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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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Journal ArticleDOI
TL;DR: A sub-band denoising and spline curve fitting processes are proposed to improve image quality for better hemodynamic quantitative analysis results and the evaluated ratio of brain tissues in perfusion MRI is comparable to PET technique in less than 1-% difference in average.
01 Jan 2000
TL;DR: Preliminary results of the effort in texture classification of thyroid gland sonographic imagery are reported on classifica- tion of diffuse processes for distinguishing between normal tissue and chronic lymphocytic thyroiditis (Hashimoto's Thy- roiditis).
Abstract: Sonography is a widely used non-invasive diag- nostic tool. Analyzing changes in sonograms provides a means of diagnosing and monitoring chronic thyroid gland diseases. Nonetheless, conventional sonography is still qual- itative. To improve the diagnosis reliability, quantitative im- age analysis is highly desirable for the assessment of various thyroid gland conditions. In this paper, we report prelimi- nary results of our effort in texture classification of thyroid gland sonographic imagery, more specifically, on classifica- tion of diffuse processes for distinguishing between normal tissue and chronic lymphocytic thyroiditis (Hashimoto's Thy- roiditis). Other conditions will be studied in the future.
Proceedings Article
01 Jan 2014
TL;DR: In this article, a Computer-Aided Diagnosis (CAD) system for the diagnosis process of benign and malignant liver tumors from computed tomography (CT) is presented.
Abstract: The main objective of this study is to provide a Computer-Aided Diagnosis (CAD) system for the diagnosis process of benign and malignant liver tumors from computed tomography (CT). Also it aimed to evaluate the potential role of Fuzzy Clustering Means (FCM) and neural network in the differential diagnosis processes of liver tumors in CT images. In this study, liver tumors are classified as hepatocellular carcinoma (cancer) and hemangioma (benign). By using FCM each suspicious tumor region is automatically extracted from liver images. Consequently, textural features are obtained. These features are used to train the Neural Network (NN) and classify the tumors. The system distinguishes tumors with high accuracy and is therefore clinically useful.
Book ChapterDOI
07 Jul 2011
TL;DR: This work improves the textural model and the classification process, through dimensionality reduction techniques, of the hepatocellular carcinoma, the most frequent malignant liver tumor, using only information from ultrasound images.
Abstract: The non-invasive diagnosis of the malignant tumors is a very important issue in nowadays research. Our purpose is to elaborate computerized, texture-based methods for performing automatic recognition of the hepatocellular carcinoma, the most frequent malignant liver tumor, using only information from ultrasound images. We previously defined the textural model of HCC, consisting in the exhaustive set of the textural features, relevant for HCC characterization, and in their specific values for the HCC class. In this work, we improve the textural model and the classification process, through dimensionality reduction techniques. From the feature extraction methods, we implemented the most representative ones - Principal Component Analysis (PCA), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (GDA) and combinations of these methods. We also assessed the combination of the feature extraction techniques with feature selection techniques. All these methods were evaluated for distinguishing HCC from the cirrhotic liver parenchyma on which it evolves.
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