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Detection of White Matter Hyperintensities in Magnetic Resonance Imaging by Hyperspectral Subpixel Detection

IEEE Access 2024年 12卷 111992-112007页

作者： Yung-Chieh Chang Chein-I Chang Yen-Chieh Ouyang Jyh-Wen Chai Wen-Hsien Chen Kuan-Jung Pan Hsin-Che Wang Clayton Chi-Chang Chen Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and Technology Taichung Taiwan Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Medical Research Center for Quantitative Imaging in Medicine (CQUIM) Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images generally associated with Alzheimer’s disease (AD) and cognitive decline. Finding WMHs of AD poses a great challenge for diagnosis. This paper interprets a brain MR image as a hyperspectral image so that a well stablished hyperspectral subpixel detection algorithm, constrained energy minimization (CEM), is applicable to solving the WMHs detection problem at mixed pixel and subpixel level. To resolve nonlinear mixing in detecting WMHs nearby boundaries, a nonlinear CEM, called kernel CEM (KCEM) is also developed. Since CEM is a hyperspectral technique without taking spatial correlation into account, CEM was also extended to iterative CEM (ICEM) by including spatial filters to capture spatial information for WMHs detection. This paper combines ICEM and KCEM to derive a new WHMs detection algorithm, iterative KCEM (IKCEM) to improve ICEM and KCEM on WMHs detection. To evaluate the WMHs detection performance, two criteria, Dice similarity index (DSI) and 3D ROC analysis are used as evaluation tools. In order to show the superiority of IKCEM, two commonly used software packages, statistical parametric mapping (SPM)-based algorithms, SPM-lesion growth algorithm (SPM-LGA) and SPM-lesion prediction algorithm (SPM-LPA) are implemented for validation and comparison.

关键词： Hyperspectral imaging Magnetic resonance imaging Iterative methods Medical diagnostic imaging White matter Manuals Energy management Neuroimaging Brain modeling Alzheimer's disease Energy measurement

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Three Dimensional Synthetic Non-Ellipsoidal Nuclei Volume Generation Using Bézier Curves

Three Dimensional Synthetic Non-Ellipsoidal Nuclei Volume Ge...

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IEEE International Symposium on Biomedical Imaging

作者： Alain Chen Liming Wu Shuo Han Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana Indiana University-Purdue University Indianapolis Indiana Division of Nephrology School of Medicine Indiana University Indianapolis Indiana

ISBN: (纸本)9781665412469;9781665429474

Automated segmentation of cell nuclei is used to analyze individual cells to determine the number of nuclei in a 3D volume. Deep learning approaches that segment nuclei require large amounts of annotated (ground truth) microscopy volumes for training. In many cases acquiring large amounts of annotated volumes may not be possible and data augmentation methods must be used. One approach has been the use of synthetic volumes for training. Alternate methods employ spherical and ellipsoidal nuclear models for synthetic ground truth generation, resulting in segmentation that does not accurately match nuclei morphology. In this paper, we present a technique to generate synthetic non-ellipsoidal nuclei microscopy images using Bézier Curves. We test our approach by training a modified 3D U-Net. Our results indicates that our synthetic non-ellipsoidal nuclei approach achieves improved segmentation on volumes with irregularly shaped nuclei.

关键词： Training Deep learning image segmentation Three-dimensional displays Microscopy Morphology Generative adversarial networks

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A critical review of quality assessment protocols in pan-sharpening 5

A critical review of quality assessment protocols in pan-sha...

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ISPRS International GeoSpatial Conference 2019, Joint Conferences of 5th Sensors and Models in Photogrammetry and Remote Sensing, SMPR 2019 and 3rd Geospatial Information Research, GI Research 2019

作者： Aghapour Maleki, S. Ghassemian, H. Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Although vast amounts of pan-sharpening methods have been proposed to date, there has been relatively little published on the topic of qualitative and quantitative assessment of the pan-sharpened multispectral (MS) data. Since a high resolution reference MS image is not available, qualitative and quantitative assessments of the spatially enhanced MS image are a much more challenging task in pan-sharpening. Thomas et al. conducted a critical survey of conventional pan-sharpening methods considering remote sensing physics. In this paper, we study the effects of physical constraints of satellite sensors on conventional quality assessment protocols. The novelty of this work is in investigating the effect of physical constraints on the performance of quality assessment protocols. The most popular protocols, which are analysed here, are quality not requiring a reference (QNR), Wald's protocol, Zhou's protocol, Khan's protocol and Ghassemi's protocol. In doing so, the strengths and weaknesses of such protocols regarding the physical limitations on satellite sensors are discussed. © 2019 S. Aghapour Maleki.

关键词： image fusion

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Spatial-spectral morphological feature extraction for hyperspectral images classification 5

Spatial-spectral morphological feature extraction for hypers...

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ISPRS International GeoSpatial Conference 2019, Joint Conferences of 5th Sensors and Models in Photogrammetry and Remote Sensing, SMPR 2019 and 3rd Geospatial Information Research, GI Research 2019

作者： Dowlatshah, M. Ghassemian, H. Imani, M. Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Remote sensing image classification is a method for labeling pixels to show the Land cover types. The ambiguity in the classification process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the feature extraction process. One of the methods for spatial feature extraction is applying morphological filters. The basic idea of the morphological filters is comparison of structures within the image with a reference form called structural element. Four types of important morphological filters are included (dilation, erosion, opening, and closing) in this work. Opening morphological filter is used to extract spatial features where this filter is implemented by applying two successive sequences dilation and erosion operators. This filter removes the light areas smaller than the structural element in binary images;and in the gray level images, the areas smaller than the structural element and brighter than the neighboring regions are removed. Differential morphology filters are other important morphological filters, which are also used in this work. In the proposed method, the principal component analysis is used to reduce the data dimensions and an SVM classifier is applied to classify the hyperspectral data. The proposed method provides better classification results than the conventional morphological profile about 2%-5% for the University of Pavia and Pavia Center datasets. The results represent the good performance of the proposed method by using a small number of training samples. © 2019 M. Dowlatshah et al.

关键词： Feature extraction

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Lagged Load Wavelet Decomposition and LSTM Networks for Short-Term Load Forecasting 4

Lagged Load Wavelet Decomposition and LSTM Networks for Shor...

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4th International Conference on Pattern Recognition and image Analysis, IPRIA 2019

作者： Imani, Maryam Ghassemian, Hassan Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781728116211

Short-term load forecasting (STLF) is one of the main challenging problems in management of power systems. By emerging the new technologies and development of smart grids, STLF becomes a necessity. A STLF framework is proposed in this work which uses the long short-term memory (LSTM) network for forecasting. In the proposed framework, a feature vector containing the lagged load values is considered for each point of the load time series where this feature vector can be given to the LSTM network in three ways: 1) the original feature vector (LSTM), 2) the coefficients of discrete wavelet transform of the feature vector (DWT-LSTM), and 3) the stacked vector of wavelet coefficients and original load values (DWT-stack-LSTM). In the proposed DWT-stack-LSTM method, both of original values of load and decomposition values of load in different frequencies and scales are given to LSTM. Thus, LSTM is trained to learn more about load variables and so, provides more accurate forecasting results. © 2019 IEEE.

关键词： Discrete wavelet transforms

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Local binary graph feature reduction for three-dimensional gabor filter based hyperspectral image classification 5

Local binary graph feature reduction for three-dimensional g...

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ISPRS International GeoSpatial Conference 2019, Joint Conferences of 5th Sensors and Models in Photogrammetry and Remote Sensing, SMPR 2019 and 3rd Geospatial Information Research, GI Research 2019

作者： Darvishnezhad, M. Ghassemian, H. Imani, M. Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

One of the challenges of the hyperspectral image classification is the fusing spectral and spatial features. There are several methods for fusing features in hyperspectral image classification. Three-Dimensional Gabor Filters are the best method to extract spectral and spatial features simultaneously. However, one of the problems with using the 3D Gabor filter is the high number of extracted features. In this paper, to reducing extracted features from 3D-Gabor filters and increasing the classification accuracy in hyperspectral images, a novel method named Local Binary Graph (LBG) is used. The LBG method uses a local graph to solve the optimization problem, which maps each pixel to the reduced dimension image and improves the McNemar test result in comparison with the existing methods. Finally, the result of the proposed method achieved 96.2% and 92.6% overall accuracy for Pavia University and Indian Pines data set, respectively. © 2019 M. Darvishnezhad et al.

关键词： Gabor filters

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Forensic Scanner Identification Using Machine Learning

Forensic Scanner Identification Using Machine Learning

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Ruiting Shao Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9781728157450

ISBN: (纸本)9781728157467

Due to the increasing availability and functionality of image editing tools, many forensic techniques such as digital image authentication, source identification and tamper detection are important for forensic image analysis. In this paper, we describe a machine learning based system to address the forensic analysis of scanner devices. The proposed system uses deep-learning to automatically learn the intrinsic features from various scanned images. Our experimental results show that high accuracy can be achieved for source scanner identification. The proposed system can also generate a reliability map that indicates the manipulated regions in an scanned image.

关键词： Reliability Forensics Task analysis Tools Feature extraction Digital images Training

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Neuron segmentation using 3D wavelet integrated encoder-decoder network

arXiv

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arXiv 2021年

作者： Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen518060 China

Motivation: 3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task. Results: In this paper, we propose a 3D wavelet and deep learning based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes;the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction. © 2021, CC BY.

关键词： Neurons

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SCSQ-MDD: A Detection Approach for Multi-Style Strokes of Chinese Characters

SSRN

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SSRN 2023年

作者： Zhou, Tian Xie, Wu Zhang, Huimin Fan, Yong Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Science and Technology Guilin China

In the Chinese character writing task of the robotic arms, the stroke category and position information should be extracted by object detection. The detection algorithms based on predefined anchor frames have difficulty in resolving the differences among many different styles of Chinese character strokes. While the deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points having no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause the effectiveness of correlation calculations between reference points in Chinese strokes and their surrounding sampled points is limited. So that the speed of vector learning stroke features in the detection head is reduced. In view of this problem, a new detection method of multi-style strokes of Chinese characters via SCSQ-MDD (Simple Conditional Spatial Query Mask Deformable DETR) is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contribution and resample the points without contribution, so that the randomness of correlation calculation among reference points is solved. Secondly, by separating the content query and spatial query of the transformer deocder, the content embedding and spatial embedding can be separately focused on when cross-attention computations are performed. Thus the dependence of the prediction task on the content embedding is relaxed and the training process is simplified. Finally, the detection model without predefined anchor frames based on deformable DETR called SCSQ-MDD is constructed using the mask mechanism and the simple conditional spatial query mechanism, and trained and validated on a multi-style Chinese character stroke dataset

关键词： Object detection

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Plant Stem Segmentation Using Fast Ground Truth Generation

Plant Stem Segmentation Using Fast Ground Truth Generation

引用

IEEE Southwest Symposium on image Analysis and Interpretation

作者： Changye Yang Sriram Baireddy Yuhao Chen Enyu Cai Denise Caldwell Valérian Méline Anjali S. Iyer-Pascuzzi Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Iyer-Pascuzzi Lab Purdue University West Lafayette Indiana USA

ISBN: (数字)9781728157450

ISBN: (纸本)9781728157467

Accurately phenotyping plant wilting is important for understanding responses to environmental stress. Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting. Plant shape analysis can be enhanced by locating the stem, which serves as a consistent reference point during wilting. In this paper, we show that deep learning methods can accurately segment tomato plant stems. We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach. Experimental results show the viability of both our proposed ground truth approach and deep learning based stem segmentation.

关键词： image segmentation Splines (mathematics) Machine learning Diseases Training Task analysis Shape

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