CN-122023272-A - Bone density detection phantom uniformity failure detection and early warning method and system

CN122023272ACN 122023272 ACN122023272 ACN 122023272ACN-122023272-A

Abstract

The invention discloses a method and a system for detecting and early warning uniformity failure of a bone mineral density detection phantom, which relate to the technical field of bone mineral density detection and specifically comprise the following steps: acquiring a potassium dihydrogen phosphate calibration rod area in a CT image, reading an edge line trend identification abnormal graph, extracting a gray level transition extension section, tracking the direction change in a multi-frame image, judging spatial coincidence, analyzing the structure texture continuing relationship, transversely comparing the layer boundary trend, and outputting a failure early warning result. According to the method, continuous trend and fracture characteristics of edge lines in an image are extracted, a gray scale extending path is combined to track direction change in a multi-frame image, an overlapping space overlapping region identifies an edge structure extending state, a relationship between pattern layer trend is judged by analyzing texture continuing and arrangement directions, an abnormal region is extracted by transversely comparing structure boundary trend differences, a comparison mode of structure extending trend and boundary trend stability in an image sequence is constructed, and identification and early warning response of abnormal states in the image are promoted.

Inventors

XU CHENG
XIONG DOU
ZHOU HAO
WANG KAIXUAN
ZHANG HAO
TANG PEIFU
LI JIANTAO
ZHANG WEI
LI MENG
JIA ZHENGFENG
ZHANG ZICHENG
ZHAO ZIXIN
MO FUHAO
XIA YANWEI

Assignees

中国人民解放军总医院第四医学中心

Dates

Publication Date: 20260512
Application Date: 20251231

Claims (10)

1. The bone mineral density detection phantom uniformity failure detection and early warning method is characterized by comprising the following steps of: S1, acquiring a potassium dihydrogen phosphate calibration rod area in a CT image, sequentially reading the trend of edge lines in the image frame, and judging whether intermittent, incomplete closure and abrupt shape continuous patterns exist or not to obtain an edge state abnormal pattern list; s2, selecting adjacent image areas based on the edge state abnormal graph list, extracting a gray level transition continuous extension range, judging whether symmetrical trend features exist or not, and obtaining edge interference line extension section information; S3, tracking the position change of the gray scale extension line in the multi-frame image based on the information of the extension section of the edge interference line, judging whether the directions are continuously consistent, and analyzing whether the extension ranges are spatially coincident to obtain an edge extension trend continuous section sequence; s4, based on the continuous section sequence of the edge extension trend, scanning the structural form of the image, extracting the continuous line combination after fracture, and analyzing whether a cross texture area is formed or not to obtain an image change consistency area pattern; and S5, judging whether the boundary direction of the front and rear image structures is consistent with the expansion or not based on the image change consistency region pattern and the transverse comparison structure gray scale trend distribution, and identifying the inter-layer dislocation image region to obtain a uniformity failure early warning classification result.
2. The bone mineral density detection phantom uniformity failure detection and early warning method according to claim 1, wherein the edge state abnormal pattern list comprises an edge line intermittent pattern, a closed incomplete pattern and a shape abrupt pattern, the edge interference line extension section information comprises symmetrical trend characteristics, a gray level transition continuous extension range and adjacent structure connection characteristics, the edge extension trend continuous section sequence comprises a direction-invariant extension line, a space overlapping area and an interframe uniformity trend identifier, the image change uniformity area pattern comprises a post-fracture continuous line combination, a cross texture area and a trunk direction uniformity pattern layer, and the uniformity failure early warning classification result comprises a structure boundary change type, an image interframe extension trend and a failure early warning condition trigger item.
3. The method for detecting and early warning the homogeneity failure of a bone mineral density detection phantom according to claim 1, wherein the calibration rod region refers to an image region in which a density rod is embedded in a potassium dihydrogen phosphate phantom which is used for carrying out density standard comparison in a CT image; The gray level transition continuous extension range refers to a gray level transition section in which the gray level value of the edge line is gradually changed and continuously extends to an adjacent area in the image.
4. The method for detecting and early warning the uniformity failure of a bone mineral density detection phantom according to claim 1, wherein the combination of continuous lines refers to a line segment set which extends in adjacent frames after an edge line in an image is broken, has consistent directions and has a connection relationship from end to end; The structural gray scale trend distribution refers to the distribution state of the arrangement direction and trend of gray scale lines in the image structural area in continuous frame images.
5. The bone mineral density detection phantom uniformity failure detection and early warning method according to claim 1, wherein the specific steps of S1 are as follows: S101, acquiring a potassium dihydrogen phosphate calibration rod area in a CT image, reading an edge gray level jump line in an image frame, comparing HU value sequences at two sides of the jump line, and screening lines with numerical value differences with adjacent background gray levels to obtain an edge jump line index set; S102, extracting hopping line directions in continuous image frames based on the edge hopping line index set, tracking lines with direction angles changed in adjacent frames, and screening edge trend lines with continuous fluctuation of extension directions to obtain a hopping direction abrupt line segment group; s103, based on the jump direction abrupt change line segment group, comparing connection conditions of lines in the same frame in an image sequence, screening structure areas with broken edges and closed breaks among the line segments, and identifying patterns with continuously changed morphological structures to obtain an edge state abnormal pattern list.
6. The bone mineral density detection phantom uniformity failure detection and early warning method according to claim 1, wherein the specific steps of S2 are as follows: S201, based on the edge state abnormal graph list, acquiring corresponding areas in adjacent image frames, extracting gray scale extension line segments for connecting boundary lines, and comparing whether gray scale changes between the extension areas and the background are continuous or not to obtain edge extension gray scale transition sections; s202, screening the directions of extension lines in front and rear image frames based on the edge extension gray level transition sections, and analyzing whether symmetrical features consistent with the directions of adjacent structures exist or not to obtain extension direction symmetrical segment information; And S203, extracting extension line coverage range in the image frame based on the extension direction symmetrical segment information, and judging whether an extension starting point continuously penetrates through the boundary of the area or not to obtain edge interference line extension section information.
7. The bone mineral density detection phantom uniformity failure detection and early warning method according to claim 1, wherein the specific steps of S3 are as follows: S301, tracking corresponding coordinates of gray scale extension lines in continuous image frames based on the edge interference line extension section information, recording trend directions of head and tail positions of the extension lines in each frame of images, and identifying line sections with unchanged directions to obtain extension line direction consistent fragments; S302, based on the segments with consistent extending line directions, extracting start and stop boundaries of extending areas in the same direction in a multi-frame image, measuring space coincidence conditions between a transverse range and longitudinal frame sequence positions, and identifying continuous covering segments among the areas to obtain space extending coincidence ranges; S303, based on the space extension overlapping range, arranging overlapping area boundary sections according to the image frame sequence, screening adjacent sections which are continuously distributed, judging whether a stable area is formed or not according to gray scale communication degree, and obtaining an edge extension trend continuous section sequence.
8. The bone mineral density detection phantom uniformity failure detection and early warning method according to claim 1, wherein the specific steps of S4 are as follows: S401, based on the continuous section sequence of the edge extension trend, sequentially scanning a structural area according to an image sequence, collecting adjacent trend line segments after extension of image break lines, screening head-tail continuous positions of the line segments, analyzing the arrangement relation of structural boundary line segments between an extension starting point and a continuous tail end, and obtaining an extension continuous line comparison set; S402, based on the extended continuous line comparison set, comparing the arrangement directions of textures in the adjacent structures with the adjacent line segments, extracting the continuous overlapping region of the arrangement angle concentrated position and the direction, and eliminating the graphic fragments with abrupt changes in direction to obtain a similar fragment set of continuous textures; S403, based on the similar segment set of the continuing texture direction, matching the line segment direction in the edge of the layer structure with the trend sequence of the structural trunk, screening the aligned segments with consistent direction angle change, extracting the corresponding structure range, and obtaining the image change consistency region pattern.
9. The bone mineral density detection phantom uniformity failure detection and early warning method according to claim 1, wherein the specific steps of S5 are as follows: S501, based on the image change consistency region pattern, positioning the structural boundary of an extension region according to an image sequence, extracting structural blocks at the same position in transversely adjacent image frames, removing unconnected fragments outside edges, and marking out a boundary range with continuous characteristics in continuous frames to obtain a structural extension comparison set; s502, extracting an arrangement track of gray-scale lines in an image frame in a structure based on the structure extension comparison set, tracking the directions of start and stop endpoints frame by frame, comparing the track direction changes between adjacent frames, and obtaining a continuous direction alignment line section by extracting line combinations with consistent directions; and S503, based on the alignment of the line sections in the continuous direction, comparing the position relation between the front and rear image frames and the boundary of the extension structure, extracting offset and staggered line trend, corresponding to gray-scale line expression and failure characteristics, and screening image fragments with abnormal indications to obtain a uniformity failure early warning classification result.
10. A bone mineral density detection phantom uniformity failure detection and early warning system, wherein the system is configured to implement the bone mineral density detection phantom uniformity failure detection and early warning method according to any one of claims 1-9, the system comprising: the image edge extraction module is used for acquiring a potassium dihydrogen phosphate calibration rod area in the CT image, sequentially reading the trend of edge lines in the image frame, judging whether intermittent, incomplete closure and abrupt shape continuous patterns exist or not, and obtaining an edge state abnormal pattern list; The interference extension recognition module is used for selecting adjacent image areas based on the edge state abnormal graph list, extracting a gray level transition continuous extension range, judging whether symmetrical trend features exist or not, and obtaining edge interference line extension section information; The spatial trend tracking module is used for tracking the position change of the gray scale extension line in the multi-frame image based on the information of the extension section of the edge interference line, judging whether the directions are continuously consistent or not, and analyzing whether the extension ranges are spatially coincident or not to obtain an edge extension trend continuous section sequence; The texture trend analysis module is used for scanning the image structure form based on the edge extension trend continuous section sequence, extracting a continuous line combination after fracture, and analyzing whether a cross texture area is formed or not to obtain an image change consistency area pattern; and the uniformity early warning judging module is used for judging whether the boundary direction of the front and rear image structures is consistent with the expansion or not based on the pattern of the image change uniformity region and the gray scale trend distribution of the transverse comparison structure, and identifying the image region with dislocation between the image layers to obtain a uniformity failure early warning classification result.

Description

Bone density detection phantom uniformity failure detection and early warning method and system Technical Field The invention relates to the technical field of bone mineral density detection, in particular to a method and a system for detecting and early warning uniformity failure of a bone mineral density detection phantom. Background The technical field of bone mineral density detection relates to quantitative evaluation and structural analysis of mineral content in human bone tissue, belongs to quantitative image measurement branches in bone mineral density detection, and comprises a bone mineral density calculation method based on three-dimensional image data, density calibration phantom design, phantom uniformity control means and development of related image evaluation tools. The traditional bone mineral density detection phantom uniformity failure detection is to monitor physical property change conditions such as uneven material density, bubble generation, solute precipitation and the like which possibly occur after the use time of a K2HPO4 liquid-soluble phantom is prolonged in QCT measurement, the detection matters are usually implemented by manually comparing and judging abnormity of HU value distribution of a calibration rod area inside the phantom in a CT image, and particularly according to gray stability and contrast performance of each known density module in the image, whether the phantom is subjected to uniformity failure is judged by observing local high-low density difference and identifying whether HU value mutation occurs in a bubble formation area, and a maintenance or replacement scheme is formulated accordingly so as to ensure the calibration precision of the QCT system. In the prior art, depending on a fixed region comparison method of a static image gray scale value, it is difficult to accurately identify a density continuous change trend in a scene with a plurality of frames of image changes or insignificant region density gradual change, dynamic analysis is performed due to unbound image edge trend, structure texture and inter-image extension direction change, and under early abnormal states such as slow solute precipitation or local bubble generation, the image gray scale fluctuation presents low-amplitude and non-centralized characteristics, signal characteristics in a single frame of image are not easily ignored obviously, so that a density non-uniform region cannot be identified in time, the reliability of a detection image and the accurate grasp of a body model calibration state are affected, the overall monitoring sensitivity is reduced, and the intervention time of body model performance change is delayed. Disclosure of Invention In order to solve the technical problems in the prior art, the embodiment of the invention provides a method and a system for detecting and early warning uniformity failure of a bone density detection phantom, which have the following specific technical scheme: In one aspect, a method for detecting and early warning uniformity failure of a bone mineral density detection phantom is provided, which comprises the following steps: S1, acquiring a potassium dihydrogen phosphate calibration rod area in a CT image, sequentially reading the trend of edge lines in the image frame, and judging whether intermittent, incomplete closure and abrupt shape continuous patterns exist or not to obtain an edge state abnormal pattern list; s2, selecting adjacent image areas based on the edge state abnormal graph list, extracting a gray level transition continuous extension range, judging whether symmetrical trend features exist or not, and obtaining edge interference line extension section information; S3, tracking the position change of the gray scale extension line in the multi-frame image based on the information of the extension section of the edge interference line, judging whether the directions are continuously consistent, and analyzing whether the extension ranges are spatially coincident to obtain an edge extension trend continuous section sequence; s4, based on the continuous section sequence of the edge extension trend, scanning the structural form of the image, extracting the continuous line combination after fracture, and analyzing whether a cross texture area is formed or not to obtain an image change consistency area pattern; and S5, judging whether the boundary direction of the front and rear image structures is consistent with the expansion or not based on the image change consistency region pattern and the transverse comparison structure gray scale trend distribution, and identifying the inter-layer dislocation image region to obtain a uniformity failure early warning classification result. As a further scheme of the invention, the edge state abnormal graph list comprises an edge line intermittent graph, a closed incomplete graph and a shape abrupt graph, the edge interference line extension section information comprises symmetrical trend characteri