CN115067997A - Biological tissue abnormality characterization method based on fractal analysis - Google Patents
Biological tissue abnormality characterization method based on fractal analysis Download PDFInfo
- Publication number
- CN115067997A CN115067997A CN202210673586.2A CN202210673586A CN115067997A CN 115067997 A CN115067997 A CN 115067997A CN 202210673586 A CN202210673586 A CN 202210673586A CN 115067997 A CN115067997 A CN 115067997A
- Authority
- CN
- China
- Prior art keywords
- ultrasonic
- fractal
- tissue
- ultrasonic image
- biological tissue
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005856 abnormality Effects 0.000 title claims abstract description 12
- 238000004458 analytical method Methods 0.000 title claims abstract description 12
- 238000012512 characterization method Methods 0.000 title claims abstract description 12
- 230000005653 Brownian motion process Effects 0.000 claims abstract description 7
- 238000005537 brownian motion Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 15
- 230000000877 morphologic effect Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 4
- 238000000585 Mann–Whitney U test Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 239000000523 sample Substances 0.000 claims description 3
- 230000003628 erosive effect Effects 0.000 claims description 2
- 230000010339 dilation Effects 0.000 claims 1
- 230000002159 abnormal effect Effects 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 abstract description 2
- 238000007474 nonparametric Mann- Whitney U test Methods 0.000 abstract 1
- 238000003384 imaging method Methods 0.000 description 13
- 238000005516 engineering process Methods 0.000 description 5
- 238000001574 biopsy Methods 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 4
- 238000002604 ultrasonography Methods 0.000 description 3
- 206010028980 Neoplasm Diseases 0.000 description 2
- 206010054107 Nodule Diseases 0.000 description 2
- 201000011510 cancer Diseases 0.000 description 2
- 238000002592 echocardiography Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000036210 malignancy Effects 0.000 description 1
- 230000003211 malignant effect Effects 0.000 description 1
- 230000009022 nonlinear effect Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 231100000915 pathological change Toxicity 0.000 description 1
- 230000036285 pathological change Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
- A61B8/5223—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for extracting a diagnostic or physiological parameter from medical diagnostic data
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biophysics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Pathology (AREA)
- Radiology & Medical Imaging (AREA)
- Veterinary Medicine (AREA)
- Heart & Thoracic Surgery (AREA)
- Physics & Mathematics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Physiology (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
Abstract
The invention discloses a biological tissue abnormity characterization method based on fractal analysis, which relates to ultrasonic image characterization and hypothesis test in statistics.A ultrasonic image and a fractal dimension analysis method measured by B-ultrasonic scanning are combined, firstly, an ultrasonic diagnostic apparatus is arranged in a second harmonic scanning mode, and the ultrasonic image of the biological tissue is scanned and stored; secondly, performing fractal dimension analysis on the ultrasonic image based on a fractal Brownian motion model, and calculating a Hurst coefficient; and finally, calculating the fractal dimension through the Hurst parameter, and judging whether the fractal dimension of the measured tissue is different from that of the normal tissue by using nonparametric Mann-Whitney U test, thereby finally realizing the characterization of the abnormal biological tissue. The invention can be obtained by only utilizing commercial B-ultrasonic without additional instruments, has simple and convenient operation and strong practicability, and effectively solves the technical problems of strong subjective dependence on complex biological tissue identification abnormality and need of abundant clinical experience in the current ultrasonic image examination.
Description
Technical Field
The invention relates to ultrasonic image inspection and hypothesis testing in statistics, in particular to a biological tissue abnormality characterization method based on fractal analysis.
Background
Ultrasonic imaging has the advantages of high resolution, high safety, low cost, high-speed real-time imaging and the like, and becomes the most common non-invasive diagnosis means in clinical medicine. The pulse echo method is the most widely used ultrasonic imaging technology at present. The pulse echo method ultrasonic imaging technology utilizes acoustic impedance and acoustic attenuation difference of different organs or tissues to receive echoes with different scattered intensities, and the returned echoes comprise biological tissue structure characteristics and morphological characteristics. By combining the echo characteristics with medical knowledge, whether the tissue has abnormal changes can be judged.
The B-mode ultrasonic diagnosis method is the main method of ultrasonic diagnosis at present. The method is an imaging technology which is developed based on a pulse echo method and displays ultrasonic information on a fault plane of a tissue in a two-dimensional distribution mode. In order to improve the accuracy of clinical diagnosis, people are dedicated to improving the B ultrasonic imaging technology. In recent years, harmonic imaging has been established by exploiting the nonlinear effect of acoustic waves in human tissue. Unlike conventional fundamental wave imaging, harmonic imaging techniques perform signal processing on harmonic signals in echo signals and form a B-ultrasonic map. The higher the frequency, the greater the attenuation in the tissue and therefore second harmonic imaging is typically used. Compared with the traditional fundamental wave imaging, the harmonic wave imaging has higher signal-to-noise ratio and stronger spatial resolution, and has advantages in the aspects of eliminating near-field artifacts and sidelobe interference, enhancing tissue contrast, improving deep tissue echo information quantity and the like. The detection target can be displayed more clearly.
B-mode imaging results can be hyperechoic, isoechoic, or hypoechoic, however complex echogenic properties inside the tissue lead to diagnostic difficulties. In addition, the diagnosis result is limited by the clinical experience and professional knowledge of doctors and has certain subjectivity. Thus, while there are many B-mode features that can help screen for malignancy, biopsy remains the standard for discriminating the nature of tumors. However, biopsy is a costly and uncomfortable invasive examination for the patient. To avoid unnecessary biopsies and to identify malignant tumors in a timely manner, it is necessary to develop aids to assist clinicians in tissue abnormality characterization. With the development of computers, people have shown great interest in identifying pathological changes using artificial intelligence.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a biological tissue abnormality characterization method based on fractal analysis.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a biological tissue abnormality characterization method based on fractal analysis comprises the following specific steps:
scanning tissues by a B-ultrasonic diagnostic apparatus, and acquiring ultrasonic image data of biological tissues on an operation platform;
randomly selecting one frame of ultrasonic image data stored in one measurement, and performing basic morphological operation and histogram equalization processing on the ultrasonic image data to remove noise and normalize a gray level map so as to obtain better fractal texture characteristics;
selecting an interested region and a reference region according to the preprocessed ultrasonic image, and calculating a corresponding Hurst coefficient in the region based on a fractal Brownian motion model;
step four, calculating the fractal dimension D of the corresponding region according to the Hurst coefficient;
and step five, judging whether the fractal dimension of the corresponding region is in a normal range by using a Mann-Whitney U test.
Further, in the first step, the B ultrasonic diagnostic apparatus is set in a second harmonic scanning mode, and a B ultrasonic probe with the center frequency of 10MHz is used for scanning tissues.
Furthermore, in the second step, considering that the B-mode scanning region may contain a plurality of tissues, morphological operations and histogram equalization are performed according to tissue features, so that tissue differences are more prominent.
Further, in the second step, the morphological operation includes erosion and swelling.
Further, in the third step, the fractal Brownian motion curve I (x) satisfies the equation (1)
E[I(x 2 )-I(x 1 )]∝(x 2 -x 1 ) H (1)
In the formula:
x 1 、x 2 the position of the pixels 1, 2;
I(x 1 )、I(x 2 ) -grey scale at pixel 1, 2;
H-Hurst coefficient;
for a type-B ultrasonic map of M × M size, Hurst coefficients can be estimated by equation (2):
log(di(k))＝Hlog(k)+log(c)，k＝1,2…n (2a)
in the formula:
c-a proportionality constant;
i (x, y) -the gray scale of the pixel (x, y);
d i (k) -a difference in intensity;
m-image size.
The Hurst coefficient H is estimated as the slope in equation (2 a).
Further, in the fourth step, the fractal dimension D is:
D＝3-H (3)。
compared with the prior art, the technical scheme provided by the invention has the following remarkable effects:
the invention provides a biological tissue abnormality characterization method based on fractal analysis, introduces a parameter capable of effectively reflecting biological tissue abnormality, and provides a parameter measuring and calculating method. Only needs to use commercial B-ultrasonic to collect the tissue image data and can obtain the tissue image data without additional instruments. Compared with the conventional biopsy method, the method has the advantages of simple calculation and high universality, effectively solves the problems of strong subjectivity and abundant clinical experience in B-ultrasonic examination, and provides an objective parameter for reflecting tissue abnormality.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic diagram of a B-mode ultrasound using the present invention to illustrate region selection (region of interest, reference region).
FIG. 2 is a pre-processed image for morphology and histogram equalization for FIG. 1; malignant nodules: a) raw graph, b) morphological operation, c) histogram equalization; benign nodules: d) raw graph, e) morphological operations, f) histogram equalization.
Fig. 3 is a graph of results of fractal analysis of different regions.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment provided by the invention comprises the following steps: a biological tissue abnormality characterization method based on fractal analysis comprises the following specific steps:
1) a VINNO Technology (Suzhou) co, Ltd) diagnostic unit was set in second harmonic scanning mode, tissue was scanned with a type B ultrasound probe centered at 10MHz, and the manufacturer acquired the raw ultrasound image data on the corresponding operating platform.
2) Randomly selecting one frame of ultrasonic image data stored in one measurement, and carrying out basic morphological operation (corrosion and expansion) and histogram equalization processing on the ultrasonic image data to realize noise removal and gray level map normalization so as to obtain better fractal texture characteristics;
3) and selecting an interested region and a reference region according to the preprocessed ultrasonic image. Calculating a corresponding Hurst coefficient in the region based on a fractal Brownian motion model;
fractal Brownian motion curve I (x) satisfying equation (1)
E[I(x 2 )-I(x 1 )]∝(x 2 -x 1 ) H (1)
In the formula:
x 1 、x 2 the position of the pixels 1, 2;
I(x 1 )、I(x 2 ) -grey scale at pixel 1, 2;
H-Hurst coefficient.
For a type-B ultrasonic map of M × M size, Hurst coefficients can be estimated by equation (2):
log(di(k))＝Hlog(k)+log(c)，k＝1,2…n (2a)
in the formula:
c-a proportionality constant;
i (x, y) -the gray scale of the pixel (x, y);
d i (k) -a difference in intensity;
m-image size.
The Hurst coefficient H can be estimated as the slope in equation (2 a).
4) Calculating a fractal dimension D of the corresponding region according to the Hurst coefficient;
D＝3-H (3)
5) and judging whether the fractal dimension of the corresponding region is in a normal range by using a Mann-Whitney U test.
In conclusion: the invention can be obtained by only utilizing commercial B-ultrasonic without additional instruments, has simple and convenient operation and strong practicability, and effectively solves the problems of strong subjectivity and rich clinical experience in biological tissue examination.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It should be understood by those skilled in the art that the above embodiments do not limit the scope of the present invention in any way, and all technical solutions obtained by using equivalent substitution methods fall within the scope of the present invention.
The parts not involved in the present invention are the same as or can be implemented using the prior art.
Claims (6)
1. A biological tissue abnormality characterization method based on fractal analysis is characterized by comprising the following specific steps:
scanning tissues by a B-ultrasonic diagnostic apparatus, and acquiring ultrasonic image data of biological tissues on an operation platform;
randomly selecting one frame of ultrasonic image data stored in one measurement, and performing basic morphological operation and histogram equalization processing on the ultrasonic image data to remove noise and normalize a gray level map so as to obtain better fractal texture characteristics;
selecting an interested region and a reference region according to the preprocessed ultrasonic image, and calculating a corresponding Hurst coefficient in the region based on a fractal Brownian motion model;
step four, calculating the fractal dimension D of the corresponding region according to the Hurst coefficient;
and step five, judging whether the fractal dimension of the corresponding region is in a normal range by using a Mann-Whitney U test.
2. The method as claimed in claim 1, wherein in the step one, the B-ultrasonic diagnostic apparatus is set to a second harmonic scanning mode, and the B-ultrasonic probe with a center frequency of 10MHz is used to scan the tissue.
3. The method as claimed in claim 1, wherein the step two takes into account that the B-mode ultrasonic scanning region may contain a plurality of tissues, and morphological operations and histogram equalization are performed according to the tissue characteristics to make the tissue difference more prominent.
4. The method as claimed in claim 1, wherein the morphological operations include erosion and dilation.
5. The method for measuring parameters of biological tissue anomalies according to claim 1, wherein in the third step, the fractal Brownian motion curve I (x) satisfies equation (1)
E[I(x 2 )-I(x 1 )]∝(x 2 -x 1 ) H (1)
In the formula:
x 1 、x 2 the position of the pixels 1, 2;
I(x 1 )、I(x 2 ) -grey scale at pixel 1, 2;
H-Hurst coefficient;
for a type-B ultrasonic map of M × M size, Hurst coefficients can be estimated by equation (2):
log(di(k))＝Hlog(k)+log(c)，k＝1,2…n (2a)
in the formula:
c-a proportionality constant;
i (x, y) -the gray scale of the pixel (x, y);
d i (k) -a difference in intensity;
m-image size.
The Hurst coefficient H is estimated as the slope in equation (2 a).
6. The method as claimed in claim 5, wherein in the step four, the fractal dimension D is:
D＝3-H (3)。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210673586.2A CN115067997A (en) | 2022-06-13 | 2022-06-13 | Biological tissue abnormality characterization method based on fractal analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210673586.2A CN115067997A (en) | 2022-06-13 | 2022-06-13 | Biological tissue abnormality characterization method based on fractal analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115067997A true CN115067997A (en) | 2022-09-20 |
Family
ID=83250378
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210673586.2A Pending CN115067997A (en) | 2022-06-13 | 2022-06-13 | Biological tissue abnormality characterization method based on fractal analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115067997A (en) |
-
2022
- 2022-06-13 CN CN202210673586.2A patent/CN115067997A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fu et al. | Clinical applications of superb microvascular imaging in the superficial tissues and organs: a systematic review | |
| Wilhjelm et al. | Quantitative analysis of ultrasound B-mode images of carotid atherosclerotic plaque: correlation with visual classification and histological examination | |
| Ho et al. | Using ultrasound Nakagami imaging to assess liver fibrosis in rats | |
| Toyoda et al. | B-mode ultrasound with algorithm based on statistical analysis of signals: evaluation of liver fibrosis in patients with chronic hepatitis C | |
| Zhou et al. | Hepatic steatosis assessment with ultrasound small-window entropy imaging | |
| Mento et al. | On the influence of imaging parameters on lung ultrasound B-line artifacts, in vitro study | |
| Tsui et al. | Classification of breast masses by ultrasonic Nakagami imaging: a feasibility study | |
| Gao et al. | Correlation between twinkling artifact and color Doppler carrier frequency: preliminary observations in renal calculi | |
| JP2003061964A (en) | Ultrasonic diagnostic apparatus | |
| Ma et al. | A computer-aided diagnosis scheme for detection of fatty liver in vivo based on ultrasound kurtosis imaging | |
| DE102004060396A1 (en) | Method and system for classifying a breast lesion | |
| Liao et al. | Strain‐compounding technique with ultrasound Nakagami imaging for distinguishing between benign and malignant breast tumors | |
| CN108670303B (en) | Method and system for detecting uniformity of ultrasonic image | |
| Lin et al. | Considerations of ultrasound scanning approaches in non-alcoholic fatty liver disease assessment through acoustic structure quantification | |
| Khairalseed et al. | Classification of thyroid nodules in H-scan ultrasound images using texture and prinicipal component analysis | |
| Molinari et al. | Accurate and automatic carotid plaque characterization in contrast enhanced 2-D ultrasound images | |
| Tsui et al. | Performance evaluation of ultrasonic Nakagami image in tissue characterization | |
| Khairalseed et al. | H-scan ultrasound imaging for the classification of thyroid tumors | |
| Wilson et al. | Initial clinical experience imaging scatterer size and strain in thyroid nodules | |
| Bahbah et al. | Linear and nonlinear characterization of microbubbles and tissue using the Nakagami statistical model | |
| Luo et al. | [Retracted] Intelligent Noise Reduction Algorithm to Evaluate the Correlation between Human Fat Deposits and Uterine Fibroids under Ultrasound Imaging | |
| CN115067997A (en) | Biological tissue abnormality characterization method based on fractal analysis | |
| Fakhr et al. | Automatic diagnosis of liver diseases from ultrasound images | |
| Kumon et al. | Characterization of the pancreas in vivo using EUS spectrum analysis with electronic array echoendoscopes | |
| Muhtadi et al. | Analyzing the texture of Nakagami parametric images for classification of breast cancer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |