G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space

2020 ◽  
pp. 1-1
Author(s):  
Cong Wang ◽  
Witold Pedrycz ◽  
Zhiwu Li ◽  
MengChu Zhou ◽  
Shuzhi Sam Ge
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Fuzzy C Means ◽  
Wavelet Space ◽  
Similarity Preserving

Gaussian Kernelized Fuzzy c-means with Spatial Information Algorithm for Image Segmentation

2012 ◽  
Vol 7 (6) ◽  
Author(s):  
Cuiyin Liu ◽  
Xiuqiong Zhang ◽  
Xiaofeng Li ◽  
Yani Liu ◽  
Jun Yang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Fuzzy C Means

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.

The Research of Leather Image Segmentation Using Texture Analysis Techniques

2014 ◽  
Vol 1030-1032 ◽  
pp. 1846-1850
Author(s):  
Hong Chen
Keyword(s):  
Image Segmentation ◽  
Surface Texture ◽  
Surface Defects ◽  
Spatial Information ◽  
Texture Feature ◽  
Gray Level ◽  
Analysis Techniques ◽  
Fuzzy C Means ◽  
Morphological Approach ◽  
Occurrence Matrix

The leather productions are produced rapidly in people’s living, the productions’ quality is required stricter. Leather must be detected include leather plainness; leather surface defects and the density of leather before they are produced to be productions.. The most important aspect is the surface defects; the defects’ location, size and quantity should be confirmed. One of the most important steps of leather defects detection is leather image segmentation so as to extract leather defects. Gray level co-occurrence matrix is used to extract a lot of leather surface texture feature, the method of optimized Fuzzy C-means is used to segment leather image in the article. The optimized Fuzzy C-means add the spatial information; the precision of segmentation is improved. The image needs to be treated use morphological approach after it is segmented. As a result, the defective areas are separated from non-defective areas successfully.

An Extended Fuzzy C-Means Segmentation for an Efficient BTD With the Region of Interest of SCP

2021 ◽  
Vol 12 (4) ◽  
pp. 11-24
Author(s):  
Subba Reddy K. ◽  
Rajendra Prasad K.
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Region Of Interest ◽  
Tumor Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Irregular Boundaries ◽  
Mri Brain Images

Magnetic resonance imaging (MRI) is the primary source to diagnose a brain tumor or masses in the medical sciences. It is emerging to detect the tumors from the scanned MRI brain images at early stages for the best treatments. Existing image segmentation techniques, morphological, fuzzy c-means are wildly successful in the extraction region of interest (ROI) in brain image segmentation. Proper extraction of ROIs is useful for regularizing the regions of tumors from the brain image with effective binarization in the segmentation. However, the existing techniques are limiting the irregular boundaries or shapes in tumor segmentation. Thus, this paper presents the proposed work extending the FCM with the spatial correlated pixel (RSCP), known as FCM-RSCP. It overcomes the problem of irregular boundaries by assessing correlated spatial information during segmentation. Benchmarked MRI brain images are used in the experiment for demonstrating the efficiency of the proposed methodology.

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