Anti-noise quick fuzzy-clustering digital image segmentation method

Abstract

The invention discloses an anti-noise quick fuzzy-clustering digital image segmentation method. The method includes the steps of performing feature extraction of an image to be segmented to obtain a feature data set X of the image; using the feature data set X and neighborhood information of the image to perform anti-noise pretreatment of an original image; initializing a cluster center by a K-means algorithm; calculating a fuzzy membership matrix; updating the fuzzy membership matrix through a space function based on space information construction; calculating the cluster center and a targeted function value used for implementing cluster segmentation based on the updated fuzzy membership matrix; performing loop iteration; and acquiring probability of data points belonging to a certain type according to a fuzzy membership matrix subjected to cluster output, and segmenting the image by performing classification markup to each data point according to the maximum probability principle. Satisfactory image segmentation effect can be obtained with few iterative times, influence of noise is eliminated well, and quality of image segmentation and stability of segmentation effect are increased.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a fast fuzzy clustering digital image segmentation method with noise resistance. Background technique [0002] Image is an important source for human beings to obtain information from the objective world and an important medium for transmitting information. With the development of technologies such as computers and the Internet, digital image processing technology is playing an increasingly important role in industries such as industry, medical care, military affairs, and transportation. [0003] In order to effectively extract and utilize the information contained in the digital image, it is necessary to segment the image. Image segmentation is to divide an image into a group of disjoint sub-regions, which have the same or similar characteristics inside the same region, where the characteristics can be grayscale, color, texture, etc. Image segmentation is the most impo...

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Problems solved by technology

[0009] However, the standard FCM method has the following obvious disadvantages in image segmentation: (1) The selection of the initial cluster center has a great impact on the image segmentation results. If the initial cluster center is not well selected, it will make the method Trapped in a local optimum, the ideal segmentation results cannot be obtained; (2) The spatial information of the pixels is not considered, which makes the method more sensitive to noise. When dealing with noisy images, satisfactory segmentation results cannot be obtained

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