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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： S. Chitroub S. AlSultan Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB Algiers Algeria Al Madinah Remote Sensing Center Riaydh Alkhabra Saudi Arabia

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3D ROC analysis for medical imaging diagnosis

3D ROC analysis for medical imaging diagnosis

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2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005

作者： Wang, Su Chang, Chein-I Yang, Sheng-Chih Hsu, Giu-Cheng Hsu, Hsian-He Chung, Pau-Choo Guo, Shu-Mei Lee, San-Kan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 Department of Electrical Engineering National Cheng Kung University Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center Taipei 114 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Administrative Office Suao Veterans Hospital Yuan County 270 Taiwan

ISBN: (纸本)0780387406

Receiver operating characteristics (ROC) has been widely used as a performance evaluation tool to measure effectiveness of medical modalities. It is derived from a standard detection theory with false alarm and detection power interpreted as false positive (FP) and true positive (TP) respectively in terms of medical diagnosis. The ROC curve is plotted based on TP versus FP via hard decisions. This paper presents a three dimensional (3D) ROC analysis which extends the traditional two-dimensional (2D) ROC analysis by including a threshold parameter in a third dimension resulting from soft decisions, (SD). As a result, a 3D ROC curve can be plotted based on three parameters, TP, FP and SD. By virtue of such a 3D ROC curve three two-dimensional (2D) ROC curves can be derived, one of which is the traditional 2D ROC curve of TP versus FP with SD reduced to hard decision. In order to illustrate its utility in medical diagnosis, its application to magnetic resonance (MR) image classification is demonstrated. © 2005 IEEE.

关键词： Medical imaging

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A computer-aided system for mass detection and classification in digitized mammograms

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Biomedical Engineering - Applications, Basis and Communications 2005年第5期17卷 215-228页

作者： Yang, Sheng-Chih Wang, Chuin-Mu Chung, Yi-Nung Hsu, Giu-Cheng Lee, San-Kan Chung, Pau-Choo Chang, Chein-I. Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Chin Yi Institute of Technology Taichung Taiwan Department of Electrical Engineering Da-Yeh University Chunghua Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Administrative Office Suao Veterans Hospital Yilan Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD United States

This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

关键词： Computer aided analysis

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A new application of texture unit coding to mass classification for mammograms

A new application of texture unit coding to mass classificat...

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2004 International Conference on image processing, ICIP 2004

作者： Chen, Yuan Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250

Texture is one of important features of masses in mammograms. A recent texture unit-based texture spectrum approach, referred to as Texture Unit Coding (TUC) has shown promise in texture classification. This paper presents a new application of the TUC to mass classification in mammograms. The TUC generates a texture spectrum for a texture image that can be used to describe the characteristics of masses. It also develops an information divergence (ID)-based discrimination criterion to measure the discrepancy between two texture spectra, a concept yet to explore in texture analysis. The TUC along with ID are further applied to mass classification in mammograms where the MiniMammographic Database provided by the Mammographie image Analysis Society (MIAS) is used for experiments. ©2004 IEEE.

关键词： Mammography

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How to effectively utilize information to design hyperspectral target detection and classification algorithms

How to effectively utilize information to design hyperspectr...

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2003 IEEE Workshop on Advances in Techniques for Analysis of remotely Sensed Data

作者： Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States

ISBN: (纸本)0780383508

Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way image analysis algorithms are designed. In this paper, we take up this issue and focus on algorithms designed and developed for target detection and classification in hyperspectral imagery. In order to effectively characterize the information available before and after the data are processed, the a priori information and a posteriori information are used in accordance with how the information is obtained. A piece of information is referred to as a priori information if it is provided by known knowledge before data are processed. On the other hand, a piece of information is referred to as a posteriori information if it is unknown a priori, but can be obtained directly from the data in an unsupervised fashion during the course of data processing. Since a priori information is known beforehand, it can be further decomposed into two types of information, desired and undesired a priori information. The desired a priori information is the knowledge that will assist, improve and enhance data analysis, whereas the undesired a priori information is the knowledge that hinders, interferes or destructs analysis during data processing. This paper investigates how these three types of information play their roles in design and development of several hyperspectral target detection and classification algorithms. Experiments are also conducted to validate their utility. © 2004 IEEE.

关键词： Classification (of information)

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Solving adundance estimation in hyperspectral unmixing as a least distance problem

Solving adundance estimation in hyperspectral unmixing as a ...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： M. Velez-Reyes S. Rosario Laboratory for Applied Remote Sensing and Image Processing University of Puerto Rico MayagÜez Mayaguez Puerto Rico

This paper presents an algorithm for abundance estimation in hyperspectral imagery. The fully constrained abundance estimation problem where the positivity and the sum to less than or equal to one (or sum equal to one) constraints are enforced is solved by reformulating the problem as a least distance (LSD) least squares (LS) problem. The advantage of reformulating the problem as a least distance problem is that the resulting LSD problem can be solved using a duality theory using a nonnegative LS problem (NNLS). The NNLS problem can then be solved using Hanson and Lawson algorithm or one of several multiplicative iterative algorithms presented in the literature. The paper presents the derivation of the algorithm

关键词： Hyperspectral sensors Hyperspectral imaging Iterative algorithms Spatial resolution Reflectivity Analytical models Noise measurement Gaussian noise Laboratories remote sensing

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Discrimination and identification for subpixel targets in hyperspectral imagery

Discrimination and identification for subpixel targets in hy...

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2004 International Conference on image processing, ICIP 2004

作者： Chang, Chein-I. Liu, Weimin Chang, Chein-Chi Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 Department of Civil and Environmental Engineering University of Maryland Baltimore County Baltimore MD 21250

Spectral measures have been used in material identification and discrimination. They are effective if the spectral signatures are calibrated and not contaminated. However, it may not be true in many real applications, specifically, for mixed pixels and subpixel targets. This paper investigates the issue of discrimination and identification for subpixel targets and further develops sample spectral covariance/correlation matrix-based hyperspectral measures to account for spectral variability within subpixel targets. Two types of measures are of interest and studied, Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures. In order to substantiate the proposed measures, a real data-based comparative analysis is conducted and compared to two spectral similarity measures, spectral angle mapper (SAM) and spectral information divergence (SID) for performance evaluation. The experiments show that both Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures work very effectively and outperformed the SAM and the SID in discrimination and identification for subpixel targets. ©2004 IEEE.

关键词： Imaging techniques

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Adaptive causal anomaly detection for hyperspectral imagery

Adaptive causal anomaly detection for hyperspectral imagery

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Mingkai Hsueh Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780387422

Anomaly detection finds target pixels whose signatures are spectrally distinct from their surrounding pixels. It is generally performed without prior knowledge. This paper presents an adaptive causal anomaly detector (ACAD) which implements a causal anomaly detector in such a fashion that a target pixel will be removed from the data correlation matrix once it is detected as an anomaly. As a result, it improves the commonly used RX algorithm as well as a recently developed causal RX filter

关键词： Hyperspectral imaging Filters Detectors Covariance matrix Adaptive signal detection Hyperspectral sensors remote sensing Signal processing image processing Laboratories

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A nested spatial window-based approach to target detection for hyperspectral imagery

A nested spatial window-based approach to target detection f...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Weimin Liu Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780387422

A great challenge of hyperspectral target detection is to detect subtle targets without prior knowledge, particularly, when the targets of interest are insignificant and occur with low probabilities. This work provides a promising alternative to adaptive hyperspectral target detection. It considers a nested spatial window-based target detection (NSWTD) approach for hyperspectral imagery where a set of different spatial windows are nested and implemented to extract targets whose signatures are spectrally and spatially distinct. The use of nested spatial windows is determined by the image pixel resolution and applications. In order to demonstrate the performance of the proposed NSWTD approach, dual nested windows and three nested windows are implemented for computer simulations and real hyperspectral image experiments. The experimental results demonstrate that our proposed NSWTD approach performs effectively and improves a recent adaptive anomaly detector developed by Kwon et al. and the commonly used anomaly detector developed by Reed and Yu, referred to as RX algorithm. Its computation complexity is also very simple.

关键词： Object detection Hyperspectral imaging Hyperspectral sensors Detectors Spatial resolution remote sensing Signal processing image processing Laboratories Computer science

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A new application of texture unit coding to mass classification for mammograms

A new application of texture unit coding to mass classificat...

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IEEE International Conference on image processing

作者： Yuan Chen Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

ISBN: (纸本)0780385543

Texture is one of important features of masses in mammograms. A recent texture unit-based texture spectrum approach, referred to as texture unit coding (TUC) has shown promise in texture classification. This paper presents a new application of the TUC to mass classification in mammograms. The TUC generates a texture spectrum for a texture image that can be used to describe the characteristics of masses. It also develops an information divergence (ID)-based discrimination criterion to measure the discrepancy between two texture spectra, a concept yet to explore in texture analysis. The TUC along with ID are further applied to mass classification in mammograms where the minimammographic database provided by the Mammographic image Analysis Society (MIAS) is used for experiments.

关键词： Pixel Breast cancer Application software image coding image texture analysis Spatial databases Helium image generation Histograms Shape

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