CN109166108B

CN109166108B - Automatic identification method for abnormal lung tissue of CT (computed tomography) image

Info

Publication number: CN109166108B
Application number: CN201810922391.0A
Authority: CN
Inventors: 张新财
Original assignee: Shanghai Rongda Information Technology Co ltd
Current assignee: Shanghai Rongda Information Technology Co ltd
Priority date: 2018-08-14
Filing date: 2018-08-14
Publication date: 2022-04-08
Anticipated expiration: 2038-08-14
Also published as: CN109166108A

Abstract

The invention relates to an automatic film reading technology realized by a computer in medical treatment, in particular to an automatic identification method of abnormal lung tissues of CT images, which is realized by adopting a template differential comparison technology. The method mainly uses a group of parameter vectors extracted from normal standard CT lung images as a template group, compares the template vectors with real-time dynamic CT lung images, and quickly finds abnormal tissues and automatically marks the abnormal tissues.

Description

Automatic identification method for abnormal lung tissue of CT (computed tomography) image

Technical Field

The invention relates to an automatic film reading technology realized by a computer in medical treatment, in particular to an automatic identification method of abnormal lung tissues of CT images, which is realized by adopting a template difference comparison technology.

Background

The widespread use of a large number of CT images in medicine has caused two problems: on one hand, the information amount of the image generated by one-time examination of each patient is larger and larger, the image information which needs to be read by a doctor every day is too much, and the working intensity is larger and larger; on the other hand, the level of diagnosis is very dependent on the experience and ability of the diagnostician, and misdiagnosis occurs. With the development of the DICOM standard in the medical image format and the computer-aided diagnosis technology, it is increasingly important to realize the automatic identification of the abnormal tissue of the CT image and provide the accuracy of the film reading diagnosis.

The main application targets of the method for automatically identifying the abnormal lung tissue of the CT image are shown in two aspects: on the basis of processing a large amount of CT lung image data of a patient, suspicious lesions are found out as accurately as possible, are marked in a key way, are provided for doctors to serve as a reference for further diagnosis, reduce the workload of reading films by the doctors and provide the accuracy of diagnosis. Secondly, modeling is realized on the basis of a large number of medical imaging processing methods and a large number of analyses on some special diseases, normal tissues and focus information which are easy to be confused in imaging are distinguished through selection of model parameters, and real focuses are screened out.

Disclosure of Invention

Aiming at the technical defects in the prior art, the invention relates to an automatic film reading technology realized by a computer in medical treatment, in particular to an automatic identification method of CT image lung abnormal tissue realized by adopting a template difference comparison technology. The method mainly uses a group of parameter vectors extracted from normal standard CT lung images as a template group, compares the template vectors with real-time dynamic CT lung images, and quickly finds abnormal tissues and automatically marks the abnormal tissues.

Firstly, screening 50 healthy people lung CT images as standard templates according to gender, age group and obesity grade of men and women, wherein the total number of the lung CT images is 50, the number of the templates is the number of the male from M1 and M2 ….. M25, and the number of the female from F1 and F2 ….. F25, and then generating template data according to steps.

The invention relates to a CT standard image template characteristic constructed by describing each detail point characteristic by a plurality of groups of data such as two-dimensional polar coordinates, node types, curvatures, directivities, distances among effective associated points, expansion factors and the like, and storing the comparison templates in a database strictly according to grouping numbers.

In the actual CT lung image comparison process, we first determine the age and sex of the patient, and then perform the same analysis processing steps as the template image until the characteristic data is finally obtained. Then, the corresponding grouping feature template data are found and are respectively compared one by one.

In the template alignment process, three main processing steps are required:

(1) we respectively obtain the difference blocks S of 5 lung image template data in the same group_n(optionally none) and area class beta_n(0-10 levels are divided according to the area of the block);

(2) at S_nOf more than 2 templates'_(n)Image stripping is performed according to the effectiveness of block closure, respectively. From s 'if image closure is invalid'_(n)Deleting the list;

(3) calculate each s'_(n)The maximum edge distance is used as the radius r, and finally, an effective abnormal block is marked on the original CT image.

Drawings

FIG. 1: is a schematic diagram of CT lung template feature generation of the invention;

FIG. 2 is a drawing: is a schematic diagram of the CT lung image comparison operation process.

Detailed Description

The invention is further illustrated with reference to the following figures and examples:

firstly, screening 50 healthy people's lung CT images as standard templates according to gender, age group and obesity grade of men and women, wherein the total number of the CT images is 50, the template numbers are M1 and M2 ….. M25 for men and F1 and F2 ….. F25 for women, and then generating template data according to steps, as shown in figure 1.

The CT lung template feature generation in FIG. 1 is based on gender classification of male and female, and is established by groups of childhood (0-6 years), teenager (7-17 years), teenager (18-40 years), middle aged (41-65 years) and elderly (66 years later). Each template has its own ID number, which is 3 bits: the first is gender (1: male, 2: female); the second bit represents the group number (1-5); the third bit represents the serial number in the same group.

The CT standard image template feature minutiae points are characterized by each minutiae point through data such as two-dimensional polar coordinates, node types, curvatures, directivities, distances between effective associated points, expansion factors and the like, and the comparison templates are stored in a database strictly according to grouping numbers.

In the template comparison process, firstly, for any input CT lung image, the same preprocessing as the template processing step is carried out to obtain the feature data of the minutiae points. Then selecting corresponding template groups to compare one by one. Finally, respectively obtaining the difference blocks S of the 5 lung image template data in the same group_n(optionally none) and area class beta_n(0 to 10 stages are divided by block area), and then S is obtained_nMinimum rectangle of block and save four coordinate points, for beta_nThe value of (c) is also saved. Allowing for multiple difference blocks, S_nE.g. Diff _ Area, the data structure is as follows:

struct Diff_Area {

short Block _ Number// difference Block Number

Struct BLOCK _ INDEX [ Block _ Number ]/difference BLOCK coordinate

Short Scale [ Block _ Number ],/Difference Block area level

};

The difference block coordinate data structure is as follows:

Struct BLOCK_INDEX {

int Top _ X// X coordinate on block

Int Top _ Y// Y coordinate on block

Int Down X// X coordinate under block

Int Down Y// block Down Y coordinate

Int Left X// block Left X coordinate

Int Left Y coordinate// block

Int Right _ X// block Right X coordinate

Int Right _ Y// block Right Y coordinate

}；

In the comparison algorithm, the second step is that the same operation is carried out between the difference blocks obtained by comparing the five templates. At S_nOf more than 2 templates'_(n)Are considered to be effective.

And respectively carrying out image closeness operation on the effective blocks. If the threshold is exceeded, then deemed closed invalid, then from s'_(n)And deleting the list.

The last step is to calculate each s'_(n)The maximum edge distance is used as the radius r, and finally, an effective abnormal block is inscribed on the original CT image, and the comparison algorithm flow is shown in FIG. 2.

The establishment of the standard characteristic template of the CT lung image requires an expert doctor to screen out soldiers from the existing CT file bank to complete modeling work. For the CT lung image to be checked, all working links such as comparison, labeling and the like are automatically read in and completed through a computer without manual intervention. The following example is performed with the standard feature template of the CT lung image and the comparison process.

1) Data structure for establishing standard characteristic template of CT lung image

The template data is characterized by two-dimensional polar coordinates, node types, curvatures, directivities, distances between effective association points, scaling factors and the like, and the comparison templates are stored in a database strictly according to grouping numbers. Its main data structure is thus defined as follows

struct CT_ BellowsForm {

char ID [4],/template numbering

int group ID// group numbering

char Sex// Sex: m: male, F: woman

date Birthday// birth date

int Block _ ToT _ Number// Total Number of blocks

int Block _ Serial _ No// Block Serial number

struct Block _ Area × PArea; // Block pointer

struct Block _ Direct _ PDir// Block pattern pointer

structure Block _ Curvolume PCur// Block direction Curvature

struct Block _ KeyPoint PKeyPoint// key node coordinates

struct Block _ KeyPoint _ Distance PDist/key point vector table

struct Block _ StretchFactor _ PFact; // key point scaling factor

int PScale// class of block area size (1 ~ 10)

}

struct Block_Area {

Short Block _ Number// Block Number

Struct BLOCK _ INDEX PNo// BLOCK coordinates

Short Scale [ Block _ Number ],/Block area level

} *PArea;

The difference block coordinate data structure is as follows:

Struct BLOCK_INDEX {

int Top _ X// X coordinate on block

Int Top _ Y// Y coordinate on block

Int Down X// X coordinate under block

Int Down Y// block Down Y coordinate

Int Left X// block Left X coordinate

Int Left Y coordinate// block

Int Right _ X// block Right X coordinate

Int Right _ Y// block Right Y coordinate

} *PNo;

2) CT lung image preprocessing

Baseline wander processing

l image

l image segmentation

l block division

l … ...

3) Implementation of the comparison Algorithm

First, we need to determine the preset matching amounts of the reject rate (FAR), the False Recognition Rate (FRR) and the Similarity (Similarity) as three groups:

0.1%、 0.02%、0.75%;

0.05%、0.06%、0.85%;

0.03%、0.08%、0.93%;

meanwhile, it is also necessary to set up a reasonable comparison threshold value for each important index in the images, blocks and detail points through a large amount of measurement and calculation, which is not particularly described herein.

ECGWaveVerify (WaveData, WaveTemplate, VerifyParameter) for the alignment algorithm;

4) CT image template reconstruction

The template reconstruction function name is:

ECGWaveTemplateRefactoring(OldWaveTemplate, NewWaveTemplate ,Parameter);

5) CT image template optimization

The template optimization function name is:

ECGWaveTemplateOptimize (WaveTemplate, Parameter);

the method is designed from the angle of the CT lung image template, can improve the precision and reliability of the CT image template to a certain extent, and improves the accuracy of automatic pre-diagnosis.

Claims

1. A method for automatically identifying abnormal lung tissues of CT images by adopting a template differential comparison technology is characterized in that parameter vectors of CT lung images in a group of normal standard forms are extracted to serve as a template group, and the parameter vectors serve as template characteristic data;

the comparison between the template set and the CT lung image collected in real time is a method for finding out abnormal blocks by adopting a difference block segmentation technology;

wherein, the step of extracting the parameter vector of the CT lung image based on a group of normal standard forms as a template group comprises:

screening lung CT images of a set number of healthy people respectively according to sex, age group and obesity level of men and women as standard templates, numbering the standard templates, and extracting parameter vectors of the standard templates as a template group according to the steps;

the method for finding the abnormal block further comprises the following steps:

s1, obtaining difference blocks Sn and area levels betan of the lung image template data with the same set number respectively;

s2, in Sn, image stripping is carried out on the overlapped areas S ' (n) with more than 2 templates according to the validity of block closure, and if the image closure is invalid, the overlapped areas S ' (n) are deleted from the S ' (n) list;

s3, the center point of each S' (n) is calculated, the maximum edge distance is used as the radius r, and finally, the valid abnormal region is circled on the original CT image.

2. The method of claim 1, characterized by a vector adaptive feature of CT lung image size.

3. The method of claim 1, wherein the extracting the vector thereof as template feature data comprises: and (3) using a plurality of groups of two-dimensional polar coordinates, node types, curvatures, directional diagrams, distances among effective association points and expansion factors obtained after CT image preprocessing as parameter vectors of lung surrounding tissue template features.