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Development of young oil palm tree recognition using Haar- based rectangular windows

IOP Conference Series: Earth and Environmental Science 2016年第1期37卷

作者： S Daliman S A R Abu-Bakar S H Md Nor Azam Computer Vision Video and Image Processing (CvviP) Research Lab Department of Electronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor MALAYSIA. Sime Darby Research Sdn. Bhd. Jalan Pulau Carey 42960 Pulau Carey Selangor MALAYSIA.

This paper presents development of Haar-based rectangular windows for recognition of young oil palm tree based on WorldView-2 imagery data. Haar-based rectangular windows or also known as Haar-like rectangular features have been popular in face recognition as used in Viola-Jones object detection framework. Similar to face recognition, the oil palm tree recognition would also need a suitable Haar-based rectangular windows that best suit to the characteristics of oil palm tree. A set of seven Haar-based rectangular windows have been designed to better match specifically the young oil palm tree as the crown size is much smaller compared to the matured ones. Determination of features for oil palm tree is an essential task to ensure a high successful rate of correct oil palm tree detection. Furthermore, features that reflects the identification of oil palm tree indicate distinctiveness between an oil palm tree and other objects in the image such as buildings, roads and drainage. These features will be trained using support vector machine (SVM) to model the oil palm tree for classifying the testing set and subimages of WorldView-2 imagery data. The resulting classification of young oil palm tree with sensitivity of 98.58% and accuracy of 92.73% shows a promising result that it can be used for intention of developing automatic young oil palm tree counting.

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Improving video-based resting heart rate estimation: A comparison of two methods

Improving video-based resting heart rate estimation: A compa...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Jeehyun Choe Dahjung Chung A. J. Schwichtenberg Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Department of Human Development and Family Studies Purdue University West Lafayette Indiana USA

Recent advancements in video processing make video-based heart rate (HR) estimation possible. Building on this burgeoning field, we adapted an existing video-based HR estimation method to produce more robust and accurate results. Specifically, we removed periodic signals from the recording environment by identifying (and removing) frequency clusters that are present the face region and background. This adaptive passband filter generates more accurate HR estimates and allows other applied filters to work more effectively.

关键词： Heart rate Face Skin Estimation Frequency estimation Passband Cutoff frequency

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Weighted boundary matching error concealment for HEVC using block partition decisions

Weighted boundary matching error concealment for HEVC using ...

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Asilomar Conference on Signals, Systems & computers

作者： Yan-Tsung Peng Pamela C. Cosman Department of Electrical and Computer Engineering Speech Image and Video Processing (Image and Video Coding) San Diego La Jolla CA

ISBN: (纸本)9781479982981

We propose a weighted boundary matching error concealment method for HEVC. It uses block partition decisions to improve a common block matching algorithm that finds blocks with the best matched boundaries from the previous frame to conceal the currently corrupted blocks. The block partition decisions from the co-located block of the corrupted one are exploited. For each partition, a summed boundary weight is computed; the one with the highest weight is chosen to be concealed next. Experimental results show the proposed method performs better than conventional error concealment methods objectively and subjectively.

关键词： video coding Partitioning algorithms Encoding Visualization PSNR Standards Error analysis

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Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR images of Epileptic Patients

Statistical Validation of Automatic Methods for Hippocampus ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Dario Pompili Hamid Soltanian-Zadeh Department of Electrical and Computer Engineering Rutgers University Medical Image Analysis Lab Radiology Department Henry Ford Health System Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the bench-mark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.

关键词： Segmentation Hippocampus Magnetic Resonance Imaging (MRI) Comparison Epilepsy

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Segmentation of bone from ADC maps in pelvis area using local level-set and prior information 11

Segmentation of bone from ADC maps in pelvis area using loca...

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2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

作者： Sanaei Nezhad, F. Saligheh Rad, H. Soltanian-Zadeh, H. School of Electrical and Computer Engineering College of Engineering Tehran Iran Department of Medical Physics and Biomedical Engineering Tehran University of Medical Sciences Tehran Iran Research Center for Molecular and Cellular Imaging Tehran University of Medical Sciences Tehran Iran Medical Image Analysis Laboratory Henry Ford Health System DetroitMI United States

ISBN: (纸本)9781467319591

Lack of anatomical details in diffusion weighted magnetic resonance images limits their utilization and treatment response monitoring, shadowing the useful information they contain. Contemporary methods of utilizing these images are based on manual selection of region of interest, raising concerns about susceptibility of manual ROI placement to human errors, and limiting the investigation in specific spatial regions. In contrary to the whole body bone marrow segmentation with the luxury to include all the diseased bone marrow, high profile analysis could be applied. In this paper, we propose an automatic method for segmentation of pelvic bone with possible bone metastasis in apparent diffusion coefficient (ADC) maps. This method is a multi-parametric registration-segmentation method, taking advantage of prior information of the pelvic anatomy. Intensity inhomogeneity in the bone structure caused by bone marrow metastasis challenges the segmentation process on anatomical MR images. Specifically, we first build a probability map which provides shape and volume constraints for the segmentation. Then, T1-weighted MR images are rigidly registered to the probability map, and then the registered T1-weighted image is non-rigidly registered to its' corresponding ADC maps. Finally, the probability map is coupled with a local level set framework for automatic pelvic bone segmentation of the T1-weighted images. The segmented bone is used as a mask on the ADC map. The method is validated on 10 pairs of ADC/T1 images of breast cancer with bone marrow metastases patients. Both quantitative and qualitative evaluation results demonstrate the validity of the proposed method. © 2014 IEEE.

关键词： Bone

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Support Vector Machine with Nonlinear-Kernel Optimization for Lateralization of Epileptogenic Hippocampus in MR images

Support Vector Machine with Nonlinear-Kernel Optimization fo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Fariborz Mahmoudi Hao Ying Hamid Soltanian-Zadeh Medical Image Analysis Lab. Radiology Department Henry Ford Health System Department of Electrical and Computer Engineering Wayne State University Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods;then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.

关键词： Magnetic Resonance Imaging (MRI) Hippocampus Epilepsy Lateralization Support Vector Machine (SVM)

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Atlas based intensity transformation of brain MR images

Atlas based intensity transformation of brain MR images

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3rd International Workshop on Multimodal Brain image analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical image Computing and computer Assisted Intervention, MICCAI 2013

作者： Roy, Snehashis Jog, Amod Carass, Aaron Prince, Jerry L. Image Analysis and Communication Laboratory Department of Electrical and Computer Engineering Johns Hopkins University United States

ISBN: (纸本)9783319021256

Magnetic resonance imaging (MRI) is a noninvasive modality that has been widely used to image the structure of the human brain. Unlike reconstructed x-ray computed tomography images, MRI intensities do not possess a calibrated scale, and the images suffer from wide variability in intensity contrasts due to scanner calibration and pulse sequence variations. Most MR image processing tasks use intensities as the principal feature and therefore the results can vary widely according to the actual tissue intensity contrast. Since it is difficult to control the MR scanner acquisition protocols in multi-scanner cross-sectional studies, results achieved using image processing tools are often difficult to compare in such studies. Similar issues can happen in longitudinal studies, as scanners undergo upgrades or improvements in pulse sequences, leading to new imaging sequences. We propose a novel probabilistic model to transform image contrasts by matching patches of a subject image to a set of patches from a multi-contrast atlas. Although the transformed images are not for diagnostic purpose, the use of such contrast transforms is shown for two applications, (a) to improve segmentation consistency across scanners and pulse sequences, (b) to improve registration accuracy between multi-contrast image pairs by transforming the subject image to the contrast of the reference image and then registering the transformed subject image to the reference image. Contrary to previous intensity transformation methods, our technique does not need any information about landmarks, pulse sequence parameters or imaging equations. It is shown to provide more consistent segmentation across scanners compared to state-of-the-art methods. © 2013 Springer International Publishing.

关键词： Magnetic resonance imaging

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IMART software for correction of motion artifacts in images collected in intravital microscopy

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Intravital 2014年第1期3卷 e28210页

作者： Kenneth W Dunn Kevin S Lorenz Paul Salama Edward J Delp Division of Nephrology School of Medicine Indiana University Indianapolis IN USA. Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA. Department of Electrical and Computer Engineering

Intravital microscopy is a uniquely powerful tool, providing the ability to characterize cell and organ physiology in the natural context of the intact, living animal. With the recent development of high-resolution microscopy techniques such as confocal and multiphoton microscopy, intravital microscopy can now characterize structures at subcellular resolution and capture events at sub-second temporal resolution. However, realizing the potential for high resolution requires remarkable stability in the tissue. Whereas the rigid structure of the skull facilitates high-resolution imaging of the brain, organs of the viscera are free to move with respiration and heartbeat, requiring additional apparatus for immobilization. In our experience, these methods are variably effective, so that many studies are compromised by residual motion artifacts. Here we demonstrate the use of IMART, a software tool for removing motion artifacts from intravital microscopy images collected in time series or in three dimensions.

关键词： fluorescence microscopy image analysis image processing image registration in vivo imaging intravital microscopy motion artifact multiphoton microscopy

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Covariance shrinking in active shape models with application to gyral labeling of the cerebral cortex

Covariance shrinking in active shape models with application...

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IEEE International Symposium on Biomedical Imaging

作者： Zhen Yang Aaron Carass Jerry. L. Prince Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University USA

ISBN: (纸本)9781467364560

Active shape models (ASMs) have been widely used in segmentation tasks in medical image analysis. Complex structures and a limited number of training samples can, however, result in the failure to capture the complete range of shape variations. Various modifications to the point distribution model (PDM) have been proposed to increase the flexibility of the model. Still model parameters are often determined empirically without respect to the underlying data structure. We explore shrinkage covariance estimation in building a PDM by combining the sample covariance matrix with a target covariance matrix estimated from a low-dimensional constrained model. Instead of using a global shrinkage intensity, we apply a spatially varying shrinkage intensity field to better adapt to the spatially varying characteristic of a complex shape. The parameters of the constrained model and the amount of shrinkage are determined in a data-driven fashion, so that the resulting distribution is optimized in representing the underlying data. The PDM, which we call SC-PDM, shows an increased flexibility in fitting new shapes and at the same time, is robust to noise. We demonstrate the effectiveness of using SC-PDM to label gyral regions on the human cerebral cortex.

关键词： Shape Brain models Computational modeling Adaptation models Covariance matrices Data models

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THREE DIMENSIONAL SEGMENTATION OF FLUORESCENCE MICROSCOPY imageS USING ACTIVE SURFACES

THREE DIMENSIONAL SEGMENTATION OF FLUORESCENCE MICROSCOPY IM...

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IEEE International Conference on image Processing

作者： Kevin S. Lorenz Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 Department of Electrical and Computer Engineering Indiana University Indianapolis Indiana 46202 Division of Nephrology School of Medicine Indiana University Indianapolis Indiana 46202

ISBN: (纸本)9781479923427

Three dimensional image volumes collected using optical microscopy exhibit many characteristics that cause difficulty in segmentation. These include decreasing image contrast with increasing tissue depth, poor edge detail with regard to cellular structures, and limited spatial resolution. This paper describes a three dimensional segmentation method utilizing active surfaces to segment microscopy image volumes. We demonstrate this method on a three dimensional sequence of images acquired from stationary/stabilized kidney tissue of a rat. Results from this method are compared against a prior pseudo-three dimensional segmentation method that analyzes slices on a single image-by-image basis, as well as against another native three dimensional segmentation method.

关键词： image segmentation biomedical optical imaging optical microscopy Biomedical optical imaging optical microscopy image segmentation images Three Dimensions Fluorescence microscopy image sequences segmentation Segmentation Cellular Structures image CONTRAST

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