Chung-Ming Wu

Bio: Chung-Ming Wu is an academic researcher from National Tsing Hua University. The author has contributed to research in topics: Fractal & Feature vector. The author has an hindex of 4, co-authored 5 publications receiving 535 citations.

Texture features for classification of ultrasonic liver images

Abstract: The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images. >

Two-stage liver tissue classification through fractal geometry

Abstract: A fractal analysis procedure for the classification of ultrasonic images of liver tissue is proposed. Based on the two properties of fractals (self‐similarity and self‐affinity), it is possible to recognize not only normal and abnormal liver tissues but also different kinds of abnormalities. Two‐stage procedures are performed in the classification. In the first stage, the mass density feature vectors are estimated by the property of self‐similarity, classification of normal and abnormal ultrasonic liver tissues can be obtained from the differences between their mass density feature vectors. In the second stage, based on the property of self‐affinity, the fractal Brownian motion model is adopted to represent the imaging surface. By evaluating the fractal Brownian feature vectors in about 27 abnormal ultrasonic liver images, it is found that it is possible to classify different kinds of diseases using the model. Moreover, the measurement of lacunarities can be added to improve the method to classif...

Automatically detecting endocardium and epicardium of left ventricle by digital image processing for 2‐D echocardiography

Abstract: Detecting the endocardium and epicardium of the left ventricle is important for further quantitative analysis of cardiac functions and three‐dimensional reconstruction. However, to detect the boundaries automatically is difficult. In this paper, we proposed a novel, single frame method, based on binary morphology and region growing techniques, to detect the boundaries of the left ventricle automatically except an A.O.I. (Area Of Interest) adjustment operation must be performed during the process of getting image data from video tape and storing it onto the disk. The method is suited for boundary detection required in three‐dimensional reconstruction for the observation of congenital abnormalities of ventricular septum or for generating cross sectional images of arbitrary orientation. Moreover, the method may be improved by further processings for accurate endocardial and epicardial boundary estimation for quantitative cardiac function evaluations.

Texture analysis

Abstract: This chapter reviews and discusses various aspects of texture analysis. The concentration is o the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing pro lems such as segmentation, classification, and shape from texture are discussed. The possible applic tion areas of texture such as automated inspection, document processing, and remote sensing a summarized. A bibliography is provided at the end for further reading.

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

Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique

Abstract: The purpose of this study is to develop a technique for computer-aided diagnosis (CAD) systems to detect lung nodules in helical X-ray pulmonary computed tomography (CT) images. The authors propose a novel template-matching technique based on a genetic algorithm (GA) template matching (GATM) for detecting nodules existing within the lung area; the GA was used to determine the target position in the observed image efficiently and to select an adequate template image from several reference patterns for quick template matching. In addition, a conventional template matching was employed to detect nodules existing on the lung wall area, lung wall template matching (LWTM), where semicircular models were used as reference patterns; the semicircular models were rotated according to the angle of the target point on the contour of the lung wall. After initial detecting candidates using the two template-matching methods, the authors extracted a total of 13 feature values and used them to eliminate false-positive findings. Twenty clinical cases involving a total of 557 sectional images were used in this study. 71 nodules out of 98 were correctly detected by the authors' scheme (i.e., a detection rate of about 72%), with the number of false positives at approximately 1.1/sectional image. The authors' present results show that their scheme can be regarded as a technique for CAD systems to detect nodules in helical CT pulmonary images.

Texture-based classification of atherosclerotic carotid plaques

Abstract: There are indications that the morphology of atherosclerotic carotid plaques, obtained by high-resolution ultrasound imaging, has prognostic implications. The objective of this study was to develop a computer-aided system that will facilitate the characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. A total of 230 plaque images were collected which were classified into two types: symptomatic because of ipsilateral hemispheric symptoms, or asymptomatic because they were not connected with ipsilateral hemispheric events. Ten different texture feature sets were extracted from the manually segmented plaque images using the following algorithms: first-order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. For the classification task a modular neural network composed of self-organizing map (SOM) classifiers, and combining techniques based on a confidence measure were used. Combining the classification results of the ten SOM classifiers inputted with the ten feature sets improved the classification rate of the individual classifiers, reaching an average diagnostic yield (DY) of 73.1%. The same modular system was implemented using the statistical k-nearest neighbor (KNN) classifier. The combined DY for the KNN system was 68.8%. The results of this paper show that it is possible to identify a group of patients at risk of stroke based on texture features extracted from ultrasound images of carotid plaques. This group of patients may benefit from a carotid endarterectomy whereas other patients may be spared from an unnecessary operation.

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

Abstract: It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.