Enhanced low-rank sparse decomposition model medical CT image denoising method

A sparse decomposition and CT image technology, applied in the field of medical image denoising, can solve the problems of poor reconstruction effect of denoised images, loss of image edge information, etc.

Active Publication Date: 2020-04-10
HEBEI UNIV OF TECH
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Problems solved by technology

However, the approximate solution of the minimum rank problem using the nuclear norm is often a suboptimal solution to the rank function minimization problem, which makes the reconstruction effect of the denoised image poor...

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  • Enhanced low-rank sparse decomposition model medical CT image denoising method
  • Enhanced low-rank sparse decomposition model medical CT image denoising method
  • Enhanced low-rank sparse decomposition model medical CT image denoising method

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Embodiment 1

[0141] In this embodiment, the LIDC / IDRI lung CT image database is selected as the image data source. Collect the image data in the lung CT image database (the image size is 512×512), represented by D=L+S, where D∈Rm×n represents the unprocessed original image data, L∈R m×n Represents the low-rank image data after denoising, S∈R m×n Represents sparse noisy data.

[0142] Step 1: For medical CT original image D ∈ R m×n Perform noise estimation, and according to the calculated noise intensity σ n size, traverse the original image, perform non-locally similar block matching, and divide the original image into multiple image block matrices D composed of non-locally similar blocks j :

[0143] Step 1.1: Get the original image D ∈ R m×n , its rank is recorded as r D , choose t=3r D / 5. Perform noise estimation on the image D and calculate the noise intensity σ n ;

[0144] Step 1.1.1: Perform a singular value decomposition operation on the image D, and calculate the av...

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Abstract

The invention relates to an enhanced low-rank sparse decomposition model medical CT image denoising method. The method comprises the following steps: determining the number and the size of similar blocks in a search window and an image block matrix and the maximum number of iterations during iterative solution according to a calculated noise intensity estimated value, traversing an original image,performing non-local similar block matching, and dividing the original image into a plurality of image block matrixes consisting of non-local similar blocks; carrying out low-rank matrix estimation on the medical CT original image D belonging to Rm*n by adopting a weighted Schatten p norm, and adding a joint constraint L1-2TV regularization item to construct an enhanced low-rank sparse decomposition model; sequentially inputting the image block matrixes into the model, and performing iterative solution by using an alternating direction multiplier method to obtain low-rank matrixes of the corresponding image block matrixes; and aggregating the low-rank matrixes corresponding to all the image block matrixes to obtain a denoised clean image. According to the method, more mixed noise can be separated as much as possible so as to obtain a better medical CT image denoising effect.

Description

technical field [0001] The invention belongs to the field of medical image denoising, in particular to an enhanced low-rank sparse decomposition model medical CT image denoising method, the method mainly uses the weighted Schatten p norm and L 1-2 TV regularization constraints to denoise medical CT images. Background technique [0002] Image noise is the main factor that hinders information understanding and analysis, and image denoising has long been concerned. Computed Tomography (CT) images are one of the important image data of computer-aided medical care. CT images carry a large amount of clinical diagnosis and treatment information, which can effectively assist doctors in disease diagnosis, surgical planning, and postoperative treatment evaluation. However, due to the influence of environment, equipment and other factors in the process of acquisition, compression and transmission of medical CT images, the image signal will inevitably be polluted by noise and the quali...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06K9/62
CPCG06T5/002G06T2207/10081G06T2207/30061G06F18/2136
Inventor 夏克文王斯洁王莉张江楠周亚同毛评生
Owner HEBEI UNIV OF TECH
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