplicity. The system can be used as a second opinion system to aid the diagnosis of liver diseases.

31 citations

Proceedings ArticleDOI
05 Nov 2007
TL;DR: The possibilities of an automatic classification of ultrasonic liver images by optimal selection of texture features are explored and these features are used to classify these images into four classes-normal, cyst, benign and malignant masses.
Abstract: Ultrasound imaging has found its own place in medical applications as an effective diagnostic tool. Ultrasonic diagnostics has made possible the detection of cysts, tumors or cancers in abdominal organs. In this paper, the possibilities of an automatic classification of ultrasonic liver images by optimal selection of texture features are explored. These features are used to classify these images into four classes-normal, cyst, benign and malignant masses. The texture features are extracted using the various statistical and signal processing methods. The automatic optimal feature selection process is based on the principal component analysis. This method extracts the principal features, or directions of maximum information from the data set. Using this new reduced feature set, the abnormalities are classified using the K-means clustering method. Based on the correct classification rate, a new optimal reduced feature set is created by combining the principal features extracted from the different texture features, to get a higher classification rate

30 citations

Proceedings ArticleDOI
25 Jul 2004
TL;DR: A computer aided diagnosis (CAD) system for the characterization of hepatic tissue from computed tomography (CT) images is presented and a classification performance of the order of 90.63% was finally achieved.
Abstract: 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 four types of hepatic tissue are drawn by an experienced radiologist on abdominal non-enhanced CT images. For each ROI, five sets of texture features are extracted and combined to provide input to the CAD system. If the dimensionality of a feature set is greater than a predefined threshold, appropriate feature selection based on a genetic algorithm (GA) is applied. Classification of the ROI is then carried out using an ensemble of classifiers consisting of two neural network (NN) and three statistical classifiers. The final decision of the CAD system is based on the application of a voting scheme across the outputs of the primary classifiers of the ensemble. A classification performance of the order of 90.63% was finally achieved.

26 citations

Proceedings ArticleDOI
01 Dec 2006
TL;DR: The objective of this work is to automatically extract the liver tumor from the liver region of the CT abdominal image and to characterize the liver tumors as benign or malignant using wavelet based texture analysis and Linear Vector Quantization (LVQ) neural network.
Abstract: The use of medical imaging and tissue characterization techniques is popular in diagnosis, treatment and research. The objective of this work is to automatically extract the liver tumor from the liver region of the CT abdominal image and to characterize the liver tumor as benign or malignant using wavelet based texture analysis and Linear Vector Quantization (LVQ) neural network. The system is tested with 100 images. The accuracy obtained is 92%. Performance of the system for the different parameters of LVQ like learning rate, number of hidden neurons and the number of epochs are analyzed. To evaluate the performance of the system, parameters like sensitivity, specificity, positive predicting value and negative predicting value are calculated. The results are evaluated with radiologists.

26 citations

Journal ArticleDOI
TL;DR: It can be concluded that the proposed hybrid CAD system design could be used as a second opinion tool in clinical setting with optimal performance for classification of benign and malignant breast tumors with a classification accuracy of 96.0%.

25 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