COMPARATIVE STUDY OF CLUSTERING ALGORITHMS IN ORDER TO VIRTUAL HISTOLOGY (VH) IMAGE SEGMENTATION

2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Zahra Rezaei ◽  
Mohd Daud Kasmuni ◽  
Ali Selamat ◽  
Mohd Shafry Mohd Rahim ◽  
Golnoush Abaei ◽  
...  
Keyword(s):  
Human Error ◽  
Color Image ◽  
Clustering Algorithms ◽  
Image Clustering ◽  
Clustering Methods ◽  
Color Code ◽  
Virtual Histology ◽  
Fuzzy C Means ◽  
Geometrical Features ◽  
Nonhierarchical Clustering

Atherosclerosis is the deadliest type of heart disease caused by soft or “vulnerable” plaque (VP) formation in the coronary arteries.  Recently, Virtual Histology (VH) has been proposed based on spectral analysis of Intravascular Ultrasound (IVUS) provides color code of coronary tissue maps. Based on pathophysiological studies, obtaining information about existence and extension of confluent pool’s component inside plaque is important. In addition, plaque components’ localization respect to the luminal border has major role in determining plaque vulnerability and plaque–stent interaction. Computational methods were applied to prognostic the pattern's structure of each component inside the plaque. The first step for post-processing of VH methodology to get further information of geometrical features is segmentation or decomposition. The medical imaging segmentation field has developed to assist cardiologist and radiologists and reduce human error in recent years as well. To perform color image clustering, several strategies can be applied which include traditional hierarchical and nonhierarchical. In this paper, we applied and compared four nonhierarchical clustering methods consists of Fuzzy C-means (FCM), Intuitionistic Fuzzy C-means (IFCM), K-means and SOM artificial neural networks in order to automate segmentation of the VH-IVUS images.  

Analysis of Segmentation Parameters Effect towards Parallel Processing Time on Fuzzy C Means Algorithm

2018 ◽  
Vol 1 (4) ◽  
pp. 99
Author(s):  
Cepi Ramdani ◽  
Indah Soesanti ◽  
Sunu Wibirama
Keyword(s):  
Parallel Processing ◽  
Processing Time ◽  
Color Image ◽  
Clustering Algorithms ◽  
Fuzzy C Means ◽  
Sequential Processing ◽  
Processing Times ◽  
Parameter Values ◽  
Fuzzy C Means Algorithm ◽  
Segmentation Parameters

Fuzzy C Means algorithm or FCM is one of many clustering algorithms that has better accuracy to solve problems related to segmentation. Its application is almost in every aspects of life and many disciplines of science. However, this algorithm has some shortcomings, one of them is the large amount of processing time consumption. This research conducted mainly to do an analysis about the effect of segmentation parameters towards processing time in sequential and parallel. The other goal is to reduce the processing time of segmentation process using parallel approach. Parallel processing applied on Nvidia GeForce GT540M GPU using CUDA v8.0 framework. The experiment conducted on natural RGB color image sized 256x256 and 512x512. The settings of segmentation parameter values were done as follows, weight in range (2-3), number of iteration (50-150), number of cluster (2-8), and error tolerance or epsilon (0.1 – 1e-06). The results obtained by this research as follows, parallel processing time is faster 4.5 times than sequential time with similarity level of image segmentations generated both of processing types is 100%. The influence of segmentation parameter values towards processing times in sequential and parallel can be concluded as follows, the greater value of weight parameter then the sequential processing time becomes short, however it has no effects on parallel processing time. For iteration and cluster parameters, the greater their values will make processing time consuming in sequential and parallel become large. Meanwhile the epsilon parameter has no effect or has an unpredictable tendency on both of processing time.

scholarly journals Learning with Adaptive Neighbors for Image Clustering

2018 ◽  
Author(s):  
Yang Liu ◽  
Quanxue Gao ◽  
Zhaohua Yang ◽  
Shujian Wang
Keyword(s):  
State Of The Art ◽  
Clustering Algorithms ◽  
Original Data ◽  
Image Clustering ◽  
Complex Structures ◽  
Clustering Methods ◽  
Proposed Model ◽  
Data Graph ◽  
The Given ◽  
Optimal Graph

Due to the importance and efficiency of learning complex structures hidden in data, graph-based methods have been widely studied and get successful in unsupervised learning. Generally, most existing graph-based clustering methods require post-processing on the original data graph to extract the clustering indicators. However, there are two drawbacks with these methods: (1) the cluster structures are not explicit in the clustering results; (2) the final clustering performance is sensitive to the construction of the original data graph. To solve these problems, in this paper, a novel learning model is proposed to learn a graph based on the given data graph such that the new obtained optimal graph is more suitable for the clustering task. We also propose an efficient algorithm to solve the model. Extensive experimental results illustrate that the proposed model outperforms other state-of-the-art clustering algorithms.

Prediction of Tetralogy of Fallot using Fuzzy Clustering

2020 ◽  
Vol 13 (4) ◽  
pp. 694-705
Author(s):  
K.R. Kosala Devi ◽  
V. Deepa
Keyword(s):  
Heart Disease ◽  
Tetralogy Of Fallot ◽  
Jaccard Index ◽  
Local Information ◽  
Rand Index ◽  
Image Clustering ◽  
Adjusted Rand Index ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Pixel Value

Background: Congenital Heart Disease is one of the abnormalities in your heart's structure. To predict the tetralogy of fallot in a heart is a difficult task. Cluster is the collection of data objects, which are similar to one another within the same group and are different from the objects in the other clusters. To detect the edges, the clustering mechanism improve its accuracy by using segmentation, Colour space conversion of an image implemented in Fuzzy c-Means with Edge and Local Information. Objective: To predict the tetralogy of fallot in a heart, the clustering mechanism is used. Fuzzy c-Means with Edge and Local Information gives an accuracy to detect the edges of a fallot to identify the congential heart disease in an efficient way. Methods: One of the finest image clustering methods, called as Fuzzy c-Means with Edge and Local Information which will introduce the weights for a pixel value to increase the edge detection accuracy value. It will identify the pixel value within its local neighbor windows to improve the exactness. For evaluation , the Adjusted rand index metrics used to achieve the accurate measurement. Results: The cluster metrics Adjusted rand index and jaccard index are used to evaluate the Fuzzy c- Means with Edge and Local Information. It gives an accurate results to identify the edges. By evaluating the clustering technique, the Adjusted Rand index, jaccard index gives the accurate values of 0.2, 0.6363, and 0.8333 compared to other clustering methods. Conclusion: Tetralogy of fallot accurately identified and gives the better performance to detect the edges. And also it will be useful to identify more defects in various heart diseases in a accurate manner. Fuzzy c-Means with Edge and Local Information and Gray level Co-occurrence matrix are more promising than other Clustering Techniques.

Kernelised Rough Sets Based Clustering Algorithms Fused With Firefly Algorithm for Image Segmentation

2019 ◽  
Vol 8 (4) ◽  
pp. 25-38
Author(s):  
Srujan Sai Chinta
Keyword(s):  
Image Segmentation ◽  
Euclidean Distance ◽  
Firefly Algorithm ◽  
Clustering Algorithms ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Radial Basis

Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional clustering algorithms with a meta-heuristic like the Firefly Algorithm improves the stability of the output as well as the speed of convergence. It is well known now that the Euclidean distance as a measure of similarity has certain drawbacks and so in this paper we replace it with kernel functions for the study. In fact, the authors combined Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Means (RIFCM) with Firefly algorithm and replaced Euclidean distance with either Gaussian or Hyper-tangent or Radial basis Kernels. This paper terms these algorithms as Gaussian Kernel based rough Fuzzy C-Means with Firefly Algorithm (GKRFCMFA), Hyper-tangent Kernel based rough Fuzzy C-Means with Firefly Algorithm (HKRFCMFA), Gaussian Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (GKRIFCMFA) and Hyper-tangent Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (HKRIFCMFA), Radial Basis Kernel based rough Fuzzy C-Means with Firefly Algorithm (RBKRFCMFA) and Radial Basis Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (RBKRIFCMFA). In order to establish that these algorithms perform better than the corresponding Euclidean distance-based algorithms, this paper uses measures such as DB and Dunn indices. The input data comprises of three different types of images. Also, this experimentation varies over different number of clusters.

scholarly journals A Comparative Study of Color Image Segmentation Using Hard, Fuzzy,Rough Set Based Clustering Techniques

2013 ◽  
Vol 11 (8) ◽  
pp. 2873-2878 ◽  
Author(s):  
Venkateswara Reddy Eluri ◽  
Dr. E.S. Reddy
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Image Data ◽  
Color Image Segmentation ◽  
Clustering Methods ◽  
Cluster Validity Index ◽  
Fuzzy C Means ◽  
Colour Image ◽  
Colour Image Segmentation ◽  
Material Sciences

Image segmentation is the process of subdividing an image into its constituent parts and extracting these parts of interest, which are the objects. Colour image segmentation emerges as a new area of research. It can solve many contemporary problems in medical imaging, mining and mineral imaging, bioinformatics, and material sciences. Naturally, color image segmentation demands well defined borders of different objects in an image. So, there is a fundamental demand of accuracy. The segmented regions or components should not be further away from the true object than one or a few pixels. So, there is a need for improved image segmentation technique that can segment different components precisely. Image data may have corrupted values due to the usual limitations or artifacts of imaging devices. Noisy data, data sparsity, and high dimensionality of data create difficulties in image pixel clustering. As a result, image pixel clustering becomes a harder problem than other form of data. Taking into account all the above considerations we propose an unsupervised image segmentation method using Rough-Fuzzy C-Mean a hybrid model for segmenting RGB image by reducing cluster centers using rough sets and Fuzzy C-Means Method, and also compare the effectiveness of the clustering methods such as Hard C Means (HCM), Fuzzy C Means (FCM), Fuzzy K Means (FKM), Rough C Means (RCM) with cluster validity index such as DB Index, XB Index and Dunn Index. A good clustering procedure should make the value of DB index as low as possible, for Dunn Index high value, and for XB Index low value.

Construct Knowledge Structure of Linear Algebra

2011 ◽  
Vol 211-212 ◽  
pp. 793-797
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih ◽  
Sue Fen Huang
Keyword(s):  
Knowledge Management ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Clustering Algorithms ◽  
Knowledge Structure ◽  
Interpretive Structural Modeling ◽  
Cognitive Characteristics ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.

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