scholarly journals Microarray Image Segmentation Using Clustering Methods

2010 ◽  
Vol 15 (2) ◽  
pp. 240-247 ◽  
Author(s):  
Volkan Uslan ◽  
Ihsan Bucak
Keyword(s):  
Image Segmentation ◽  
Clustering Methods ◽  
Microarray Image

scholarly journals A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

2021 ◽  
Author(s):  
Himanshu Mittal ◽  
Avinash Chandra Pandey ◽  
Mukesh Saraswat ◽  
Sumit Kumar ◽  
Raju Pal ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Performance Parameters ◽  
Clustering Methods ◽  
Benchmark Datasets ◽  
Comprehensive Survey

scholarly journals IMAGE SEGMENTATION AND OBJECT SELECTION BASED ON MULTI-THRESHOLD PROCESSING

2019 ◽  
Vol 22 (3) ◽  
pp. 24-35 ◽  
Author(s):  
Vladimir Yu. Volkov ◽  
Oleg A. Markelov ◽  
Mikhail I. Bogachev
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Test Methods ◽  
Object Identification ◽  
Detection Methods ◽  
Clustering Methods ◽  
Object Selection ◽  
Advantages And Disadvantages ◽  
Infrared Cameras ◽  
Threshold Processing

Introduction. Detection, isolation, selection and localization of variously shaped objects in images are essential in a variety of applications. Computer vision systems utilizing television and infrared cameras, synthetic aperture surveillance radars as well as laser and acoustic remote sensing systems are prominent examples. Such problems as object identification, tracking and matching as well as combining information from images available from different sources are essential. Objective. Design of image segmentation and object selection methods based on multi-threshold processing. Materials and methods. The segmentation methods are classified according to the objects they deal with, including (i) pixel-level threshold estimation and clustering methods, (ii) boundary detection methods, (iii) regional level, and (iv) other classifiers, including many non-parametric methods, such as machine learning, neural networks, fuzzy sets, etc. The keynote feature of the proposed approach is that the choice of the optimal threshold for the image segmentation among a variety of test methods is carried out using a posteriori information about the selection results. Results. The results of the proposed approach is compared against the results obtained using the well-known binary integration method. The comparison is carried out both using simulated objects with known shapes with additive synthesized noise as well as using observational remote sensing imagery. Conclusion. The article discusses the advantages and disadvantages of the proposed approach for the selection of objects in images, and provides recommendations for their use.

Review of Fuzzy Image Segmentation Techniques

2011 ◽  
pp. 282-313 ◽  
Author(s):  
Gour C. Karmakar ◽  
Laurence Dooley ◽  
Mahbubhur Rahman Syed
Keyword(s):  
Image Segmentation ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Clustering Methods ◽  
Comprehensive Overview ◽  
Domain Specific ◽  
Soft Computing Techniques ◽  
Fuzzy Clustering Methods ◽  
Fuzzy Image ◽  
Multimodal Images

This chapter provides a comprehensive overview of various methods of fuzzy logic-based image segmentation techniques. Fuzzy image segmentation techniques outperform conventional techniques, as they are able to evaluate imprecise data as well as being more robust in noisy environment. Fuzzy clustering methods need to set the number of clusters prior to segmentation and are sensitive to the initialization of cluster centers. Fuzzy rule-based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. It is also capable of drawing partial inference using fuzzy IF-THEN rules. It has been also intensively applied in medical imaging. These rules are, however, application-domain specific and very difficult to define either manually or automatically that can complete the segmentation alone. Fuzzy geometry and thresholding-based image segmentation techniques are suitable only for bimodal images and can be applied in multimodal images, but they don’t produce a good result for the images that contain a significant amount of overlapping pixels between background and foreground regions. A few techniques on image segmentation based on fuzzy integral and soft computing techniques have been published and appear to offer considerable promise.

cDNA microarray image segmentation using root signals

2006 ◽  
Vol 16 (2) ◽  
pp. 51-64 ◽  
Author(s):  
Rastislav Lukac ◽  
Konstantinos N. Plataniotis
Keyword(s):  
Image Segmentation ◽  
Cdna Microarray ◽  
Microarray Image ◽  
Root Signals

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

2014 ◽  
Vol 3 (1) ◽  
pp. 16 ◽  
Author(s):  
Minakshi Sharma ◽  
Saourabh Mukherjee
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Inference ◽  
Research Work ◽  
Diagnostic Technique ◽  
Clustering Methods ◽  
Inference System ◽  
Fuzzy C Means ◽  
Soft Computing Techniques

<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties.  Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques  (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus  Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of  Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.</p>

scholarly journals Automatic clustering method to segment COVID-19 CT images

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244416
Author(s):  
Mohamed Abd Elaziz ◽  
Mohammed A. A. Al-qaness ◽  
Esraa Osama Abo Zaid ◽  
Songfeng Lu ◽  
Rehab Ali Ibrahim ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Threshold Value ◽  
Ct Images ◽  
Optimal Number ◽  
Clustering Methods ◽  
Segmentation Method ◽  
Density Peaks ◽  
Automatic Clustering ◽  
Proposed Model ◽  
Density Peaks Clustering

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

scholarly journals Automatic microarray image segmentation with clustering-based algorithms

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0210075 ◽  
Author(s):  
Guifang Shao ◽  
Dongyao Li ◽  
Junfa Zhang ◽  
Jianbo Yang ◽  
Yali Shangguan
Keyword(s):  
Image Segmentation ◽  
Microarray Image
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