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Advance techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

Advances in Computed Tomography 2021年第1期10卷 1-10页

作者： S. Zimeras Department of Statistics and Actuarial-Financial Mathematics University of the Aegean Karlovassi Samos

Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.

关键词： segmentation techniques Big Data Analysis Contour Model Shape Model Radial Basis Function Active Contours Snakes

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Image segmentation Through Fuzzy Clustering: A Survey 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Jain, Rashi Sharma, Rama Shankar Rajasthan Tech Univ Kota Rajasthan India

ISBN: (纸本)9789811307614;9789811307607

In modern years, image processing is a vast area for research. Image segmentation is the most popular part of image processing which divides the image into number of segments to analyze the better quality of image. It is used to detect objects and boundaries in images. Main goal of image segmentation is to change the representation of image into the more meaningful regions. Image segmentation results in a set of segments that covers the whole image or curves that are extracted from the image. In this paper, different image segmentation techniques and algorithms are presented, and clustering is one of the techniques that is used for segmentation. Fuzzy c-means clustering (FCM) algorithm is presented in this paper for image segmentation. On the basis of literature reviewed, several problems are analyzed in previously FCM, and the problems have been overcome by modifying the objective function of the previously FCM, and spatial information is incorporated in objective function of FCM. Fuzzy c-mean clustering is also known as soft clustering. The techniques that are explained in this survey are segmentation of the noisy medicinal images along spatial probability, histogram-based FCM, improved version of fuzzy c-means (IFCM), fuzzy possibilistic c-means (FPCM), possibilistic c-means (PCM), and possibilistic fuzzy c-means (PFCM) algorithms are to be explained in further sections on the basis of literature review. Moreover, several recent works on fuzzy c-means using clustering till 2017 are presented in this survey.

关键词： segmentation segmentation techniques Fuzzy c-means clustering (FCM) PCM FPCM PFCM Membership function

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Automatic White Blood Cell Counting Approach Based on Flower Pollination Optimization Multilevel Thresholoding Algorithm 4th

Automatic White Blood Cell Counting Approach Based on Flower...

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4th International Conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Mohamed, Shahd T. Ebeid, Hala M. Hassanien, Aboul Ella Tolba, Mohamed F. Ain Shams Univ Fac Comp & Informat Sci Cairo Egypt Cairo Univ Fac Comp & Informat Giza Egypt SRGE Giza Egypt

ISBN: (纸本)9783319990101;9783319990095

This paper presents an swarm optimization approach based on flower pollination optimization algorithm for multilevel thresholding by the criteria of Otsu minimizes the weighted within-class variance to make the optimal thresholding more effective. An application of microscopic white blood cell imaging has been chosen and the proposed approach has been applied to see their ability and accuracy to segment and count the blood cells. An adaptive watershed segmentation algorithm was applied that depends on a mask created from the required microscopic image to detect the minima points for segmenting the overlapped cells. The cell counting process depends on labeling the connected regions of the segmented binary image and count the labeled cells. The proposed approach archives promised results with respect to quality measures of accuracy, peak to signal-to-noise ratio (PSNR) and the root mean square error (RMSE) on microscopic images. Experimental results are recorded for the proposed approach over ten selected different images with accuracy of 98.4% that present better accuracy over the manual traditional techniques.

关键词： Blood microscopic image White blood cells Multilevel thresholding Watershed segmentation techniques

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Real-Time segmentation and Feature Extraction of Electromyography: Towards Control of a Prosthetic Hand

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IFAC-PapersOnLine 2017年第2期50卷 151-156页

作者： Eisenberg, Gabriel D. Fyvie, Kyle G.H.M. Mohamed, Abdul-Khaaliq University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa

Surface electromyographic (sEMG) signals can be used as inputs to control a myoelectric prosthetic hand. This requires the discrimination of sEMG associated with different hand movements. The signals for key unilateral hand movements such as wrist extension, wrist flexion, and power grip are similar, making the classification and control of these hand movements challenging. This preliminary study explores the viability of classifying the sEMG signals of these hand movements in real-time, in order to control a software simulation of a prosthetic hand. sEMG data was recorded from two bipolar electrodes for offline classifier training purposes. A novel segmentation technique was used to separate the muscle contraction and rest periods of the sEMG time-series data. A time-frequency algorithm was then applied for the first time to extract sEMG-based features from the segmented data. Features were used to train four support vector machines offline, in a one-versus-all architecture. The classification system was tested offline and in real-time. The system yielded accuracies of 89.39% and 84.93% for offline and real-time testing respectively. Real-time classification was done in 10.2 ms when processing 0.5 s of input sEMG data. This shows that the system is accurate and computationally efficient, even with limited electrodes. Thus, it can serve as a foundation for further work in the implemented segmentation and feature extraction methods, for an inexpensive alternative to commercial myoelectric prosthetic devices. © 2017

关键词： Biomedical signal processing Computer software Electrodes Electromyography Extraction Feature extraction Image segmentation Prosthetics Support vector machines Classification system Classifier training Computationally efficient Feature extraction methods Real time segmentation segmentation techniques sEMG Software simulation

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Temporal and spatial changes of land use in rare metal mining areas of Rwanda

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2017年第8期31卷 519-529页

作者： Lehmann, Dieter Brinkmann, Katja Diogo, Rodrigue V. C. Buerkert, Andreas Nurtingen Geislingen Univ Appl Sci IT Serv Nurtingen Germany Univ Kassel Organ Plant Prod & Agroecosyst Res Witzenhausen Germany

To analyse the effects of mining activities on land use and land cover changes in the Gatumba mining district of Rwanda, aerial photos from 1958, 1974 and 2009 were classified using segmentation approaches. Drastic land cover changes were observed in the study region with the highest mining activities in 1974. From 1958 to 1974, woodland decreased by 54% and cropland increased rapidly. From 1974 to 2009 the process reversed, with woodland increasing while cropland decreased by 51%. This was mainly due to enforced legal regulations of the Government of Rwanda to control land degradation and a decline of mining activities.

关键词： Coltan mining Rwanda land cover changes historical aerial photographs segmentation techniques

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Recent Advances in Image Processing techniques for Automated Harvesting Purposes: A Review

Recent Advances in Image Processing Techniques for Automated...

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Intelligent Systems Conference (IntelliSys)

作者： Pereira, Carlos S. Morais, Raul Reis, Manuel J. C. S. Inst Politecn Porto Escola Super Tecnol & Gestao Rua Curral P-4610156 Felgueiras Portugal INESC TEC UTAD P-5001801 Vila Real Portugal IEETA UTAD P-5001801 Vila Real Portugal

ISBN: (纸本)9781509064359

Image processing has been proved to be an effective tool for analysis in various human activity areas, namely, agricultural applications. Interpreting a digital color image of fruit orchard captured in field environment is extremely challenging due to adverse weather conditions, luminance variability and the presence of dust, insects and other unavoidable image noises. The purpose of this survey is to categorize and briefly review the literature on computer analysis of fruit images in agricultural operations, which comprises more than 60 papers published in the last 10 years. With the aim to perform applied research in agricultural imaging, this paper intends to focus on advanced image processing and analysis techniques used in applications for detection and classifications of fruits, developed in the last decade. For the reviewed techniques, some performance evaluation metrics achieved in various experiments are emphasized to help the researchers when making choices and develop new computer vision applications in fruit images.

关键词： Computer vision segmentation techniques fruit characterization classification methods precision agriculture machine learning pattern recognition

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Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation

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NEUROIMAGE 2016年第0期129卷 1-14页

作者： Schoemaker, Dorothee Buss, Claudia Head, Kevin Sandman, Curt A. Davis, Elysia P. Chakravarty, M. Mallar Gauthier, Serge Pruessner, Jens C. McGill Univ McGill Ctr Studies Aging Montreal PQ Canada McGill Univ Douglas Hosp Res Ctr Dept Psychiat Montreal PQ Canada Univ Calif Irvine Irvine CA USA Charite Berlin Germany Univ Denver Denver CO 80208 USA McGill Univ Dept Biomed Engn Montreal PQ Canada

The volumetric quantification of brain structures is of great interest in pediatric populations because it allows the investigation of different factors influencing neurodevelopment. FreeSurfer and FSL both provide frequently used packages for automatic segmentation of brain structures. In this study, we examined the accuracy and consistency of those two automated protocols relative to manual segmentation, commonly considered as the "gold standard" technique, for estimating hippocampus and amygdala volumes in a sample of preadolescent children aged between 6 to 11 years. The volumes obtained with FreeSurfer and FSL-FIRST were evaluated and compared with manual segmentations with respect to volume difference, spatial agreement and between-and within-method correlations. Results highlighted a tendency for both automated techniques to overestimate hippocampus and amygdala volumes, in comparison to manual segmentation. This was more pronounced when using FreeSurfer than FSL-FIRST and, for both techniques, the over estimation was more marked for the amygdala than the hippocampus. Pearson correlations support moderate associations between manual tracing and FreeSurfer for hippocampus (right r = 0.69, p < 0.001;left r = 0.77, p < 0.001) and amygdala (right r = 0.61, p < 0.001;left r = 0.67, p < 0.001) volumes. Correlation coefficients between manual segmentation and FSL-FIRST were statistically significant (right hippocampus r = 0.59, p < 0.001;left hippocampus r = 0.51, p < 0.001;right amygdala r = 0.35, p < 0.001;left amygdala r = 0.31, p < 0.001) but were significantly weaker, for all investigated structures. When computing intraclass correlation coefficients between manual tracing and automatic segmentation, all comparisons, except for left hippocampus volume estimated with FreeSurfer, failed to reach 0.70. When looking at each method separately, correlations between left and right hemispheric volumes showed strong associations between bilateral hippocampus and bilater

关键词： segmentation techniques Pediatric population Hippocampus Amygdala FSL-FIRST FreeSurfer

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Pixel Variation Problem Identification in Image segmentation for big Image Data set in Cloud Platform

Pixel Variation Problem Identification in Image segmentation...

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IEEE International Conference on Advances in Computer Applications (ICACA)

作者： Santra, Soumen Mali, Kalyani Techno India Coll Technol Dept Comp Applicat Kolkata India Univ Kalyan Kalyani Dept CSE Nadia India

ISBN: (纸本)9781509037704

This paper describes problem regarding pixel identification for image segmentation methodologies for big image dataset for real robust applications. For medical, urban or satellite images, segmentation process can done through high computing nodes for high I/O performance, low computing time by considering low overhead of transmission rate. Multi thresholding segmentation may apply for large images must conduct due to features of different computing nodes for different images. For heterogeneity after reduction of a big image data set into image set there is a lack of information regarding pixel may arise in cloud platform. This paper identified the problem and shown how that one occurs.

关键词： Big Dataset segmentation techniques Cloud Platform

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Investigating materials for breast nodules simulation by using segmentation and similarity analysis of digital images

Investigating materials for breast nodules simulation by usi...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Siqueira, Paula N. Marcomini, Karem D. Sousa, Maria A. Z. Schiabel, Homero EESC USP Dept Elect Engn Sao Carlos SP Brazil

ISBN: (纸本)9781628415025

The task of identifying the malignancy of nodular lesions on mammograms becomes quite complex due to overlapped structures or even to the granular fibrous tissue which can cause confusion in classifying masses shape, leading to unnecessary biopsies. Efforts to develop methods for automatic masses detection in CADe (Computer Aided Detection) schemes have been made with the aim of assisting radiologists and working as a second opinion. The validation of these methods may be accomplished for instance by using databases with clinical images or acquired through breast phantoms. With this aim, some types of materials were tested in order to produce radiographic phantom images which could characterize a good enough approach to the typical mammograms corresponding to actual breast nodules. Therefore different nodules patterns were physically produced and used on a previous developed breast phantom. Their characteristics were tested according to the digital images obtained from phantom exposures at a LORAD M-IV mammography unit. Two analysis were realized the first one by the segmentation of regions of interest containing the simulated nodules by an automated segmentation technique as well as by an experienced radiologist who has delineated the contour of each nodule by means of a graphic display digitizer. Both results were compared by using evaluation metrics. The second one used measure of quality Structural Similarity (SSIM) to generate quantitative data related to the texture produced by each material. Although all the tested materials proved to be suitable for the study, the PVC film yielded the best results.

关键词： Breast nodules phantom image nodule shape nodule contour segmentation techniques

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Vineyard Yield Estimation Based on the Analysis of High Resolution Images Obtained with Artificial Illumination at Night

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SENSORS 2015年第4期15卷 8284-8301页

作者： Font, Davinia Tresanchez, Marcel Martinez, Dani Moreno, Javier Clotet, Eduard Palacin, Jordi Univ Lleida Dept Comp Sci & Ind Engn Lleida 25001 Spain

This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV). The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1) the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate;and (2) the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate;the yield errors obtained were 16% and -17%, respectively.

关键词： precision agriculture yield estimation segmentation techniques color features

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