WO2022111195A1 - 血管瘤瘤体颜色量化评估系统及评估方法 - Google Patents

血管瘤瘤体颜色量化评估系统及评估方法 Download PDF

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WO2022111195A1
WO2022111195A1 PCT/CN2021/126742 CN2021126742W WO2022111195A1 WO 2022111195 A1 WO2022111195 A1 WO 2022111195A1 CN 2021126742 W CN2021126742 W CN 2021126742W WO 2022111195 A1 WO2022111195 A1 WO 2022111195A1
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tumor
image
rgb
paratumor
hemangioma
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PCT/CN2021/126742
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French (fr)
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谢明峰
刘潜
徐仙贇
刘海金
黄海金
陈枫
李林福
曾林山
曾勇
阎金龙
彭威
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赣南医学院
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/1032—Determining colour for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4842—Monitoring progression or stage of a disease
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
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    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/04—Babies, e.g. for SIDS detection
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    • G06T2207/30096—Tumor; Lesion
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    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G—PHYSICS
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    • G06V2201/03—Recognition of patterns in medical or anatomical images

Definitions

  • the invention relates to the field of medical technology, in particular to a quantitative evaluation system and an evaluation method for the color of a hemangioma tumor.
  • Infantile Hemangioma is the most common benign tumor in infants and young children. It develops from the soft tissue of infants and young children. It is an imbalance of vascular homeostasis caused by abnormal vascular development and formation during embryonic period. tumor. The incidence rate is 4% to 10%, and the incidence is higher in premature infants and low birth weight infants (infants with birth weight ⁇ 1000 g). Occurs on the face, limbs and other body surfaces, seriously affecting the patient's appearance, vision and mental health; IH occurring in the respiratory tract, intracranial and other parts can cause respiratory obstruction, intracranial hemorrhage and other critical symptoms, and even cause death, which is a serious hazard One of the most common diseases in children's physical and mental health.
  • typical IH is divided into three phases: proliferative phase, plateau phase, and regression phase.
  • Natural history lesions appear 1 to 2 weeks after birth, rapidly proliferate within 6 months, and then gradually stop growing and enter a spontaneous slow resolution. fade, a process that can last for several years.
  • hemangioma according to the degree of infiltration of IH tumor, it can be divided into superficial type, deep type and mixed type.
  • Superficial infantile hemangioma is often referred to as "strawberry hemangioma" according to its shape, which is a bright red mass with irregular protrusions on the surface, surrounded by normal skin tissue.
  • the patient's condition gradually improved after oral propranolol treatment. This process is manifested as: the diameter of the tumor decreases, the degree of bulge decreases, the local skin temperature changes, the color of the tumor changes, and the blood flow in the tumor cavity decreases.
  • the current evaluation methods for the efficacy of infantile hemangioma include: Achauer percentile quartile, visual analog scale, infantile hemangioma activity score, imaging-assisted methods (ultrasound, CT, MRI), The skin temperature method, the measurement of body fluids and biochemical indicators, however, does not have an accurate, effective and objective evaluation method.
  • the color of infantile hemangioma can indirectly reflect its proliferation and regression, thus becoming an important indicator for evaluating the condition of children.
  • clinicians’ visual evaluation with a certain subjective color is commonly used, which causes great trouble to objectively and accurately evaluate the color of tumor and reflect the condition of children.
  • the purpose of the present invention is to overcome the problems of inaccurate and inconvenient evaluation of hemangioma in the prior art, and to provide a quantitative evaluation system and evaluation method for hemangioma, which have the advantages of high accuracy, convenience and practicality, and easy promotion. .
  • the present invention provides a hemangioma tumor color quantification evaluation system, the evaluation system includes an image acquisition and image acquisition module, an image analysis module, a result evaluation module and a display module;
  • the image acquisition module includes a light source device, an imaging device, and a storage device; the image of the hemangioma is collected through the imaging device, and then the image of the hemangioma is stored in the storage device;
  • the image analysis module includes a digital signal processor and digital analysis software; the digital signal processor divides the pixel points of the hemangioma image into a tumor image (31) and a tumor-side image (32); the digital analysis software Calculate the rgb comprehensive value of the tumor image (31) and the adjacent tumor image (32), and the results are recorded as the tumor rgb comprehensive value and the adjacent tumor rgb comprehensive value respectively;
  • the display module shown contains a display for displaying images and numerical values.
  • the light source device includes a light source box and a light shield; the light source box includes an infrared filter.
  • the distance between the imaging device and the imaging object is fixed, preferably 1 m.
  • the digital signal processor classifies the pixel points of the hemangioma image into a red area and a normal area, and the red area is the tumor image, with the largest diameter of the tumor as the diameter and the midpoint of the diameter as the origin, making a plane right angle Coordinate system, in the four directions of the coordinate system, 1cm ⁇ 0.5mm outside the edge of the tumor body, and four normal areas with an area of 1cm ⁇ 0.5cm 2 are the images around the tumor ; the images next to the tumor are divided into four areas, which are Paratumor I, Paratumor II, Paratumor III, and Paratumor IV.
  • the digital analysis software converts the tumor image and the adjacent tumor image into red, green, basket color values and rgb values, which are respectively recorded as R, G, B, and rgb comprehensive values; the tumor image rgb comprehensive values are recorded as is the rgb comprehensive value of the tumor body; the rgb comprehensive value of the four regions of the adjacent tumor image, the adjacent tumor I, the adjacent tumor II, the adjacent tumor III, and the adjacent tumor IV, is averaged and recorded as the adjacent tumor rgb comprehensive value.
  • the present invention provides a method for quantitatively evaluating the color of a hemangioma tumor, wherein the method for quantitatively evaluating the color of a hemangioma tumor is performed in the system for quantitatively evaluating the color of a hemangioma tumor.
  • the evaluation method described includes the following steps:
  • the imaging device captures an image of the hemangioma, and stores the image of the hemangioma in a storage device;
  • the digital signal processor divides the hemangioma image into a tumor body image (31) and a tumor adjacent image (32);
  • the digital analysis software calculates the rgb comprehensive value of the tumor image (31) and the adjacent tumor image (32), and the results are recorded as the tumor body rgb comprehensive value and the adjacent tumor rgb comprehensive value respectively;
  • the display screen displays the image of the hemangioma, the image of the tumor body (31), the image of the adjacent tumor (32), the rgb comprehensive value of the tumor body, the comprehensive value of the rgb adjacent to the tumor, and the therapeutic effect evaluation coefficient.
  • the system stability calibration process includes the following steps:
  • the step of dividing the adjacent tumor image includes: 1) selecting the center of the tumor image as the origin, and using the first quadrant axis, the second quadrant axis, the third quadrant axis and the fourth quadrant axis The axis divides the tumor image into four quadrants;
  • the paratumor image is divided into four regions, which are Paratumor I, Paratumor II, Paratumor III, and Paratumor IV.
  • the paratumor rgb comprehensive value is the average value of the rgb comprehensive values of the four regions of the paratumor image: paratumor I, paratumor II, paratumor III, and paratumor IV.
  • the system and method for quantitative evaluation of hemangioma provided by the present invention are more accurate and effective, convenient and practical, and have extremely high promotion value.
  • the hemangioma tumor color quantification evaluation system and evaluation method provided by the invention have the advantages of objectively quantifying the depth of tumor color, more accurately reflecting the condition of children, and guiding doctors to treat.
  • FIG. 1 is a schematic diagram of the quadrant axis of a paratumor image divided by an image analysis module according to a preferred embodiment of the present invention
  • FIG. 2 is a schematic diagram of a paratumor image divided by an image analysis module provided according to a preferred embodiment of the present invention
  • 3 is a tumor image divided by an image analysis module provided according to a preferred embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a quantitative assessment system for hemangioma according to the present invention.
  • FIG. 5 is a schematic diagram of the quantitative assessment method for hemangioma according to the present invention.
  • the present invention provides a hemangioma tumor color quantification evaluation system
  • the evaluation system includes an image acquisition and image acquisition module, an image analysis module, a result evaluation module and a display module
  • the image acquisition module includes light source equipment, imaging equipment, a storage device
  • the image analysis module includes a digital signal processor and digital analysis software
  • the digital signal processor stores the hemangioma image in the storage device
  • the image pixels are divided into tumor body image 31 and adjacent tumor image 32
  • the digital analysis software calculates the rgb comprehensive value of the tumor body image 31 and the adjacent tumor image 32, and the results are respectively recorded as the tumor body rgb comprehensive value and the adjacent tumor rgb comprehensive value.
  • the present invention provides a hemangioma tumor color quantification evaluation system, the evaluation system includes an image acquisition and image acquisition module, an image analysis module, a result evaluation module and a display module;
  • the image acquisition module includes a light source device, an imaging device, and a storage device; the image of the hemangioma is collected through the imaging device, and then the image of the hemangioma is stored in the storage device;
  • the light source equipment adopts a P120 light source box and a PHILIPS-TLD-36W/865 strip-shaped three-primary fluorescent lamp, the light source lamp holder model is G13, the color rendering index is 85, and the color temperature is 6500K light,
  • the light source can be used as a full-spectrum light source to ensure the spatial uniformity of light, so as to evenly project the light to the tumor body to be photographed.
  • the imaging device adopts an Olympus digital camera, model: D33235, with a total pixel of up to 10 million, an effective pixel of 9.3 million, a M.Zuiko professional lens, and a sensor of 17.4mm*13.0mm size CCD.
  • the storage device is any one or more of a network cloud disk, a semiconductor memory, a magnetic surface memory, and an optical surface memory.
  • the light source device includes a light source box and a light shield to shield external light, which can eliminate the influence of ambient light on the measurement system.
  • an infrared filter is included in the light source box, which can effectively reduce the temperature of the output light, thereby reducing the influence of the self-heating temperature of the light on the skin color, and ensuring that the captured images are accurate reflect the patient's condition.
  • the distance between the imaging device and the imaging object is fixed at 1 meter, so as to ensure that the imaging object can be clearly imaged.
  • the imaging device is arranged on a bracket with an adjustable direction, so that the imaging device is more convenient to use.
  • the image analysis module includes a digital signal processor and digital analysis software
  • the digital signal processor and digital analysis software are ImageJ and/or Photoshop.
  • the digital signal processor divides the hemangioma image pixels into a tumor image 31 and a tumor-side image 32; the digital analysis software calculates the rgb comprehensive value of the tumor image 31 and the tumor-side image 32, and the results are respectively Recorded as tumor rgb comprehensive value, adjacent tumor rgb comprehensive value.
  • the digital signal processor classifies the pixels of the hemangioma image into a red area and a normal area, the red area is the tumor image 31, and the largest diameter of the tumor is the diameter, The midpoint of the diameter is the origin, and a plane rectangular coordinate system is used. In the four directions of the coordinate system, the distance from the outside of the tumor edge is 1 cm, and the four normal areas with an area of 1 cm are the adjacent tumor images 32; the adjacent tumor images 32 are divided into four The regions are respectively paratumor I321, paratumor II322, paratumor III323, and paratumor IV324.
  • the digital analysis software converts the tumor image 31 and the adjacent tumor image 32 into red, green, basket color values and rgb values, denoted as R, G, B, rgb respectively Comprehensive value; the 31 rgb comprehensive value of the tumor image is recorded as the tumor rgb comprehensive value; the rgb comprehensive values of the four regions of the adjacent tumor image 32, including the adjacent tumor I321, the adjacent tumor II 322, the adjacent tumor III 323, and the adjacent tumor IV 324, are averaged and recorded as the tumor.
  • the average rgb comprehensive value of the four para-tumor regions was taken as the para-tumor rgb comprehensive value to reduce systematic errors.
  • the shown display module includes a display screen for displaying images and numerical values, and the display screen may be a liquid crystal display screen.
  • another aspect of the present invention provides a method for quantifying the color of a hemangioma tumor, which is performed in the hemangioma quantification evaluation system described in the evaluation method, and the evaluation method includes the following steps:
  • the imaging device captures an image of the hemangioma, and stores the image of the hemangioma in a storage device;
  • the digital signal processor divides the hemangioma image into a tumor body image 31 and a tumor adjacent image 32;
  • the digital analysis software calculates the rgb comprehensive value of the tumor body image 31 and the adjacent tumor image 32, and the results are recorded as the tumor body rgb comprehensive value and the adjacent tumor rgb comprehensive value respectively;
  • the display screen displays the image of the hemangioma, the image of the tumor body 31, the image of the adjacent tumor 32, the comprehensive value of the rgb of the tumor body, the comprehensive value of the rgb of the adjacent tumor and the efficacy evaluation coefficient for the doctor's reference, evaluation, and guidance for treatment.
  • the stability of the measurement system is very important for the acquisition of high-quality images, and the stability of the measurement system is affected by the unstable light output of the light source, the spatial unevenness of the light, the change of the CCD spectral response characteristics, Photoelectric conversion device noise and other factors.
  • the system stability calibration process includes the following steps:
  • the step of dividing the adjacent tumor image 32 includes:
  • the paratumor image 32 is divided into four regions, which are paratumor I 321, paratumor II 322, paratumor III 323, and paratumor IV 324.
  • the comprehensive value of the paratumor rgb is the rgb of the four regions of the paratumor image 32, the paratumor I321, the paratumor II 322, the paratumor III 323, and the paratumor IV324.
  • the average value of the comprehensive value reducing the systematic error.
  • Tumor site Female 139 Average age at first consultation/month 3.48 ⁇ 1.96 Average treatment time/month 3.52 ⁇ 1.82 Tumor site: head and neck 61 Tumor site: Limbs 63 Tumor site: body 75 Tumor site: perineum 8
  • the hemangioma tumor color quantitative evaluation system provided by the present invention is used, and the patient's tumor is quantitatively evaluated by the evaluation scheme of the present invention.
  • the measurement results are shown in Table 2.
  • Table 2 The comprehensive value of tumor rgb, the comprehensive value of adjacent tumor rgb, and the evaluation coefficient of curative effect in children before and after treatment
  • Treatment time period Treatment 0 months Treatment for 1 month treatment for 2 months End of treatment
  • Tumor rgb comprehensive value 90.14 ⁇ 29.35 103.40 ⁇ 31.77 112.68 ⁇ 31.98 131.42 ⁇ 33.93
  • Peritumoral rgb comprehensive value 146.93 ⁇ 39.57 143.66 ⁇ 35.92 141.07 ⁇ 34.22 144.93 ⁇ 36.40
  • Efficacy evaluation coefficient -56.79 ⁇ 22.07 -40.21 ⁇ 14.94 -28.70 ⁇ 12.72 -13.17 ⁇ 6.44
  • the hemangioma tumor color quantification evaluation system of the present invention can objectively analyze the color of the tumor of the patient and prompt the condition of the patient; the clinician groups the tumors of the children according to the growth state, and analyzes and finds the proliferation stage and the plateau stage, the proliferation stage and the regression stage of the children. There were significant differences in the efficacy evaluation coefficients between the plateau phase and the regression phase.

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Abstract

本发明公开了一种血管瘤量化评估系统及评估方法，该评估系统包括图像采集模块、图像分析模块、结果评价模块和显示模块；所述图像采集模块包含光源设备、成像设备、存储设备；所述图像分析模块包括数字信号处理器和数字分析软件；所述数字信号处理器将所述血管瘤图像像素点划分为瘤体图像和瘤旁图像；所述数字分析软件计算瘤体图像和瘤旁图像的rgb综合值；所述结果评价模块包含疗效评价系数；所述疗效评价系数＝瘤体rgb综合值-瘤旁rgb综合值。本发明提供的血管瘤瘤体颜色量化评估系统和评估方法通过客观量化瘤体颜色的深浅程度，较准确反映患儿病情，指导医生治疗等优点。

Description

血管瘤瘤体颜色量化评估系统及评估方法
本申请要求于2020年11月25日提交中国专利局、申请号为202011338568.6、发明名称为“血管瘤瘤体颜色量化评估系统及评估方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及医疗技术领域，具体地涉及一种血管瘤瘤体颜色量化评估系统及评估方法。
背景技术
婴幼儿型血管瘤(Infantile Hemangioma，IH)为婴幼儿群体中最常见的一种良性肿瘤，由婴幼儿软组织发展而来，由于胚胎时期血管发育及形成调控异常所导致的血管稳态失衡性血管肿瘤。发病率为4％～10％，在早产儿、低体重儿(出生体重<1000g的婴儿)中发病率更高。好发于颜面、四肢等体表部位，严重影响患者容貌、视力和心理健康；而发生在呼吸道、颅内等部位的IH可致呼吸道阻塞、颅内出血等危急症状，甚至引起死亡，是严重危害小儿身心健康最常见疾病之一。从生物学角度，典型IH分成三期：增殖期、平台期、消退期，自然病程：出生后1～2周出现病灶，6月内以内快速增生，随后逐渐停止生长，并进入自发缓慢的消褪期，这一过程可持续数年。在血管瘤增殖阶段，根据IH瘤体浸润程度可分为浅表型、深部型和混合型。浅表型婴幼儿型血管瘤依据其形态常被称为“草莓状血管瘤”，呈鲜红色包块，表面有不规则突起，四周由正常皮肤组织所包绕。患儿经口服普萘洛尔治疗后，病情逐渐缓解。这过程表现为：瘤体直径减小，凸起程度减小，局部皮温改变，瘤体颜色变化，瘤体腔内血流减少等。
目前对婴幼儿型血管瘤的疗效的评价方法有：Achauer百分位数四分法、视觉模拟评分法、婴幼儿型血管瘤活动度评分法，影像学辅助法(超声、CT、MRI)，皮温法，体液、生化指标测定法，然而却没有一种准确、有效且客观的评价方法。婴幼儿型血管瘤瘤体颜色可间接反映其增生与消退的情况，从而成为评估患儿病情的重要指标。而目前针对患儿瘤体颜色评估，常用的是带有一定主观色彩的临床医师目测评估，这对客观、准确 评估瘤体颜色，反映患儿病情造成极大的困扰。
发明内容
本发明的目的是为了克服现有技术存在的血管瘤评估不准确、不方便的问题，提供一种血管瘤量化评估系统及评估方法，该评估系统具有精确度高、方便实用、易于推广的优点。
为了实现上述目的，本发明提供一种血管瘤瘤体颜色量化评估系统，该评估系统包括图像采图像采集模块、图像分析模块、结果评价模块和显示模块；
所述图像采集模块包含光源设备、成像设备、存储设备；通过成像设备采集血管瘤图像，再将血管瘤图像储存于存储设备中；
所述图像分析模块包括数字信号处理器和数字分析软件；所述数字信号处理器将所述血管瘤图像像素点划分为瘤体图像(31)和瘤旁图像(32)；所述数字分析软件计算瘤体图像(31)和瘤旁图像(32)的rgb综合值，结果分别记为瘤体rgb综合值、瘤旁rgb综合值；
所述结果评价模块包含疗效评价系数；所述疗效评价系数＝瘤体rgb综合值-瘤旁rgb综合值；
所示显示模块包含显示屏，用于显示图像和数值。
优选地，所述光源设备包含光源箱和遮光罩；所述光源箱中包含红外滤光片。
优选地，所述成像设备与成像对象的距离固定，距离优选为1m。
优选地，所述数字信号处理器对血管瘤图像的像素点分类，区分为红色区域和正常区域，红色区域为瘤体图像，以瘤体最大径为直径，直径中点为原点，做平面直角坐标系，在坐标系四个方向距离瘤体边缘外侧1cm±0.5mm，四个面积为1cm 2±0.5cm 2的正常区域为瘤旁图像；所述瘤旁图像分为四个区域，分别为瘤旁Ⅰ、瘤旁Ⅱ、瘤旁Ⅲ、瘤旁Ⅳ。
优选地，所述数字分析软件将瘤体图像和瘤旁图像转化输出为红、绿、篮颜色值及rgb值，分别记为R、G、B，rgb综合值；瘤体图像rgb综合值记为瘤体rgb综合值；瘤旁图像的四个区域瘤旁Ⅰ、瘤旁Ⅱ、瘤旁Ⅲ、瘤旁Ⅳ的rgb综合值求平均值后记为瘤旁rgb综合值。
根据本发明的第二方面，本发明提供一种血管瘤瘤体颜色量化评估方 法，所述血管瘤瘤体颜色量化评估方法在本发明所述的血管瘤瘤体颜色量化评估系统中进行，所述评估方法包括以下步骤：
S1、打开光源设备和成像设备，进行系统稳定性校准过程，直至系统稳定性达标；
S2、成像设备拍摄血管瘤图像，并将血管瘤图像储存到存储设备中；
S3、数字信号处理器将血管瘤图像划分为瘤体图像(31)和瘤旁图像(32)；
S4、数字分析软件计算瘤体图像(31)和瘤旁图像(32)的rgb综合值，结果分别记为瘤体rgb综合值、瘤旁rgb综合值；
S5、计算疗效评价系数，所述疗效评价系数＝瘤体rgb综合值-瘤旁rgb综合值；
S6、显示屏显示血管瘤图像、瘤体图像(31)、瘤旁图像(32)、瘤体rgb综合值、瘤旁rgb综合值和疗效评价系数。
优选地，步骤S1中，所述系统稳定性校准过程包括以下步骤：
a、将均匀的白色漫反射板放在测量区域，通过调节相机或光源箱的光阑大小，使得所拍摄图像的RGB值中有一分量达255；
b、将一灰色标准板放在测量区域，通过调整相机参数使输出的成像图像RGB三分量依次为122，122，121；
c、将一黑色标准板放在测量区域和/或盖上镜头盖，通过调整相机参数使输出的成像图像RGB三分量均为0；
d、将一白色标准板放到测量区域，通过调整相机参数使输出的成像图像RGB值均为255。
优选地，步骤S3中，所述瘤旁图像的划分步骤包括：1)选取所述瘤体图像的中心为原点，并利用第一象限轴、第二象限轴、第三象限轴和第四象限轴将所述瘤体图像均分为四个象限；
2)在每个象限轴上距离所述瘤体图像外侧1±0.5cm处选取面积为1±0.5cm 2的图像为瘤旁图像。
优选地，所述瘤旁图像分为四个区域，分别为瘤旁Ⅰ、瘤旁Ⅱ、瘤旁Ⅲ、瘤旁Ⅳ。
优选地，步骤S3中，所述瘤旁rgb综合值为瘤旁图像的四个区域瘤 旁Ⅰ、瘤旁Ⅱ、瘤旁Ⅲ、瘤旁Ⅳ的rgb综合值的平均值。
本发明提供的血管瘤量化评估系统和方法相比于现有技术的目测测量更加准确有效，且方便实用，推广价值极高。
本发明提供的血管瘤瘤体颜色量化评估系统和评估方法通过客观量化瘤体颜色的深浅程度，较准确反映患儿病情，指导医生治疗等优点。
附图说明
图1为根据本发明的一种优选实施方式提供的图像分析模块划分瘤旁图像象限轴示意图；
图2为根据本发明的一种优选实施方式提供的图像分析模块划分出的瘤旁图像示意图；
图3为根据本发明的一种优选实施方式提供的图像分析模块划分出的瘤体图像；
图4为本发明血管瘤量化评估系统结构示意图；
图5为本发明血管瘤量化评估方法示意图。
附图标记说明
31——瘤体图像；
311——第一象限轴；
312——第二象限轴；
313——第三象限轴；
314——第四象限轴；
32——瘤旁图像；
321——瘤旁Ⅰ；
322——瘤旁Ⅱ；
323——瘤旁Ⅲ；
324——瘤旁Ⅳ。
具体实施方式
以下结合附图对本发明的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本发明，并不用于限制本发明。
如前述，本发明提供一种血管瘤瘤体颜色量化评估系统，该评估系统 包括图像采图像采集模块、图像分析模块、结果评价模块和显示模块；所述图像采集模块包含光源设备、成像设备、存储设备；通过成像设备采集血管瘤图像，再将血管瘤图像储存于存储设备中；所述图像分析模块包括数字信号处理器和数字分析软件；所述数字信号处理器将所述所述血管瘤图像像素点划分为瘤体图像31和瘤旁图像32；所述数字分析软件计算瘤体图像31和瘤旁图像32的rgb综合值，结果分别记为瘤体rgb综合值、瘤旁rgb综合值；所述结果评价模块包含疗效评价系数；所述疗效评价系数＝瘤体rgb综合值-瘤旁rgb综合值；所示显示模块包含显示屏，用于显示图像和数值。采用本发明的前述方案，通过客观量化瘤体颜色的深浅程度，较准确反映患儿病情，指导医生治疗等优点。
参阅图1-图3所示，本发明提供一种血管瘤瘤体颜色量化评估系统，该评估系统包括图像采图像采集模块、图像分析模块、结果评价模块和显示模块；
根据本发明，所述图像采集模块包含光源设备、成像设备、存储设备；通过成像设备采集血管瘤图像，再将血管瘤图像储存于存储设备中；
在本发明的一种优选实施方式中，所述光源设备采用P120光源箱和PHILIPS-TLD-36W/865条形三基色荧光灯管，光源灯头型号为G13，显色指数85，色温6500K的光，该光源可供全光谱光源，确保光线的空间均匀性，从而将光线均匀地投射到待拍摄的瘤体部位。
在本发明的一种优选实施方式中，所述成像设备采用Olympus数码相机，型号：D33235，总像素可达1000万，有效像素930万，使用M.Zuiko专业镜头，传感器为17.4mm*13.0mm大小的CCD。
根据本发明，所述存储设备为网络云盘、半导体存储器、磁表面存储器、光表面存储器中的任意一种或多种。
在本发明的一种优选实施方式中，所述光源设备包含光源箱和遮光罩，屏蔽外界光线，这可以消除环境光线对测量系统的影响。
在本发明的一种优选实施方式中，所述光源箱中包含红外滤光片这可有效降低输出光的温度，从而减小灯光的自热温度对皮肤颜色的影响，保证所采集图像能准确反映患儿病情。
在本发明的一种优选实施方案中，所述成像设备与成像对象的距离固 定为1米，从而保证能对成像对象清楚成像。
在本发明的一种优选实施方案中，将成像设备安置在一个可调节方向的支架上，使得成像设备更加方便使用。
根据本发明，所述图像分析模块包括数字信号处理器和数字分析软件；
根据本发明的一种优选实施方式，所述数字信号处理器和数字分析软件为ImageJ和/或Photoshop。
所述数字信号处理器将所述所述血管瘤图像像素点划分为瘤体图像31和瘤旁图像32；所述数字分析软件计算瘤体图像31和瘤旁图像32的rgb综合值，结果分别记为瘤体rgb综合值、瘤旁rgb综合值。
在本发明的一种优选实施方案中，所述数字信号处理器对血管瘤图像的像素点分类，区分为红色区域和正常区域，红色区域为瘤体图像31，以瘤体最大径为直径，直径中点为原点，做平面直角坐标系，在坐标系四个方向距离瘤体边缘外侧1cm，四个面积为1cm 2的正常区域为瘤旁图像32；所述瘤旁图像32分为四个区域，分别为瘤旁Ⅰ321、瘤旁Ⅱ322、瘤旁Ⅲ323、瘤旁Ⅳ324。
在本发明的一种优选实施方案中，所述数字分析软件将瘤体图像31和瘤旁图像32转化输出为红、绿、篮颜色值及rgb值，分别记为R、G、B，rgb综合值；瘤体图像31rgb综合值记为瘤体rgb综合值；瘤旁图像32的四个区域瘤旁Ⅰ321、瘤旁Ⅱ322、瘤旁Ⅲ323、瘤旁Ⅳ324的rgb综合值求平均值后记为瘤旁rgb综合值，取四个瘤旁区域的平均rgb综合值作为瘤旁rgb综合值，降低系统误差。
根据本发明，所述结果评价模块包含疗效评价系数；所述疗效评价系数＝瘤体rgb综合值-瘤旁rgb综合值；通过疗效评价系数降低患儿个体皮肤颜色差异对结果的影响，更准确反映患儿治疗疗效。
所示显示模块包含显示屏，用于显示图像和数值，所述显示屏可以为液晶显示屏。
参阅图4所示，本发明的另一方面，提供一种血管瘤瘤体颜色量化评估方法，该评估方法所述的血管瘤量化评估系统中进行，所述评估方法包括以下步骤：
S1、打开光源设备和成像设备，进行系统稳定性校准过程，直至系 统稳定性达标；
S2、成像设备拍摄血管瘤图像，并将血管瘤图像储存到存储设备中；
S3、数字信号处理器将血管瘤图像划分为瘤体图像31和瘤旁图像32；
S4、数字分析软件计算瘤体图像31和瘤旁图像32的rgb综合值，结果分别记为瘤体rgb综合值、瘤旁rgb综合值；
S5、计算疗效评价系数，所述疗效评价系数＝瘤体rgb综合值-瘤旁rgb综合值；
S6、显示屏显示血管瘤图像、瘤体图像31、瘤旁图像32、瘤体rgb综合值、瘤旁rgb综合值和疗效评价系数，供医生参考、评估，指导治疗。
在本发明的一种优选实施方案中，测量系统的稳定性对于高质量图像的获取至关重要，测量系统的稳定性受光源光线输出不稳定、光线空间不均匀、CCD光谱响应特性的变化、光电转化器件的噪声等因素的影响。为确保测量系统的稳定性，所述系统稳定性校准过程包括以下步骤：
a、将均匀的白色漫反射板放在测量区域，通过调节相机或光源箱的光阑大小，使得所拍摄图像的RGB值中有一分量达255；
b、将一灰色标准板放在测量区域，通过调整相机参数使输出的成像图像RGB三分量依次为122，122，121；
c、将一黑色标准板放在测量区域和/或盖上镜头盖，通过调整相机参数使输出的成像图像RGB三分量均为0；
d、将一白色标准板放到测量区域，通过调整相机参数使输出的成像图像RGB值均为255。
在本发明的一种优选实施方案中，所述步骤S3中，所述瘤旁图像32的划分步骤包括：
1)选取所述瘤体图像31的中心为原点，并利用第一象限轴311、第二象限轴312、第三象限轴313和第四象限轴314将所述瘤体图像均分为四个象限；
2)在每个象限轴上距离所述瘤体图像31外侧1cm处选取面积为1cm 2的图像为瘤旁图像32。
在本发明的一种优选实施方案中，所述瘤旁图像32分为四个区域，分别为瘤旁Ⅰ321、瘤旁Ⅱ322、瘤旁Ⅲ323、瘤旁Ⅳ324。
在本发明的一种优选实施方案中，所述步骤S3中，所述瘤旁rgb综合值为瘤旁图像32的四个区域瘤旁Ⅰ321、瘤旁Ⅱ322、瘤旁Ⅲ323、瘤旁Ⅳ324的rgb综合值的平均值，降低系统误差。
实施例
S1、安装血管瘤瘤体颜色量化评估系统：P120光源箱和PHILIPS-TLD-36W/865条形三基色荧光灯管(光源灯头型号为G13，显色指数85，色温6500K的光)，Olympus数码相机(型号：D33235，总像素可达1000万，有效像素930万，使用M.Zuiko专业镜头，传感器为17.4mm*13.0mm大小的CCD)，成像设备与成像对象的距离固定为1米，连接网络云盘和计算机系统，打开数字信号处理器Image J和数字分析软件Photoshop。
S2、进行系统稳定性校准：
a、将均匀的白色漫反射板放在测量区域，通过调节相机或光源箱的光阑大小，使得所拍摄图像的RGB值中有一分量达255；
b、将一灰色标准板放在测量区域，通过调整相机参数使输出的成像图像RGB三分量依次为122，122，121；
c、将一黑色标准板放在测量区域，通过调整相机参数使输出的成像图像RGB三分量均为0；
d、将一白色标准板放到测量区域，通过调整相机参数使输出的成像图像RGB值均为255。
S3、通过血管瘤瘤体颜色量化评估系统为血管瘤患者进行拍摄，分析，得到瘤体rgb综合值、瘤旁rgb综合值和疗效评价系数。
本研究中，共有218例浅表型婴幼儿型血管瘤患者参与，其中有6例患儿中途自行退出，5例因随访次数过少(少于4次)剔除，不再纳入本次研究，最后共计207例纳入本研究，本次研究临床资料参见表1，病人一般资料。
表1病人一般资料
类别  
年龄/月 3.48±1.96
男 68
女 139
平均首诊月龄/月 3.48±1.96
平均治疗时间/月 3.52±1.82
瘤体部位：头颈 61
瘤体部位：四肢 63
瘤体部位：躯体 75
瘤体部位：会阴 8
采用本发明提供的血管瘤瘤体颜色量化评估系统，并采用本发明的评估方案对患者的瘤体进行量化评估，测量结果见表2。
表2治疗前后患儿瘤体rgb综合值、瘤旁rgb综合值、疗效评价系数
治疗时间段 治疗0月 治疗1月 治疗2月 治疗结束
瘤体rgb综合值 90.14±29.35 103.40±31.77 112.68±31.98 131.42±33.93
瘤旁rgb综合值 146.93±39.57 143.66±35.92 141.07±34.22 144.93±36.40
疗效评价系数 -56.79±22.07 -40.21±14.94 -28.70±12.72 -13.17±6.44
表2结果进行方差分析，其中F＝438.847，p<0.01，方差结果表明患儿各治疗时间疗效评价系数之间存在显著差异。本发明血管瘤瘤体颜色量化评估系统可客观地分析患者瘤体颜色、提示患者病情；临床医生对患儿瘤体按照生长状态分组，分析发现患儿增殖期与平台期、增殖期与消退期、平台期与消退期疗效评价系数有显著差异。
以上结合附图和实施例详细描述了本发明的优选实施方式，但是，本发明并不限于此。在本发明的技术构思范围内，可以对本发明的技术方案进行多种简单变型，包括各个具体技术特征以任何合适的方式进行组合，为了避免不必要的重复，本发明对各种可能的组合方式不再另行说明。但这些简单变型和组合同样应当视为本发明所公开的内容，均属于本发明的保护范围。

Claims (11)

  1. 一种血管瘤瘤体颜色量化评估系统，其特征在于，所述评估系统包括图像采集模块、图像分析模块、结果评价模块和显示模块；
    所述图像采集模块包含光源设备、成像设备、存储设备；通过成像设备采集血管瘤图像，再将血管瘤图像储存于存储设备中；
    所述图像分析模块包括数字信号处理器和数字分析软件；所述数字信号处理器将所述血管瘤图像的像素点划分为瘤体图像(31)和瘤旁图像(32)；所述数字分析软件计算瘤体图像(31)和瘤旁图像(32)的rgb综合值，结果分别记为瘤体rgb综合值、瘤旁rgb综合值；
    所述结果评价模块包含疗效评价系数；所述疗效评价系数＝瘤体rgb综合值-瘤旁rgb综合值；
    所示显示模块包含显示屏，用于显示图像和数值。
  2. 根据权利要求1所述的评估系统，其特征在于，所述光源设备包含光源箱和遮光罩；所述光源箱中包含红外滤光片。
  3. 根据权利要求1所述的评估系统，其特征在于，所述成像设备与成像对象的距离固定。
  4. 根据权利要求1所述的评估系统，其特征在于，所述数字信号处理器对血管瘤图像的像素点分类，区分为红色区域和正常区域，红色区域为瘤体图像(31)，以瘤体最大径为直径，直径中点为原点，做平面直角坐标系，在坐标系四个方向距离瘤体边缘外侧1cm±0.5mm，四个面积为1cm 2±0.5cm 2的正常区域为瘤旁图像(32)；所述瘤旁图像(32)分为四个区域，分别为瘤旁Ⅰ(321)、瘤旁Ⅱ(322)、瘤旁Ⅲ(323)、瘤旁Ⅳ(324)。
  5. 根据权利要求4所述的评估系统，其中，所述数字分析软件将瘤体图像(31)和瘤旁图像(32)转化输出为红、绿、篮颜色值及rgb值，分别记为R、G、B，rgb综合值；瘤体图像(31)rgb综合值记为瘤体rgb综合值；瘤旁图像(32)的四个区域瘤旁Ⅰ(321)、瘤旁Ⅱ(322)、瘤旁Ⅲ(323)、瘤旁Ⅳ(324)的rgb综合值求平均值后记为瘤旁rgb综合值。
  6. 一种血管瘤瘤体颜色量化评估方法，其特征在于，所述血管瘤瘤体颜色量化评估方法在权利要求1～5中任意一项所述的血管瘤瘤体颜色量化评估系统中进行，所述评估方法包括以下步骤：
    S1、打开光源设备和成像设备，进行系统稳定性校准过程，直至系统稳定性达标；
    S2、成像设备拍摄血管瘤图像，并将血管瘤图像储存到存储设备中；
    S3、数字信号处理器将血管瘤图像划分为瘤体图像(31)和瘤旁图像(32)；
    S4、数字分析软件计算瘤体图像(31)和瘤旁图像(32)的rgb综合值，结果分别记为瘤体rgb综合值、瘤旁rgb综合值；
    S5、计算疗效评价系数，所述疗效评价系数＝瘤体rgb综合值-瘤旁rgb综合值；
    S6、显示屏显示血管瘤图像、瘤体图像(31)、瘤旁图像(32)、瘤体rgb综合值、瘤旁rgb综合值和疗效评价系数。
  7. 根据权利要求6所述的评估方法，其特征在于，步骤S1中，所述成像设备与成像对象的距离固定，距离为1m。
  8. 根据权利要求6所述的评估方法，其特征在于，步骤S1中，所述系统稳定性校准过程包括以下步骤：
    a、将均匀的白色漫反射板放在测量区域，通过调节相机或光源箱的光阑大小，使得所拍摄图像的RGB值中有一分量达255；
    b、将一灰色标准板放在测量区域，通过调整相机参数使输出的成像图像RGB三分量依次为122，122，121；
    c、将一黑色标准板放在测量区域和/或盖上镜头盖，通过调整相机参数使输出的成像图像RGB三分量均为0；
    d、将一白色标准板放到测量区域，通过调整相机参数使输出的成像图像RGB值均为255。
  9. 根据权利要求6～8任意一项所述的评估方法，其特征在于，步骤S3中，所述瘤旁图像(32)的划分步骤包括：
    1)选取所述瘤体图像(31)的中心为原点，并利用第一象限轴(311)、第二象限轴(312)、第三象限轴(313)和第四象限轴(314)将所述瘤体图像均分为四个象限；
    2)在每个象限轴上距离所述瘤体图像(31)外侧1±0.5cm处选取面积为1±0.5cm 2的图像为瘤旁图像(32)。
  10. 根据权利要求9所述的评估方法，其特征在于，所述瘤旁图像(32)分为四个区域，分别为瘤旁Ⅰ(321)、瘤旁Ⅱ(322)、瘤旁Ⅲ(323)、瘤旁Ⅳ(324)。
  11. 根据权利要求6所述的评估方法，其特征在于，步骤S3中，所述瘤旁rgb综合值为瘤旁图像(32)的四个区域瘤旁Ⅰ(321)、瘤旁Ⅱ(322)、瘤旁Ⅲ(323)、瘤旁Ⅳ(324)的rgb综合值的平均值。
PCT/CN2021/126742 2020-11-25 2021-10-27 血管瘤瘤体颜色量化评估系统及评估方法 WO2022111195A1 (zh)

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