scholarly journals Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Subrajeet Mohapatra ◽  
Dipti Patra ◽  
Kundan Kumar
Keyword(s):  
Image Segmentation ◽  
Fuzzy Sets ◽  
Rough Sets ◽  
Necessary Condition ◽  
Hematological Disease ◽  
Fuzzy C Means ◽  
Segmented Images ◽  
Segmented Nucleus ◽  
Recognition Systems ◽  
Leukocyte Segmentation

The segmentation of leukocytes and their components acts as the foundation for all automated image-based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented images is plentiful, suitable segmentation routines need to be developed for better disease recognition. Clustering is an essential image segmentation procedure which segments an image into desired regions. A judicious integration of rough sets and fuzzy sets is suitably employed towards leukocyte segmentation in a clustering framework. In this study, the goodness of fuzzy sets and rough sets is suitably integrated to achieve improved segmentation performance. The membership concept of fuzzy sets endow is efficient handling of overlapping partitions, and the rough sets provide a reasonable solution to deal with uncertainty, vagueness, and incompleteness in data. Such synergistic combination gives the proposed scheme an edge over standard cluster-based segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means algorithm is robust in segmenting stained blood microscopic images. The accomplished segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which leads to automated leukemia detection.

scholarly journals Enhanced and Explored Intuitionistic Rough Based Fuzzy C-means Approach for MR Brain Image Segmentation

2018 ◽  
Vol 7 (3.12) ◽  
pp. 73
Author(s):  
B Prasanthi ◽  
Dr N. Nagamalleswararao
Keyword(s):  
Image Segmentation ◽  
Fuzzy Sets ◽  
Rough Sets ◽  
Evaluation Method ◽  
Medical Images ◽  
Magnetic Resonance Images ◽  
Brain Images ◽  
Brain Image ◽  
Fuzzy C Means ◽  
Brain Image Segmentation

Segmentation of magnetic resonance images is medically complex and important for study and diagnosis of medical brain images, because of its sensitivity in terms of noise for brain medical images. These are the main issues in classification of brain images. Because of uncertainty & vagueness of brain medical images, so that rough sets, fuzzy sets and Rough sets are mathematical tools evaluate and handle uncertainty and vagueness in medical brain images. Traditionally, different type of fuzzy sets, Rough sets and rough sets based approaches were introduced, they have different several drawbacks with respect to different parameters. So this paper introduces a novel image segmentation calculation method i.e. Enhanced and Explored Intuitionistic Rough based Fuzzy C-means Approach (EEISFCMA) with estimation of weight bias parameter for brain image segmentation. Intuitionistic Rough based fuzzy sets are generalized form of fuzzy, Rough sets and their representative elements are evaluated with non-membership and membership value. Proposed algorithm of this paper consists standard features of existing clustering without spatial weight context data, it defines sensitive of noise in brain images, so that our proposed algorithm deals with intensity and noise reduction of brain image effectively. Furthermore, to reduce iterations in clustering, proposed algorithm initializes cluster centroid based on weight measure using max-dist evaluation method before execution of proposed algorithm. Experimental results of proposed approach carried out efficient image segmentation results compared to existing segmented approaches developed in brain image and other related images. Mainly proposed approach have consists better experimental evaluation based on results.  

Type-2 Fuzzy C-Means Algorithm with Spatial Informations and Membership Constrains for Liver Segmentation

2013 ◽  
Vol 746 ◽  
pp. 570-574
Author(s):  
Qin Li Zhang ◽  
Ya Fan Yue ◽  
Zhao Zhuang Guo
Keyword(s):  
Image Segmentation ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Spatial Information ◽  
Target Area ◽  
Fuzzy C Means ◽  
Liver Image ◽  
Fuzzy C Means Algorithm

The Fuzzy C-Means algorithm with spatial informations and membership constrains is a very effective and efficient image segmentation method. However£¬it is founded with Type-1 fuzzy sets, which can not handle the uncertainties existing in liver image well.The type-2 fuzzy sets have better performance on handling uncertainties than Type-1 fuzzy set. In this paper, a new robust Type-2 FCM image segmentation algorithm is proposed aiming to improve the segmentation precision and robustness of liver image by introducing the type-2 fuzzy set into FCM with spatial information and membership constrains. We extend the type-1 fuzzy set of membership to interval type-2 fuzzy set using two fuzzifiers and which create a footprint of uncertainty (FOU). The experimental results show that the target area of the liver in CT images can be segmented well by the proposed method.

Fusion of Fuzzy Set and FCM for Image Segmentation

2012 ◽  
Vol 182-183 ◽  
pp. 723-728
Author(s):  
Chao Quan Zhang ◽  
Liang Chen ◽  
Shao Jing Zhou
Keyword(s):  
Image Segmentation ◽  
Fuzzy Set ◽  
Rough Sets ◽  
Segmentation Method ◽  
Fcm Algorithm ◽  
Rough Sets Theory ◽  
Fuzzy C Means ◽  
Sets Theory ◽  
Test Result

Image segmentation with the traditional Fuzzy C-means (FCM) algorithm only uses each pixel’s gray value, when the image is corrupted by noises, the accuracy of segmentation will be greatly reduced. So, this paper proposed an image segmentation method which based on rough sets theory and fuzzy c-mean clustering. The test result shows that the method has a good segmentation performance.

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.

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