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Kernelized Weighted SUSAN based Fuzzy C-Means Clustering for Noisy Image Segmentation [article]

Satrajit Mukherjee, Bodhisattwa Prasad Majumder, Aritran Piplai, and Swagatam Das
2016 arXiv   pre-print
The paper proposes a novel Kernelized image segmentation scheme for noisy images that utilizes the concept of Smallest Univalue Segment Assimilating Nucleus (SUSAN) and incorporates spatial constraints  ...  These weights are used to vary the contributions of the different nuclei in the Kernel based framework.  ...  Fuzzy c-means (FCM) [8] [9] clustering partitions a dataset or a set of image pixels, into c pre-defined number of clusters and assigns fuzzy membership values to each image pixel for its tendency to  ... 
arXiv:1603.08564v1 fatcat:cv7dbftpdfepzgt26yo7tvm2vi

Smallest Univalue Segment Assimilating Nucleus approach to Brain MRI Image Segmentation using Fuzzy C-Means and Fuzzy K-Means Algorithms

AJALA Funmilola Alaba, AKANDE Noah Oluwatobi, ADEYEMO Isiaka Akinkunmi, Ogundokun Roseline Oluwaseun
2017 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY  
Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (FCM) segmentation algorithms were employed for segmenting brain MRI images acquired from four different MRI databases into their White Matter (WM), Gray  ...  This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms  ...  Based Fuzzzy C-Means (TEFCM), Robust Fuzzy C-means Based Kernel Function (RFCMK), Weighted Image Patch Based FCM (WIPFCM) and Kernel Weighted Fuzzy Local Information C-Means (KWFLICM).  ... 
doi:10.24297/ijct.v16i7.6170 fatcat:po4kjindx5htjhxyyu66mto64q

Automatic Crack Detection in Eggshell Based on SUSAN Edge Detector Using Fuzzy Thresholding

Meysam Siyah Mansoory, Meghdad Ashtiyani, Hossein Sarabadani
2011 Modern Applied Science  
These algorithms are based on Fuzzy thresholding and SUSAN edge detector.  ...  main input image with variable variance between 0.002 and 0.01; the accuracy of detection for proposed algorithm was 97% and 82%, also this algorithm had least value of error function (number of error  ...  Acknowledgment The authors would like to express their appreciation to the Islamic Azad University, Majlesi Branch for full support of the project.  ... 
doi:10.5539/mas.v5n6p117 fatcat:yhpbqyi63nb5lkmejtrmeadpvq

Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

Trushali Doshi, John Soraghan, Derek Grose, Kenneth MacKenzie, Lykourgos Petropoulakis, Lubomir M. Hadjiiski, Georgia D. Tourassi
2015 Medical Imaging 2015: Computer-Aided Diagnosis  
The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types  ...  Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP).  ...  data sets and Clinician for manual results. region2 region1  ... 
doi:10.1117/12.2081864 dblp:conf/micad/DoshiSGMP15 fatcat:ailv77xf3bfb7ob4s2p63ryomu

Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

R Preetha, Department of Electronics and Communication Engineering, SRM Institute of Science & Technology, S Vanaja, K Durga Devi, R Lathamanju, Nami Susan Kurian, Jacqulin Veda Jancy
2022 Indian Journal of Science and Technology  
The filtered MR image slices are segmented and classified to detect the tumor areas and the tumor pixels are subjected for 3D reconstruction.  ...  MR images are subjected to Rician noise which can be removed by a simple correction scheme, initiated to change the bias due to the Rician distribution of the noisy magnitude data.  ...  This paper presents the conventional fuzzy c -means (FCM) clustering algorithm for the segmentation of brain MR images.  ... 
doi:10.17485/ijst/v15i3.1633 fatcat:kodihfjhxzcjzalzs63rjdjpdm

Edge Measures Using Similarity Regions [chapter]

Maneesh K. Singh, Narendra Ahuja
2001 Foundations of Image Understanding  
To this end, we present two clustering algorithms based on cost function optimization.  ...  The second algorithm minimizes a Soft Clustering Evaluation Function (SCEF) to partition the image into clusters such that the mutual information between these clusters is minimized.  ...  Regions in the image pyramid are discovered as roots and the mean segment values are propagated down the links to each pixel at the finest level (base of the pyramid).  ... 
doi:10.1007/978-1-4615-1529-6_9 fatcat:vqjm5pllkjclphbwdkuugbp3pm

Smoothing vs. sharpening of colour images: Together or separated

Cristina Pérez-Benito, Samuel Morillas, Cristina Jordán, J. Alberto Conejero
2017 Applied Mathematics and Nonlinear Sciences  
It is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods.  ...  We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.  ...  After a segmentation, each pixel is processed with a weighted mean that uses bilateral weights of the corresponding cluster.  ... 
doi:10.21042/amns.2017.1.00025 fatcat:er7scz2fjrfsxfybtegvbelvey

Quantum Image Processing [article]

Alok Anand and Meizhong Lyu and Prabh Simran Baweja and Vinay Patil
2022 arXiv   pre-print
In particular, the rapidly increasing volume of image data as well as increasingly challenging computational tasks have become important driving forces for further improving the efficiency of image processing  ...  In quantum image processing, quantum image representation plays a key role, which substantively determines the kinds of processing tasks and how well they can be performed.  ...  The SUSAN area will reach a minimum while the nucleus lies on a corner point. 5) Fuzzy System [22] : The measure of "cornerness" for each pixel in the image is computed by fuzzy rules (represented as  ... 
arXiv:2203.01831v1 fatcat:vuwhkinfnzfzvjjnch723hejyy

Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging

Harini Eavani, Meng Kang Hsieh, Yang An, Guray Erus, Lori Beason-Held, Susan Resnick, Christos Davatzikos
2016 NeuroImage  
For this purpose, we use the Mixture-Of-Experts (MOE) framework, which combines unsupervised modeling of mixtures of distributions with supervised learning of classifiers.  ...  We found strong evidence for the presence of two subgroups of older adults, with similar age distributions in each subgroup, but different connectivity patterns associated with aging.  ...  We used a linear-SVM along with fuzzy c-means clustering to identify multiple subgroups in the heterogeneous population, along with the associated abnormal connectivity pattern for each subgroup.  ... 
doi:10.1016/j.neuroimage.2015.10.045 pmid:26525656 pmcid:PMC5460911 fatcat:knqmxjzurrfkde7vl73qgvoelu

A Survey on Shadow Detection Techniques in a Single Image

SARITHA MURALI, V.K GOVINDAN, SAIDALAVI KALADY
2018 Information Technology and Control  
Researchers have made effort to device techniques to locate and remove shadows from images and videos. This paper attempts to survey the various shadow detection algorithms for a single image.  ...  For the purpose of survey, the notable research work in the literature is classified under five major categories: invariant-based detection, feature-based detection, region-based detection, color model  ...  [6] CIE L*u*v* Segment features: area, border length, intensity with respect to neighbor segments, color ratio, brightness ratio -Fuzzy c-means clustering lvador et al.  ... 
doi:10.5755/j01.itc.47.1.15012 fatcat:u46ygjkwtvbcjie2dynjdq2ewm

A Survey on Contemporary Computer-Aided Tumor, Polyp, and Ulcer Detection Methods in Wireless Capsule Endoscopy Imaging [article]

Tariq Rahim, Muhammad Arslan Usman, Soo Young Shin
2019 arXiv   pre-print
Also, we have attempted to classify these methods based on their technical aspects. This paper also includes a potential proposal for joint classification of aforementioned three diseases.  ...  In this paper, we have presented a survey of contemporary computer-aided detection methods that take WCE images as input and classify those images in a diseased/abnormal or disease-free/ normal image.  ...  K-means clustering used to generate clusters having similar properties and the output of the Gabor filters was used to obtain a number of segmented regions based on the elevation levels.  ... 
arXiv:1910.00265v1 fatcat:cziq6sauuzaqpgpmca5pmgdwke

An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images

Marco Caballo, John M. Boone, Ritse Mann, Ioannis Sechopoulos
2018 Medical Physics (Lancaster)  
Finally, blood vessels are separated from fibroglandular tissue by a k-means clustering algorithm based on automatically extracted shape-based features.  ...  Methods: The proposed algorithm combines intensity-and region-based segmentation methods with energy minimizing splines and unsupervised data mining approaches for classifying and segmenting the different  ...  Komen Foundation for the Cure.  ... 
doi:10.1002/mp.12920 pmid:29676025 pmcid:PMC5997547 fatcat:w7aa2m47i5hy5bjaonvapc3mia

Recent advances in techniques for hyperspectral image processing

Antonio Plaza, Jon Atli Benediktsson, Joseph W. Boardman, Jason Brazile, Lorenzo Bruzzone, Gustavo Camps-Valls, Jocelyn Chanussot, Mathieu Fauvel, Paolo Gamba, Anthony Gualtieri, Mattia Marconcini, James C. Tilton (+1 others)
2009 Remote Sensing of Environment  
In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing.  ...  algorithms. j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / rs e S111 A.  ...  Susan L. Ustin and Michael E.  ... 
doi:10.1016/j.rse.2007.07.028 fatcat:yhl3syflhjhvbhwsgiuvxht3lu

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

Md Khaled Hasan, Md. Shamim Ahsan, Abdullah-Al-Mamun, S. H. Shah Newaz, Gyu Myoung Lee
2021 Electronics  
Face detection, which is an effortless task for humans, is complex to perform on machines.  ...  The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces.  ...  A sub-clustering hierarchical DBNN was explored for static models, and a fixed weight low pass filter was used for temporal prediction models.  ... 
doi:10.3390/electronics10192354 fatcat:oy7adwj6cjefnm66cn5kxrybni

CobWeb 1.0: machine learning toolbox for tomographic imaging

Swarup Chauhan, Kathleen Sell, Wolfram Rühaak, Thorsten Wille, Ingo Sass
2020 Geoscientific Model Development  
The CobWeb software package covers image processing and machine learning libraries of MATLAB® used for image enhancement and image segmentation operations, which are compiled into series of Windows-executable  ...  To overcome these problems, we have developed CobWeb 1.0, which is automated and explicitly tailored for accurate greyscale (multiphase) image segmentation using unsupervised and supervised machine learning  ...  We also thank Michael Kersten, Frieder Enzmann and his group at the Institute for Geoscience, Johannes-Gutenberg Universität Mainz, for providing high-resolution gas hydrate synchrotron data.  ... 
doi:10.5194/gmd-13-315-2020 fatcat:fgckjykv7reddj7zus5vwecneq
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