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Image Processing Method Applied to Surgical Navigation

A technology of surgical navigation and image processing, which is applied in the field of image processing for surgical navigation, can solve the problems of low contrast of lesion tissue and the inability of surgical navigation system to effectively implement surgical navigation and imaging, and achieve the effect of improving accuracy

Active Publication Date: 2019-04-19
苏州国科康成医疗科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Traditional surgical navigation systems, especially those based on CT images, cannot clearly image some lesions whose tissue density is similar to that of surrounding normal tissues in CT images, resulting in low contrast of lesions, making it impossible for clinicians to accurately determine The location, size and shape of the lesion make the existing surgical navigation system unable to effectively implement surgical navigation
Moreover, the existing surgical navigation system does not have multi-modal image registration and analysis functions, and the surgical navigation system cannot automatically and accurately segment the lesion and calculate the parameters of the lesion, which limits the scope of application of the surgical navigation system

Method used

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  • Image Processing Method Applied to Surgical Navigation
  • Image Processing Method Applied to Surgical Navigation
  • Image Processing Method Applied to Surgical Navigation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] Such as figure 1 As shown, the present invention provides an image processing method applied to surgical navigation,

[0053] S10, acquiring the subject image;

[0054] S20, image registration: if the acquired images to be tested include multimodal images of CT images and / or MR images, perform multimodal image registration;

[0055] S30, Image Segmentation: Segment the voxels of the acquired image according to the background, blood vessels, nerves, bones, lesions, and organs where the lesions are located, and mark them as L in sequence b , L v , L n , L s , L f and L t ;

[0056] S40, feature extraction and processing: respectively extract the features of the background, blood vessels, nerves, bones, lesions, and organs where the lesions are located, and perform feature vector preprocessing;

[0057] S50, region of interest analysis: calculating basic features, geometric features, shape features, surface shape, texture, statistical features, topological features...

Embodiment approach

[0064] As a preferred embodiment of the present invention, feature extraction, such as figure 2 shown, including the following steps:

[0065] S41, selecting a neighborhood range whose voxel size is R in the multimodal image;

[0066] S42, calculate the histogram feature set of voxels in the neighborhood range R;

[0067] S43, calculating the gray level co-occurrence matrix feature set of voxels in the neighborhood range R;

[0068] S44, calculating the gray run length matrix feature set of the voxels in the neighborhood range R;

[0069] S45. Calculate a set of gray-scale region size matrices of voxels in the neighborhood range R.

[0070] In this embodiment, the histogram feature set includes: the mean and standard deviation of the Gaussian curve fitted by the least square algorithm, skewness, peak value, uniformity, entropy, grayscale variability, size region variability, and the 2.5th in the histogram HU at the 1st percentile, HU at the 25th percentile in the histogra...

Embodiment 2

[0083] On the basis of Example 1, this embodiment provides a preferred implementation mode, and before obtaining the subject image, it also includes steps:

[0084] S70. Establish an image database, and perform image registration on the images in the image database.

[0085] In this embodiment, the way of establishing the image database can be based on the tree structure, and the image storage directory can be established sequentially according to the modality and time point. Manage the storage directory according to the mode form and time point: the image data of the same subject is stored in the same directory; if the subject has images of multiple different modalities, create multiple subdirectories in the subject’s directory, To store the images of different modalities separately; if there are multiple images of different time points in the images of a certain modality of the subject, then create multiple subdirectories under the directory of the modality, and store the im...

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Abstract

The invention discloses an image processing method applied to surgical navigation. The method comprises the steps that a tested image is acquired; if the acquired tested image comprises a multimodality image of a CT image and / or an MR image, multimodality image registration is performed; voxels of the tested image are segmented and marked; features of background, vessels, nerves, skeletons, foci and visceral organs where the foci are located are extracted, and feature vector preprocessing is performed; feature parameters of focus areas obtained after image segmentation are calculated; the end point of a surgical navigation path is determined though feature parameter calculation, a starting point area of the surgical navigation path is determined in a man-machine interaction mode, and path optimization is done. Through multimodality image registration and analysis, automatic and accurate segmentation of the foci, calculation of the parameters of the foci and output of an optimized path for cooperation with visual operation during surgical navigation are realized.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, and more specifically, the present invention relates to an image processing method applied to surgical navigation. Background technique [0002] Traditional surgical navigation systems, especially those based on CT images, cannot clearly image some lesions whose tissue density is similar to that of surrounding normal tissues in CT images, resulting in low contrast of lesions, making it impossible for clinicians to accurately determine The location, size and shape of the lesion make the existing surgical navigation system unable to effectively implement surgical navigation. Moreover, the existing surgical navigation system does not have multi-modal image registration and analysis functions, and the surgical navigation system cannot automatically and accurately segment the lesion and calculate the parameters of the lesion, which limits the scope of application of the surgic...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H30/20G06T7/30G06T7/10
CPCG06F19/321G06T2207/10081G06T2207/10088G06T2207/30008G06T2207/30101
Inventor 周志勇戴亚康佟宝同赵凌霄刘燕
Owner 苏州国科康成医疗科技有限公司
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