Anatomic compartments extraction from diffusion medical images using factorial analysis and K-means clustering methods: a combined analysis tool

2021 ◽  
Author(s):  
Hadjira Medeghri ◽  
Sid Ahmed Sabeur
Keyword(s):  
Medical Images ◽  
Factorial Analysis ◽  
Analysis Tool ◽  
Clustering Methods ◽  
Combined Analysis

R*-Tree Based Similarity and Clustering Analysis for Images

2011 ◽  
pp. 50-61
Author(s):  
Jiaxiong Pi ◽  
Yong Shi ◽  
Zhengxin Chen
Keyword(s):  
Cluster Analysis ◽  
Spatial Data ◽  
Image Data ◽  
Multimedia Retrieval ◽  
Spatial Proximity ◽  
Similarity Analysis ◽  
Analysis Tool ◽  
Clustering Methods ◽  
Hierarchical Clustering Methods ◽  
And Cluster Analysis

Image content analysis plays an important role for adaptive multimedia retrieval. In this chapter, the authors present their work on using a useful spatial data structure, R*-tree, for similarity analysis and cluster analysis of image contents. First, they describe an R*-tree based similarity analysis tool for similarity retrieval of images. They then move on to discuss R*-tree based clustering methods for images, which has been a tricky issue: although objects stored in the same R* tree leaf node enjoys spatial proximity, it is well-known that R* trees cannot be used directly for cluster analysis. Nevertheless, R* tree’s indexing feature can be used to assist existing cluster analysis methods, thus enhancing their performance of cluster quality. In this chapter, the authors report their progress of using R* trees to improve well-known K-means and hierarchical clustering methods. Based on R*-Tree’s feature of indexing Minimum Bounding Box (MBB) according to spatial proximity, the authors extend R*-Tree’s application to cluster analysis containing image data. Two improved algorithms, KMeans-R and Hierarchy-R, are proposed. Experiments have shown that KMeans-R and Hierarchy-R have achieved better clustering quality.

scholarly journals K-Neighbourhood Analysis: A Method for Understanding SMLM Images as Compositions of Local Neighbourhoods

2021 ◽  
Vol 1 ◽  
Author(s):  
Kristen Feher ◽  
Matthew S. Graus ◽  
Simao Coelho ◽  
Megan V. Farrell ◽  
Jesse Goyette ◽  
...  
Keyword(s):  
Single Molecule ◽  
Principal Component ◽  
Spatial Arrangement ◽  
Point Pattern ◽  
Analysis Tool ◽  
Nearest Neighbour ◽  
Clustering Methods ◽  
Neighbourhood Analysis ◽  
Localisation Microscopy ◽  
Novel Algorithm

Single molecule localisation microscopy (SMLM) is a powerful tool that has revealed the spatial arrangement of cell surface signalling proteins, producing data of enormous complexity. The complexity is partly driven by the convolution of technical and biological signal components, and partly by the challenge of pooling information across many distinct cells. To address these two particular challenges, we have devised a novel algorithm called K-neighbourhood analysis (KNA), which emphasises the fact that each image can also be viewed as a composition of local neighbourhoods. KNA is based on a novel transformation, spatial neighbourhood principal component analysis (SNPCA), which is defined by the PCA of the normalised K-nearest neighbour vectors of a spatially random point pattern. Here, we use KNA to define a novel visualisation of individual images, to compare within and between groups of images and to investigate the preferential patterns of phosphorylation. This methodology is also highly flexible and can be used to augment existing clustering methods by providing clustering diagnostics as well as revealing substructure within microclusters. In summary, we have presented a highly flexible analysis tool that presents new conceptual possibilities in the analysis of SMLM images.

Framelet transform and fuzzy clustering-based intelligent technique for speckle noise removal in ultrasound images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Praveen Kumar Lendale ◽  
N.M. Nandhitha
Keyword(s):  
Performance Indicators ◽  
Medical Images ◽  
Speckle Noise ◽  
Noise Removal ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Edge Preservation ◽  
Content Type ◽  
Intelligent Technique

PurposeSpeckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.Design/methodology/approachThe work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.FindingsThe proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.Originality/valueFuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

scholarly journals XQCAT: eXtra Quark Combined Analysis Tool

2015 ◽  
Vol 197 ◽  
pp. 263-275 ◽  
Author(s):  
D. Barducci ◽  
A. Belyaev ◽  
M. Buchkremer ◽  
J. Marrouche ◽  
S. Moretti ◽  
...  
Keyword(s):  
Analysis Tool ◽  
Combined Analysis

Fuzzy-Based Medical Image Processing

2015 ◽  
pp. 666-681
Author(s):  
G. R. Sinha
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Hybrid Approach ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Clustering Methods ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Medical Image Processing (MIP) is a set of tools applied over medical images, which consists of several components such as image acquisition, enhancement, segmentation, restoration, etc. The most important component of MIP is medical image segmentation used in Computer-Aided Diagnosis (CAD) systems used for detection of abnormalities in medical images. This chapter presents an overview and the importance of soft computing techniques in solving the problems of medical imaging. The authors highlight the significance of fuzzy-based clustering and similar methods for MIP and its applications. Fuzzy C-Means Clustering Method (FCM) is found the most suitable method among existing clustering methods for medical images. FCM addresses the problem of over-segmentation and helps in improvement of diagnosis accuracy. Application of optimization tool causes the reduction of execution time. A comparison of fuzzy-based methods over conventional methods suggests that neuro-fuzzy system as hybrid approach is an efficient method for medical image analysis.

scholarly journals eIMES 3D: An innovative medical images analysis tool to support diagnostic and surgical intervention

2017 ◽  
Vol 110 ◽  
pp. 459-464 ◽  
Author(s):  
P. Iaquinta ◽  
M. Iusi ◽  
L. Caroprese ◽  
S. Turano ◽  
S. Palazzo ◽  
...  
Keyword(s):  
Surgical Intervention ◽  
Medical Images ◽  
Analysis Tool

scholarly journals An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3903 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
Mohit Mittal ◽  
Kumaresan P. ◽  
P. A. Pattanaik ◽  
Zbigniew Marszalek
Keyword(s):  
Breast Cancer ◽  
Classification System ◽  
Medical Images ◽  
Research Work ◽  
Support Vector ◽  
Clustering Methods ◽  
Analysis Society ◽  
Fuzzy Clustering Methods ◽  
Detection And Diagnosis

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

scholarly journals A Comprehensive Study on the Importance of the Elbow and the Silhouette Metrics in Cluster Count Prediction for Partition Cluster Models

2021 ◽  
Vol 11 (4) ◽  
pp. 3792-3806
Author(s):  
A.A. Abdulnassar ◽  
Latha R. Nair
Keyword(s):  
Clustering Algorithms ◽  
Large Data ◽  
Cluster Models ◽  
Data Repository ◽  
Data Sets ◽  
Analysis Tool ◽  
Clustering Methods ◽  
K Value ◽  
Statistical Analysis Tool ◽  
Partition Clustering

Proper selection of cluster count gives better clustering results in partition models. Partition clustering methods are very simple as well as efficient. Kmeans and its modified versions are very efficient cluster models and the results are very sensitive to the chosen K value. The partition clustering algorithms are more suitable in applications where the data are arranged in a uniform manner. This work aims to evaluate the importance of assigning cluster count value in order to improve the efficiency of partition clustering algorithms using two well known statistical methods, the Elbow method and the Silhouette method. The performance of the Silhouette method and Elbow method are compared with different data sets from the UCI data repository. The values obtained using these methods are compared with the results of cluster performance obtained using the statistical analysis tool Weka on the selected data sets. Performance was evaluated on cluster efficiency for small and large data sets by varying the cluster count values. Similar results obtained from the three methods, the Elbow method, the Silhouette method and the clustering by Weka. It was also observed that the fast reduction in clustering efficiency for small changes in cluster count when the cluster count is small.

Fuzzy-Based Medical Image Processing

2015 ◽  
pp. 45-61 ◽  
Author(s):  
G. R. Sinha
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Hybrid Approach ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Clustering Methods ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Medical Image Processing (MIP) is a set of tools applied over medical images, which consists of several components such as image acquisition, enhancement, segmentation, restoration, etc. The most important component of MIP is medical image segmentation used in Computer-Aided Diagnosis (CAD) systems used for detection of abnormalities in medical images. This chapter presents an overview and the importance of soft computing techniques in solving the problems of medical imaging. The authors highlight the significance of fuzzy-based clustering and similar methods for MIP and its applications. Fuzzy C-Means Clustering Method (FCM) is found the most suitable method among existing clustering methods for medical images. FCM addresses the problem of over-segmentation and helps in improvement of diagnosis accuracy. Application of optimization tool causes the reduction of execution time. A comparison of fuzzy-based methods over conventional methods suggests that neuro-fuzzy system as hybrid approach is an efficient method for medical image analysis.

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