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An image segmentation method based on improved intuitionistic fuzzy C-means clustering

A fuzzy intuition and mean value clustering technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as inaccurate segmentation results, large clustering errors, and ignoring spatial correlation of pixels

Active Publication Date: 2019-01-04
JIANGNAN UNIV +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, the traditional fuzzy C-means clustering algorithm and the intuitionistic fuzzy C-means clustering algorithm (IFCM) are usually sensitive to noise and initial cluster centers, and ignore the spatial correlation of pixels, resulting in large clustering errors and inaccurate segmentation results

Method used

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  • An image segmentation method based on improved intuitionistic fuzzy C-means clustering
  • An image segmentation method based on improved intuitionistic fuzzy C-means clustering
  • An image segmentation method based on improved intuitionistic fuzzy C-means clustering

Examples

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Embodiment 1

[0137] Example 1: Segmentation of simple square images

[0138] In order to verify the effectiveness of the method of the present invention for different types of images, first use the method of the present invention and six methods of FCM, IFCM, KIFCM, IFCM-S, and IIFCM to segment a simple-structured block image with a size of 256*256. The simulated square image consists of four parts with different gray values ​​of 7, 78, 214 and 251 (as shown in Fig. 2(a)). The classes in the image are divided into patches of different sizes. For simplicity, call them C1 (gray value 7), C2 (gray value 78), C3 (gray value 214), and C4 (gray value 251). Figure 2(c) is the ground-truth segmentation result map of the square image, which is divided into four parts, corresponding to four categories. To verify the robustness of different methods to noise, images polluted by 1% salt and pepper noise (S&P 1%) (as shown in Fig. 2(b)) are processed by the above six methods.

[0139] The experimenta...

Embodiment 2

[0153] Example 2: Segmentation of MRI brain images

[0154] The human brain has a complex structure. Noise and blurring between different tissues make segmentation of MRI brain images difficult. Segmentation requires precise segmentation of the three main tissues of the brain: cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM). In this embodiment, segmentation experiments are performed on MRI brain images in order to further compare the performance of different methods. The simulated MRI brain images and ground-truth segmentation results used in the experiment can be obtained from Brain Web, the publicly available dataset Simulated Brain Dataset (SBD). Keeping the same parameter configuration as the block image experiment, different methods are validated. It should be noted that the number of categories to be clustered is c=4, corresponding to GM, WM, CSF and background respectively.

[0155] Figure 3(a) is a simulated MRI brain image with a size of 217*181....

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Abstract

The invention relates to an image segmentation method based on improved intuitionistic fuzzy C-means clustering, belonging to the image segmentation field. Firstly, an improved non-membership functionis proposed to generate intuitionistic fuzzy sets, and a method based on gray feature is proposed to determine the initial clustering center, which highlights the role of uncertainty in intuitionistic fuzzy sets and improves the robustness to noise. Secondly, the improved nonlinear function is used to map the data to the kernel space in order to measure the distance between the data points and the clustering centers more precisely. Then we introduce the local space-Gray level information, considering membership degree, gray level features and spatial position information. Finally, the intuitionistic fuzzy entropy in the objective function is improved, and the fuzziness and intuitionism of the intuitionistic fuzzy set are considered. The invention can effectively overcome the influence ofnoise and blur in the image on the algorithm, improves the segmentation performance of the algorithm, the pixel clustering performance and the robustness, is suitable for various different types of gray-scale images, and can obtain more accurate segmentation results.

Description

technical field [0001] The invention belongs to the field of image segmentation, in particular to an image segmentation method based on improved intuitionistic fuzzy C-means clustering. Background technique [0002] Image information is one of the most important information for human beings to understand the world and communicate with the outside world. There are always some regions in an image with specific similar properties, namely "target regions". Most of the information of the image is usually contained in these regions. Image segmentation is a basic and critical part of image analysis and processing in the field of image video and computer vision. Its essence is to divide an image into multiple non-overlapping sub-regions based on certain characteristics of pixels. In the past few decades, scholars around the world have proposed various image segmentation methods based on different theories, including global threshold methods, edge detection methods, region-based me...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/34G06K9/62
CPCG06V10/267G06F18/23211G06F18/22
Inventor 孔军侯健邓朝阳杨生蒋敏
Owner JIANGNAN UNIV
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