This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

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Ultrasound image segmentation: a survey

http://www.ens-lyon.fr/DI/wp-content/uploads/2009/07/LopesBetrouni.pdf

Fractal and multifractal analysis: a review.

Texture features for image classification and retrieval.

Fractal dimension applied to plant identification

Texture is an important image feature and has been applied greatly in the food industry for quality evaluation and inspection. Recent applications of texture-analysis techniques in the food industry were reviewed in the current paper. There are four different types of texture, i.e. statistical texture, structural texture, model-based texture, and transform-based texture. Statistical texture can be obtained using statistical approachs from the higher-order of pixel grey values of images while structural texture is acquired through some structural primitives constructed from grey values of pixels. Transform-based texture can be obtained by using statistical measurements from the images transformed with certain methods. Model-based texture can be attained by calculating coefficients from a model based on the relationship of the grey values between a pixel and its neighboring pixels. Among them, statistical texture is the most widely used one in the food industry for its high accuracy and less computation time. Transform-based texture and model-based texture are also used, although not as popular as statistical texture. However, structural texture is rarely used in the food industry. Due to the limited understanding of image texture up to now, it is difficult to identify which kind of method is the best for certain application. Therefore, it is expected that by employing different methods together, final results can be improved.

Recent applications of image texture for evaluation of food qualities—a review

Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector

Highlights? We extract five feature spaces for the ultrasonic liver tissue characterization ? The proposed genetic-algorithm-based feature fusion is effective ? Feature fusion combines features selected from different feature spaces ? We select features to improve the classification accuracy for US liver images This paper describes a two-stage feature fusion method for ultrasonic liver tissue characterization The proposed method hierarchically incorporates a genetic-algorithm-based feature selection to automatically select more efficient feature subset to discriminate among ultrasonic images of liver tissue in three states: normal liver, cirrhosis, and hepatoma Multiple feature spaces are adopted in this paper, including the spatial gray-level dependence matrices (SGLDMs), multiresolution fractal feature vector and multiresolution energy feature vector Features extracted from different feature spaces may contain complementary information The feature subsets of different feature spaces are fused and the genetic-algorithm-based feature selection is applied onto the fused feature space to facilitate the two-stage feature fusion The classification accuracy of the fused feature subset is up to 9662% Experimental results demonstrate that the proposed method is capable to select discriminative features among multiple feature vectors to achieve the early detection of hepatoma and cirrhosis based on ultrasonic liver imaging

Ultrasonic liver tissue characterization by feature fusion

In this study, we evaluate the accuracy of classifiers for classification of ultrasonic liver tissues. Two different statistic classifiers and three various artificial neural networks are included: Bayes classifier, k-nearest neighbor classifier, Back-propagation neural networks, probabilistic neural network and modified probabilistic neural network. These five different classifiers were investigated to determine their ability to classify various categories of ultrasonic liver images. The investigation was performed on the basis of the same feature vector. For statistic classifiers the classification accuracy is at most 90.7% and with artificial neural networks the accuracy is at least 92%. The experimental results illustrated that artificial neural networks are an attractive alternative to conventional statistic techniques when dealing with classification task. Moreover, the feature vector based on fractal geometry and wavelet transform can provide good discriminant ability for ultrasonic liver images under study.

A study of ultrasonic liver images classification with artificial neural networks based on fractal geometry and multiresolution analysis

This paper presents an evolution-based hierarchical feature fusion system that selects the dominant features among multiple feature vectors for ultrasonic liver tissue characterization. After extracting the spatial gray-level dependence matrices, multiresolution fractal feature vectors and multiresolution energy feature vectors, the system utilizes evolution-based algorithms to select features. In each feature space, features are selected independently to compile a feature subset. As the features of different feature vectors contain complementary information, a feature fusion process is used to combine the subsets generated from different vectors. Features are then selected from the fused feature vector to form a fused feature subset. The selected features are used to classify ultrasonic images of liver tissue into three classes: hepatoma, cirrhosis, and normal liver. Experiment results show that the classification accuracy of the fused feature subset is superior to that derived by using individual feature subsets. Moreover, the findings demonstrate that the proposed algorithm is capable of selecting discriminative features among multiple feature vectors to facilitate the early detection of hepatoma and cirrhosis via ultrasonic liver imaging.

Evolution-Based Hierarchical Feature Fusion for Ultrasonic Liver Tissue Characterization

This work describes the feasibility of multiresolution feature selection and its application to classify ultrasonic liver images. The proposed approach uses genetic algorithm and defines a novel fitness function for medical applications since the diagnosis correctness is the most important consideration. Via the measurement of class seperability, we can uniquely select the feature vector. The effectiveness of the proposed approach is tested by using five different classifiers. From the experimental results, the proposed approach is trustworthy.

Wen-Li Lee

Papers

Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector

Ultrasonic liver tissue characterization by feature fusion

A study of ultrasonic liver images classification with artificial neural networks based on fractal geometry and multiresolution analysis

Evolution-Based Hierarchical Feature Fusion for Ultrasonic Liver Tissue Characterization

A GA-Based Multiresolution Feature Selection for Ultrasonic Liver Tissue Characterization