Kai-Sheng Hsieh

Bio: Kai-Sheng Hsieh is an academic researcher. The author has contributed to research in topics: Feature vector & Contextual image classification. The author has an hindex of 6, co-authored 9 publications receiving 318 citations.

Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform

Abstract: Describes the feasibility of selecting a fractal feature vector based on M-band wavelet transform to classify ultrasonic liver images - normal liver, cirrhosis, and hepatoma The proposed feature extraction algorithm is based on the spatial-frequency decomposition and fractal geometry Various classification algorithms based on respective texture measurements and filter banks are presented and tested Classifications for the three sets of ultrasonic liver images reveal that the fractal feature vector based on M-band wavelet transform is trustworthy A hierarchical classifier, which is based on the proposed feature extraction algorithm is at least 967% accurate in the distinction between normal and abnormal liver images and is at least 936% accurate in the distinction between cirrhosis and hepatoma liver images Additionally, the criterion for feature selection is specified and employed for performance comparisons herein

Ultrasonic liver tissue characterization by feature fusion

Abstract: 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

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

Abstract: 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.

Evolution-Based Hierarchical Feature Fusion for Ultrasonic Liver Tissue Characterization

Abstract: 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.

Ultrasound image segmentation: a survey

Abstract: 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

Feature selection using fuzzy entropy measures with similarity classifier

Abstract: Feature selection plays an important role in classification for several reasons. First it can simplify the model and this way computational cost can be reduced and also when the model is taken for practical use fewer inputs are needed which means in practice that fewer measurements from new samples are needed. Second by removing insignificant features from the data set one can also make the model more transparent and more comprehensible, providing better explanation of suggested diagnosis, which is an important requirement in medical applications. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy entropy measures is introduced and it is tested together with similarity classifier. Model was tested with four medical data sets which were, dermatology, Pima-Indian diabetes, breast cancer and Parkinsons data set. With all the four data sets, we managed to get quite good results by using fewer features that in the original data sets. Also with Parkinsons and dermatology data sets, classification accuracy was managed to enhance significantly this way. Mean classification accuracy with Parkinsons data set being 85.03% with only two features from original 22. With dermatology data set, mean accuracy of 98.28% was achieved using 29 features instead of 34 original features. Results can be considered quite good.

Texture Analysis of Aggressive and Nonaggressive Lung Tumor CE CT Images

Abstract: This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluoro deoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure.