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Spatial Modification in the Parameters of Mountain Image Clustering Algorithm
2019
مجلة النهرين للعلوم الهندسية
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Our proposed method used to overcome the drawbacks of computing values parameters in the mountain algorithm to image clustering. ...
of Mountain Image Clustering Algorithm. ...
In the paper [5] proposed Improved Mountain Clustering version-2 (IMC-2) based medical image segmentation. ...
doi:10.29194/njes.22010055
fatcat:rzhk677kxjhcxpxb46wm3zy234
Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization
2020
PLoS ONE
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In this paper, the proposed approach was compared with various unsupervised image segmentation techniques on various image segmentation benchmarks. ...
Various algorithms have been developed for image segmentation, but clustering algorithms play an important role in the segmentation of digital images. ...
An improved version of the AFHA algorithm was developed by Yu et al. [32] called Improved AFHA (IAFHA). ...
doi:10.1371/journal.pone.0240015
pmid:33091007
fatcat:gl7itc2kofbybio6les5wr7muq
Energy based Methods for Medical Image Segmentation
2016
International Journal of Computer Applications
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It needs correct segmentation connected with medical images regarding correct diagnosis. An efficient method assures good quality segmentation of medical images. ...
This study is useful for determining the appropriate use of the image segmentation methods and for improving their accuracy and performance and also works on the main objective, which is designing new ...
Figures are showing segmentation results Fig 2(a) using the active contour method without edges, Fig. 2(b) using the improved Chan Vese method, Fig. 2(c) using the Active contour method by local image ...
doi:10.5120/ijca2016910808
fatcat:2nm5bgqg55blbmrtnrjdotpbdi
Emerging Applications of Bio-Inspired Algorithms in Image Segmentation
2021
Electronics
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Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. ...
This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms ( ...
Yearly publication of the bio-inspired based image segmentation (2003-2021).
Table 2 . 2 PSO-based image-segmentation techniques (published from 2003 to 2020). ...
doi:10.3390/electronics10243116
fatcat:iusw5vsfxnbbbb47erqpmqaavy
Automative Multi Classifier Framework for Medical Image Analysis
2015
Research Journal of Applied Sciences Engineering and Technology
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Medical image processing is the technique used to create images of the human body for medical purposes. ...
Several researches have done in this area to enhance the techniques for medical image processing. ...
A new enhanced mountain clustering technique is planned, by Verma and Hanmandlu (2007) which is contrast with a few of the existing techniques such as FCM, K-Means, EM and Modified Mountain Clustering ...
doi:10.19026/rjaset.9.2598
fatcat:j5lmvc6kivh5ddkma5lqihmib4
A Novel Brain Tumor Detection and Coloring Technique from 2D MRI Images
2022
Applied Sciences
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Moreover, the colorization approach based on luminance and pixel matrix after segmentation and ROI selection is beneficial due to better PSNR and SSIM values and visible contrast improvement. ...
Our proposed algorithm works with less processing overhead and uses less time than those of the industry's previously used color transfer method. ...
The gray toning technique improves the photograph's results. After the skull is removed, the image is improved. ...
doi:10.3390/app12115744
fatcat:xgddleahpbf5tpq2d3onn2pgg4
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System
2012
Journal of Medical Signals & Sensors
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Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. ...
This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ...
The determined clusters centers, which are obtained from mountain clustering algorithm, are used to initialize an ANFIS. ...
pmid:23493054
pmcid:PMC3592505
fatcat:e52eavs36zhfhihnbfoyf5cigi
Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images
2010
International Journal of Computers Communications & Control
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In this paper, we present an improvement non-parametric background modeling and foreground segmentation. ...
In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. ...
[10] propose a new improved mountain clustering technique, which is compared with some of the existing techniques such as K-Means, FCM, EM and Modified Mountain Clustering. ...
doi:10.15837/ijccc.2010.2.2474
fatcat:ksdz4lz2bbenjjp4vvlicgubm4
Evaluation of modified adaptive k-means segmentation algorithm
2019
Computational Visual Media
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However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). ...
Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. ...
This method is not widely applicable to medical image segmentation. ...
doi:10.1007/s41095-019-0151-2
fatcat:ilp32rgejfcqvgzoohtcdf5kru
Natural Language Processing through the Subtractive Mountain Clustering Algorithm - A Medication Intake Chatbot
2021
International Journal on Natural Language Computing
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The implemented algorithm version allosws for the association of a set of words into clusters. ...
Thence, the design of an interactive aid system, preferably using natural language, to help the older population with medication is in demand. ...
Some common applications for clustering include market segmentation, social network analysis, search result grouping, medical imaging, image segmentation or anomaly detection. ...
doi:10.5121/ijnlc.2021.10503
fatcat:75siree3zzcnlarcvzlgzpl22e
Coarse Segmentation with GDD Clustering using Color and Spatial Data
2020
IEEE Access
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A merging process of these two outputs is implemented to improve the final segmentation. ...
In this study, a novel segmentation algorithm which incorporates downscaling and clustering methods has been developed to find consistent coarse regions in a given input image. ...
Perceptual weights are studied in segmentation to improve region-based image segmentation using pooling strategies [7] . ...
doi:10.1109/access.2020.3015377
fatcat:6dxb2bqfbfclra7orgndmuzoei
Cell Lineage Construction of Neural Progenitor Cells
2014
International Journal of Computer Applications
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In this paper we have described a system for tracking neural progenitor cells in a sequence of images using multiple matching object method based on modified mahalanobis algorithm. ...
In order to construct the cell lineage it is very useful to have an efficient cell tracking system. ...
Preprocessing Mathematical morphology [19] is well suited to biological and medical image analysis. This system uses morphological techniques for shape analysis and filtering. ...
doi:10.5120/15565-4370
fatcat:4smdhebmrbdavkfaip7sezkixe
Hierarchical Blurring Mean-Shift
[chapter]
2011
Lecture Notes in Computer Science
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In recent years, various Mean-Shift methods were used for filtration and segmentation of images and other datasets. ...
In this paper, we propose an improved segmentation method that we call Hierarchical Blurring Mean-Shift. ...
As a source image, the famous Lena gray-scale photo in 256 × 256 pixels was used and for speed tests, original 512 × 512 version and resized versions were used too. ...
doi:10.1007/978-3-642-23687-7_21
fatcat:4rhcmptjdraavlilbufxo56xpm
Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms
2002
Medical Physics (Lancaster)
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Our purpose in this study is to develop a parameter optimization technique for the segmentation of suspicious microcalcification clusters in digitized mammograms. ...
At a sensitivity rate of 93.2%, there was an average of 0.8 false positive clusters per image. The results are very comparable with those taken using our previously published rule-based method. ...
The membership functions used for scaling the histogram features were different versions of the S function given below: A ͑ y ͒ϭT͑ ␣,͒ϭ Ά 0, рϪ␣, 2 ͩ ϩ␣ 2␣ ͪ 2 , Ϫ␣Ͻр0, 1Ϫ2 ͩ Ϫ␣ 2␣ ͪ 2 , ␣ϾϾ0, 1, ...
doi:10.1118/1.1460874
pmid:11998828
fatcat:kaoebreyvrbsvdhghwkcg5gziq
A review of original articles published in the emerging field of radiomics
2020
European Journal of Radiology
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Logistic regression and LASSO were the two most commonly used techniques for feature selection. ...
The following information was retrieved for each article: radiological subspecialty, imaging technique(s), machine learning technique(s), sample size, study setting and design, statistical result(s), study ...
Using extensive medical imaging features extracted from regions of interest (ROIs) on medical images for the purpose of heterogeneity assessments, radiomics has demonstrated its value for improving clinical ...
doi:10.1016/j.ejrad.2020.108991
pmid:32334372
fatcat:vmhiihdwofaznfndnxinbkugqy
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