An Enhanced Clustering Method for Image Segmentation

2019 ◽  
pp. 325-343
Author(s):  
Bikram Keshari Mishra ◽  
Amiya Kumar Rath
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithms ◽  
Experimental Result ◽  
Image Size ◽  
Clustering Method ◽  
Impressive Result ◽  
Structural Content ◽  
Overall Performance ◽  
Image Pixels ◽  
Visual Characteristic

The findings of image segmentation reflect its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this chapter, the authors have evaluated the performances of three different clustering algorithms used in image segmentation: the classical k-means, its modified k-means++, and proposed enhanced clustering method. Brief explanations of the fundamental working principles implicated in these methods are presented. Thereafter, the performance which affects the outcome of segmentation are evaluated considering two vital quality measures, namely structural content (SC) and root mean square error (RMSE). Experimental result shows that the proposed method gives impressive result for the computed values of SC and RMSE as compared to k-means and k-means++. In addition to this, the output of segmentation using the enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.

Improving the Efficiency of Color Image Segmentation Using an Enhanced Clustering Methodology

Biometrics ◽  
2017 ◽  
pp. 1788-1802 ◽  
Author(s):  
Nihar Ranjan Nayak ◽  
Bikram Keshari Mishra ◽  
Amiya Kumar Rath ◽  
Sagarika Swain
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Clustering Algorithms ◽  
Performance Criterion ◽  
Color Image Segmentation ◽  
Experimental Result ◽  
Image Size ◽  
Impressive Result ◽  
Image Pixels ◽  
Visual Characteristic

The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms normally used in image segmentation – the typical K-Means, its modified K-Means++ and their proposed Enhanced Clustering method. The idea is to present a brief explanation of the fundamental working principles implicated in these methods. They have analyzed the performance criterion which affects the outcome of segmentation by considering two vital quality measures namely – Structural Content (SC) and Root Mean Square Error (RMSE) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives impressive result for the computed values of SC and RMSE as compared to K-Means and K-Means++. In addition to this, the output of segmentation using the Enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.

Improving the Efficiency of Color Image Segmentation using an Enhanced Clustering Methodology

2015 ◽  
Vol 6 (2) ◽  
pp. 50-62 ◽  
Author(s):  
Nihar Ranjan Nayak ◽  
Bikram Keshari Mishra ◽  
Amiya Kumar Rath ◽  
Sagarika Swain
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Clustering Algorithms ◽  
Performance Criterion ◽  
Color Image Segmentation ◽  
Experimental Result ◽  
Image Size ◽  
Impressive Result ◽  
Image Pixels ◽  
Visual Characteristic

The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms normally used in image segmentation – the typical K-Means, its modified K-Means++ and their proposed Enhanced Clustering method. The idea is to present a brief explanation of the fundamental working principles implicated in these methods. They have analyzed the performance criterion which affects the outcome of segmentation by considering two vital quality measures namely – Structural Content (SC) and Root Mean Square Error (RMSE) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives impressive result for the computed values of SC and RMSE as compared to K-Means and K-Means++. In addition to this, the output of segmentation using the Enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.

A Comparative Analysis of the Direct Clustering Algorithms Based on Different Similarity Measure of Vague Sets

2013 ◽  
Vol 380-384 ◽  
pp. 1488-1494
Author(s):  
Wang Wei ◽  
Jin Yue Peng
Keyword(s):  
Similarity Measure ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Evaluation Criteria ◽  
Experimental Result ◽  
Clustering Method ◽  
Vague Sets ◽  
Intelligence System ◽  
System Clustering

In the research and development of intelligence system, clustering analysis is a very important problem. According to the new direct clustering algorithm using similarity measure of Vague sets as evaluation criteria presented by paper, the Vague direct clustering method are used to analysis using different similarity measure of Vague sets. The experimental result shows that the direct clustering method based on the similarity of Vague sets is effective, and the direct clustering method based on different similarity measure of Vague sets is the same basically, but difference on the steps of clustering. To select different algorithms according different conditions in the work of the actual applications.

scholarly journals IMPROVED FAST FUZZY C-MEAN AND ITS APPLICATION IN MEDICAL IMAGE SEGMENTATION

2010 ◽  
Vol 19 (01) ◽  
pp. 203-214 ◽  
Author(s):  
M. A. BALAFAR ◽  
A. B. D. RAHMAN RAMLI ◽  
M. IQBAL SARIPAN ◽  
SYAMSIAH MASHOHOR ◽  
ROZI MAHMUD
Keyword(s):  
Image Segmentation ◽  
Input Data ◽  
Color Image ◽  
Correct Diagnosis ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Experimental Result ◽  
Clustering Method ◽  
Target Class ◽  
Low Contrast

Image segmentation is a preliminary stage in diagnosis tools and the accurate segmentation of medical images is crucial for a correct diagnosis by these tools. Sometimes, due to inhomogeneity, low contrast, noise and inequality of content with semantic, automatic methods fail to segment image correctly. Therefore, for these images, it is necessary to use user help to correct method's error. We proposed to upgrade FAST FCM method to use training data to have more accurate results. In this paper, instead of using pixels as training data which is usual, we used different gray levels as training data and that is why we have used FAST FCM, because the input of FAST FCM is gray levels exist in image (histogram of the image). We named the new clustering method improved fast fuzzy C-mean (FCM). We use two facts to improve fast FCM. First, training data for each class are the member of the class. Second, the relevance distance of each input data from the training data of a class show the distance of the input data from the class. To cluster an image, first, the color image is converted to gray level image; then, from histogram of image, user selects training data for each target class, afterwards, the image is clustered using postulated clustering method. Experimental result is demonstrated to show effectiveness of the new method.

A Time Efficient Clustering Algorithm for Gray Scale Image Segmentation

2013 ◽  
Vol 3 (1) ◽  
pp. 22-32 ◽  
Author(s):  
Nihar Ranjan Nayak ◽  
Bikram Keshari Mishra ◽  
Amiya Kumar Rath ◽  
Sagarika Swain
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Clustering Algorithms ◽  
Performance Measure ◽  
Experimental Result ◽  
Computational Time ◽  
Watershed Segmentation ◽  
Gray Scale ◽  
Gray Scale Image

The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms – the classical K-Means, a modified Watershed segmentation as proposed by A. R. Kavitha et al., (2010) and their proposed Improved Clustering method normally used for gray scale image segmentation. The authors have analyzed the performance measure which affects the result of gray scale segmentation by considering three very important quality measures that is – Structural Content (SC) and Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives remarkable consequence for the computed values of SC, RMSE and PSNR as compared to K-Means and modified Watershed segmentation. In addition to this, the end result of segmentation by means of the Proposed technique reduces the computational time as compared to the other two approaches irrespective of any input images.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Alifia Puspaningrum ◽  
Nahya Nur ◽  
Ozzy Secio Riza ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Hierarchical Clustering ◽  
Gabor Filter ◽  
Hierarchical Cluster ◽  
Experimental Result ◽  
Main Process ◽  
Percentile Method ◽  
Cluster Analysis Method ◽  
2D Gabor Filter

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES

2021 ◽  
pp. 2150012
Author(s):  
Kuo-Lung Lor ◽  
Chung-Ming Chen
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Linear Time ◽  
Mean Shift ◽  
Texture Features ◽  
Color Image Segmentation ◽  
Image Size ◽  
Region Merging ◽  
Tissue Segmentation ◽  
Regional Pattern

The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from the background scene. This iterative region-merging is based on a modified Region Adjacency Graph (RAG) model built from initial segmented results of the mean shift to speed up the merging process. The foreground region of interest (ROI) is segmented by the reduction of the background region and discrimination of uncertain regions. We then compare our method against state-of-the-art interactive image segmentation algorithms in both natural images and histological images. Taking into account the homogeneity of both color and texture, the resulting semi-supervised classification and interactive segmentation capture histological structures more completely than other intensity or color-based methods. Experimental results show that the merging of the RAG model runs in a linear time according to the number of graph edges, which is essentially faster than both traditional graph-based and region-based methods.

scholarly journals A semantic approach for text document clustering using frequent itemsets and WordNet

2018 ◽  
Vol 7 (2.18) ◽  
pp. 102
Author(s):  
Harsha Patil ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Clustering Algorithms ◽  
Document Clustering ◽  
Knowledge Bases ◽  
Experimental Result ◽  
Semantic Approach ◽  
Text Document ◽  
Clustering Quality ◽  
Ship Function ◽  
Membership Score ◽  
Specific Cluster

Document Clustering is an unsupervised method for classified documents in clusters on the basis of their similarity. Any document get it place in any specific cluster, on the basis of membership score, which calculated through membership function. But many of the traditional clustering algorithms are generally based on only BOW (Bag of Words), which ignores the semantic similarity between document and Cluster. In this research we consider the semantic association between cluster and text document during the calculation of membership score of any document for any specific cluster. Several researchers are working on semantic aspects of document clustering to develop clustering performance. Many external knowledge bases like WordNet, Wikipedia, Lucene etc. are utilized for this purpose. The proposed approach exploits WordNet to improve cluster member ship function. The experimental result shows that clustering quality improved significantly by using proposed framework of semantic approach. 

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