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Medical image segmentation method based on Markov

A Markov segmentation and medical image technology, applied in the field of image processing, can solve the problems of reduced segmentation accuracy, increased computational overhead, and increased initial value sensitivity, achieving improved accuracy, high accuracy, and robustness Good results

Active Publication Date: 2014-04-16
HOPE CLEAN ENERGY (GRP) CO LTD
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Problems solved by technology

The level set segmentation algorithm fully considers the gray level inhomogeneity of medical images, and can correct the gray level of medical images at the same time as segmentation, but it only uses gray level information and does not use spatial information, and the difference in processing segmentation areas is small (The gray scale of the segmented area is very similar or the contrast is very low) The sensitivity to the initial value increases, and the segmentation accuracy decreases
In addition, the algorithm is based on local areas, and each pixel needs to be calculated multiple times, which increases the computational overhead

Method used

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Embodiment Construction

[0016] In order to describe the content of the present invention conveniently, at first the following prior art is briefly introduced:

[0017] Definition 1: An M×N image is recorded as Y={y s |s∈S}, S={s=(i,j)|1≤i≤M,1≤j≤N}, y s Represents the pixel value of pixel s, S is the set of pixel points in all images, and the segmentation result of the image is recorded as X={x s |s∈S},x s Indicates the category of the image pixel, x s The value range is recorded as L={1,2,...,N}, and L represents the category of the image pixel.

[0018] Definition 2: 8-neighborhood system and its potential group energy. Let δ(s) be the neighborhood of pixel s, which is a circular area centered at position s and radius r: δ(s)={s 1 ∈S|dist(s,s 1 )≤r 2 ,s≠s 1}, where dist(s,s 1 ) for s and s 1 Euclidean distance between two points. The neighborhood system is defined according to this: δ={δ(s)|s∈S}, which satisfies the following three conditions: 1) 2) Arrows indicate equivalence, s and ...

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Abstract

The invention provides a medical image segmentation method based on the Markov. The method comprises the steps that 1, an original image is segmented initially through the Markov segmentation method; 2, the image is processed through the level set algorithm, and a level set function, a biased field and the gray average of areas are updated; 3, the original image divided by the currently updated biased field is a corrected image; 4, the corrected image is segmented through the Markov segmentation algorithm; 5, whether the sum of the number of times of iteration of the current level set algorithm and the Markov segmentation algorithm is greater than a preset value or not is judged, if yes, the corrected image which is finally obtained is segmented and then a final segmentation result is obtained, and if not, the step 2 is carried out again. The medical image segmentation method based on the Markov can process the medical image segmentation with the uneven gray level rapidly, steadily and accurately.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to an image segmentation method in Markov and level set technology. Background technique [0002] In recent years, medical imaging technology has developed rapidly, and its clinical application has become more and more extensive. Various modern imaging technologies such as computed tomography (CT), B-ultrasound, magnetic resonance imaging (MRI) and electronic endoscopy have As a routine auxiliary method, it is used in clinical diagnosis, which enables doctors to observe and diagnose patients' tissues and organs more clearly and directly, greatly improving the diagnostic accuracy and shortening the diagnosis time. Moreover, the role and influence of these auxiliary means are constantly increasing, which is driving the transformation of modern medical diagnosis. At present, higher-dimensional and higher-resolution imaging technology is becoming a research hotspot. [0...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 解梅李亮岳兴明
Owner HOPE CLEAN ENERGY (GRP) CO LTD
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