Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach
TL;DR: A hybrid approach called Support vector machine combined with relevance feedback for the retrieval of liver diseases from Ultrasound (US) images is introduced and comprises several benefits when compared to existing CBIR for medical system by neural network algorithms.
Abstract: Problem statement: In the past few years, immense improvement was obtained in the field of Content-Based Image Retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from images by human experts. Approach: In this study, we present a hybrid approach called Support vector machine combined with relevance feedback for the retrieval of liver diseases from Ultrasound (US) images is introduced. SVM and RF are supervised active learning technique used to improve the effectiveness of the retrieval system. Three kinds of liver diseases are identified including cyst, alcoholic cirrhosis and carcinoma. The diagnosis scheme includes four steps: image registration, feature extraction, feature selection and image retrieval. First the ultrasound images are registered in the database based on the modality. Then the features, derived from first order statistics, gray level co-occurrence matrix and fractal geometry, are obtained from the Pathology Bearing Regions (PBRs) among the normal and abnormal ultrasound images. The Correlation Based Feature Selection (CFS) algorithm selects the certain features for the specific diseases and also reduces dimensionality space for classification. Finally, we implement our hybrid approach for retrieval of specific diseases from the database. Results: This hybrid approach can get the query from user and has retrieved both positive and negative samples from the database, by getting feedback in each round from the radiologist is help to improve the retrieval of correct images. Conclusion: The hybrid approach (SVM+RF) comprises several benefits when compared to existing CBIR for medical system by neural network algorithms. Fractal geometry in feature extraction plays crucial role in ultrasound liver image retrieval. CFS also reduce the dimensionality issue during storage. Image registration plays an important role in the retrieval. It reduces the redundancy of retrieval images and increases the response rate. Getting relevance feedback from physician helps to improve the accuracy of retrieval images from the database.
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TL;DR: Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set and showed an excellent rate of accuracy for the training data set.
Abstract: The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.
82 citations
TL;DR: A new method of content based medical image retrieval through considering fused, context-sensitive similarity, which has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases and achieved not only better retrieval results but also the satisfactory computation efficiency.
56 citations
Proceedings Article•
01 Jan 2005TL;DR: A strict separation of feature extraction, feature storage, feature comparison, and the user interfaces is suggested, which allows to reuse implemented components in different retrieval algorithms, which improves software quality, shortens the development cycle for applications, and allows to introduce standardized end-user interfaces.
Abstract: This work presents mechanisms to support the development and installation of content-based image retrieval in medical applications (IRMA). A strict separation of feature extraction, feature storage, feature comparison, and the user interfaces is suggested. The concept and implementation of a system following these guidelines is described. The system allows to reuse implemented components in different retrieval algorithms, which improves software quality, shortens the development cycle for applications, and allows to establish standardized end-user interfaces.
44 citations
TL;DR: Comparative analysis of nine textural feature measures derived from gray-level co-occurrence matrix obtained from the region(s) of interest (ROI) among the normal and abnormal anatomical structures that appear in the patient’s ultrasound liver images shows that cluster prominence, cluster shade, maximum probability, and entropy have high classes’ separability power.
Abstract: Comparative analysis of nine textural feature measures derived from gray-level co-occurrence matrix obtained from the region(s) of interest (ROI) among the normal and abnormal anatomical structures that appear in the patient’s ultrasound liver images is presented in this paper. Selection of the most robust discriminating features for classification experiment is performed through analysis of each feature classes’ separability power. The results analysis shows that cluster prominence, cluster shade, maximum probability, and entropy have high classes’ separability power and were selected for the classification of liver ultrasound images into normal liver (NL), primary liver cell carcinoma (PLCC) and hepatocellular carcinoma (HCC) at 0.4, 0.4, 0.2 and 0.6 sensitivity respectively. Keywords— Liver tissue, Feature extraction, Feature selection.
26 citations
Cites background from "Content Based Image Retrieval of Ul..."
...ltrasound images play an important role to detect anatomical and functional information of liver tissue for diagnosis [5, 6]....
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TL;DR: An investigation on the performance of five widely used feature selection methods namely Chi-square, Correlation, GSS Coefficient, Information Gai n and Relief F and a n approach of combination of feature selection meth ods based on the average weight of the features.
Abstract: Text classification is a very important task due to the huge amount of electronic documents. One of the problems of text classification is the high dimensi onality of feature space. Researchers proposed many algorithms to select related features from text. Th ese algorithms have been studied extensively for En glish text, while studies for Arabic are still limited. This st udy introduces an investigation on the performance of five widely used feature selection methods namely Chi-square, Correlation, GSS Coefficient, Information Gai n and Relief F. In addition, this study also introduces a n approach of combination of feature selection meth ods based on the average weight of the features. The experime nts are conducted using Naive Bayes and Support Vector Machine classifiers to classify a published Arabic corpus. The results show that the best results were obtained when using Information Gain method. The results also show that the combination of multiple feature sel ection methods outperforms the best results obtain by the individual methods.
18 citations
References
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TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
40,826 citations
"Content Based Image Retrieval of Ul..." refers background or methods in this paper
...Carcinoma: Carcinoma involves benign hepatic masses and consists of large thin-walled blood vessels, lined with flattened epithelium and separated by firous spaces filled with venous blood, commonly occurs to women (Chang and Lin, 2001; Jain et al., 2000)....
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...SVM is originally a method for binary classification (Lee et al., 2002; Lee et al., 2003; Chang and Lin, 2001; Jain et al., 2000), however, in medical practice; the number of possible disease types is rarely restricted to two categories, called positive samples and negative samples....
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TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Abstract: Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
6,562 citations
"Content Based Image Retrieval of Ul..." refers background in this paper
...Several existing works on content based medical image retrieval for ultrasound liver diseases were undergone by neural network algorithms (Hsu and Lin, 2002)....
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...CHU et al. present a knowledge-based image retrieval system with spatial and temporal constructs (Hsu and Lin, 2002)....
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TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Abstract: The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
6,527 citations
Book•
01 Jan 1993
TL;DR: The digitized image and its properties are studied, including shape representation and description, and linear discrete image transforms, and texture analysis.
Abstract: List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.
5,451 citations
TL;DR: It is shown theoretically and empirically that AUC is a better measure (defined precisely) than accuracy and reevaluate well-established claims in machine learning based on accuracy using AUC and obtain interesting and surprising new results.
Abstract: The area under the ROC (receiver operating characteristics) curve, or simply AUC, has been traditionally used in medical diagnosis since the 1970s. It has recently been proposed as an alternative single-number measure for evaluating the predictive ability of learning algorithms. However, no formal arguments were given as to why AUC should be preferred over accuracy. We establish formal criteria for comparing two different measures for learning algorithms and we show theoretically and empirically that AUC is a better measure (defined precisely) than accuracy. We then reevaluate well-established claims in machine learning based on accuracy using AUC and obtain interesting and surprising new results. For example, it has been well-established and accepted that Naive Bayes and decision trees are very similar in predictive accuracy. We show, however, that Naive Bayes is significantly better than decision trees in AUC. The conclusions drawn in this paper may make a significant impact on machine learning and data mining applications.
1,528 citations
"Content Based Image Retrieval of Ul..." refers background in this paper
...The decisions made by radiologist are heavily dependent on their experience, which might be related to certain characteristics from the visual interpretation of the image or some comparisons with different pathologies (Huang and Ling, 2005)....
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