TW202010472A - Image calibration method and detection device - Google Patents
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Abstract
Description
本發明有關於一種校正方法及檢測裝置,特別是有關於一種影像校正方法以及檢測裝置。 The invention relates to a correction method and a detection device, in particular to an image correction method and a detection device.
醫學超音波檢查是一種基於超音波的醫學影像診斷技術,可以顯現人體內肌肉、內臟器官的大小、結構和病灶。在醫學超音波檢查的應用中,都卜勒超音波大幅生了醫學超音波檢查的能力。藉由都卜勒超音波檢測人體內或生物體內流體的流動,例如是血液的流動方向以及速度都可以藉由都卜勒超音波量測,在心血管等相關醫學領域中特別可以提供有效的檢測功能。 Medical ultrasound examination is a medical imaging diagnosis technology based on ultrasound, which can show the size, structure and lesions of muscles and internal organs in the human body. In the application of medical ultrasound examination, Doppler ultrasound has greatly developed the ability of medical ultrasound examination. Use Doppler ultrasound to detect the flow of fluid in the human body or in vivo, for example, the direction and speed of blood flow can be measured by Doppler ultrasound, which can provide effective detection in cardiovascular and other related medical fields. Features.
然而,都卜勒超音波的觀察區域主要是藉由檢測者根據超音波所產生的圖像所選定。追蹤連續影像內的特定區域一直是被研究的議題,且超音波圖像會隨著檢測者手持超音波探頭時的晃動、移動、下壓、以及被測者本身的心跳、呼吸或移動等改變。由於都卜勒超音波的觀察區域是相對於超音波所產生的圖像來定位,因此超音波圖像的改變也會改變都卜勒超音波的觀察區域,進而降低都卜勒超音波的準確性。 However, the observation area of Doppler ultrasound is mainly selected by the detector based on the image generated by ultrasound. Tracking specific areas in continuous images has been the subject of research, and ultrasound images will change as the detector shakes, moves, depresses, and the subject's own heartbeat, breathing, or movement when holding the ultrasound probe . Since the observation area of Doppler ultrasound is positioned relative to the image generated by ultrasound, changes in the ultrasound image will also change the observation area of Doppler ultrasound, thereby reducing the accuracy of Doppler ultrasound Sex.
本發明提供一種影像校正方法,其可以有效校正觀察區域 在一動態影像中的位置。 The invention provides an image correction method, which can effectively correct the position of the observation area in a dynamic image.
本發明提供一種檢測裝置,其可以提供準確的檢測效果。 The invention provides a detection device, which can provide an accurate detection effect.
本發明的影像校正方法可以校正一觀察區域在一動態影像的位置,其中動態影像包括複數個影像幀。所述影像校正方法的步驟包括:於動態影像中的第一影像幀中決定觀察區域並取得觀察區域在第一影像幀中的中心位置;自第一影像幀決定第一特徵區域,其中第一特徵區域符合一灰階特徵;取得觀察區域在第一影像幀中的中心位置至第一特徵區域在第一影像幀中的中心位置的第一向量;在動態影像中第二影像幀以灰階特徵尋找第二影像幀的第二特徵區域;取得觀察區域在第二影像幀中的中心位置至第二特徵區域在第二影像幀中的中心位置的第二向量;以及以第二向量與第一向量之間的差值校正觀察區域在一第三影像幀中的位置。 The image correction method of the present invention can correct the position of an observation area in a dynamic image, where the dynamic image includes a plurality of image frames. The steps of the image correction method include: determining the observation area in the first image frame in the dynamic image and obtaining the center position of the observation area in the first image frame; determining the first characteristic area from the first image frame, wherein the first The feature area conforms to a gray scale feature; the first vector of the center position of the observation area in the first image frame to the center position of the first feature area in the first image frame is obtained; the gray scale of the second image frame in the dynamic image Feature search for the second feature area of the second image frame; obtain the second vector of the center position of the observation area in the second image frame to the center position of the second feature area in the second image frame; The difference between a vector corrects the position of the observation area in a third image frame.
在本發明的一實施例中,上述決定第一特徵區域的步驟包括:自第一影像幀決定一輪廓影像;自輪廓影像決定第一特徵區域,其中在第一特徵區域中的部分輪廓影像的像素具有相同的像素聯通標誌;以及自第一特徵區域所對應的部分第一影像幀取得灰階特徵。 In an embodiment of the invention, the step of determining the first feature region includes: determining a contour image from the first image frame; determining the first feature region from the contour image, wherein part of the contour image in the first feature region The pixels have the same pixel connection mark; and grayscale features are obtained from a portion of the first image frame corresponding to the first feature area.
在本發明的一實施例中,取得上述輪廓影像的步驟包括:取得第一影像幀的一斷開(open)影像以及一閉合(close)影像。所述斷開(open)影像為數位影像處理技術中形態學(morphology)的侵蝕(erosion)後再膨脹(dilation)的影像,所述閉合(close)影像為影像幀膨脹(dilation)後再侵蝕(erosion)的影像。將斷開影像減去閉合影像後取絕對值的影像決定為上述輪廓影像。 In an embodiment of the invention, the step of obtaining the outline image includes obtaining an open image and a close image of the first image frame. The open image is an erosion and dilation image of morphology in the digital image processing technology, and the close image is a dilation of the image frame and then erosion (erosion) image. The image whose absolute value is obtained by subtracting the closed image from the open image is determined as the outline image.
在本發明的一實施例中,上述決定第一特徵區域的步驟還 包括:以像素聯通判斷將輪廓影像區分為多個像素群,並將這些像素群中像素數目最多的像素群決定為上述的第一特徵區域。 In an embodiment of the invention, the step of determining the first characteristic region further includes: dividing the contour image into a plurality of pixel groups by pixel connectivity determination, and determining the pixel group with the largest number of pixels among the pixel groups as the above The first characteristic area.
在本發明的一實施例中,取得上述灰階特徵的方法還包括將特徵區域中部分影像幀的灰度共生矩陣(Gray-Level Co-occurrence Matrix,GLCM)決定為灰階特徵。 In an embodiment of the invention, the method for obtaining the gray-scale feature further includes determining a gray-level co-occurrence matrix (GLCM) of the partial image frames in the feature area as the gray-scale feature.
在本發明的一實施例中,上述決定至少一特徵區域的步驟還包括:將第一影像幀區分為多個子影像;以及在每個子影像中決定特徵區域。後續影像幀則參照第一影像幀區分為多個子影像,並在每個子影像中以灰階特徵尋找各自的特徵區域。 In an embodiment of the invention, the step of determining the at least one characteristic area further includes: dividing the first image frame into a plurality of sub-images; and determining the characteristic area in each sub-image. Subsequent image frames are divided into a plurality of sub-images with reference to the first image frame, and grayscale features are used to find respective feature regions in each sub-image.
在本發明的一實施例中,上述的動態影像為超音波影像,在使用者在動態影像中的影像幀決定觀察區域後還包括:取得對應至觀察區域中的部分影像幀的都卜勒訊號。 In an embodiment of the present invention, the above-mentioned dynamic image is an ultrasound image. After the user determines the observation area in the image frame of the dynamic image, the method further includes: acquiring a Doppler signal corresponding to a part of the image frame in the observation area .
本發明的檢測裝置用以檢測一生物體。檢測裝置包括影像擷取元件、處理單元、顯示單元、輸入界面。影像擷取元件自生物體取得動態影像,其中動態影像包括複數個影像幀。處理單元連接至影像擷取單元,並自影像擷取元件取得動態影像。顯示單元連接至處理單元,並用以顯示來自處理單元的動態影像。輸入界面連接至處理單元,由輸入界面自動態影像的第一影像幀決定一觀察區域。 The detection device of the present invention is used to detect a living body. The detection device includes an image capturing element, a processing unit, a display unit, and an input interface. The image capturing element obtains a dynamic image from a living body, where the dynamic image includes a plurality of image frames. The processing unit is connected to the image capture unit and obtains dynamic images from the image capture component. The display unit is connected to the processing unit and used to display the dynamic image from the processing unit. The input interface is connected to the processing unit, and an observation area is determined from the first image frame of the dynamic image by the input interface.
處理單元在決定觀察區域後,取得觀察區域在第一影像幀中的中心位置;自第一影像幀決定第一特徵區域,其中第一特徵區域符合一灰階特徵;取得觀察區域在第一影像幀中的中心位置至第一特徵區域在第一影像幀中的中心位置的一第一向量;在動態影像中一第二影像幀以灰 階特徵尋找第二影像幀的一第二特徵區域;取得觀察區域在第二影像幀中的中心位置至第二特徵區域在第二影像幀中的中心位置的第二向量;處理單元以第二向量與第一向量之間的差值校正觀察區域在一第三影像幀中的位置。 After determining the observation area, the processing unit obtains the center position of the observation area in the first image frame; the first characteristic area is determined from the first image frame, wherein the first characteristic area conforms to a gray-scale feature; the observation area is obtained in the first image A first vector from the center position in the frame to the center position of the first feature area in the first image frame; a second image frame in the dynamic image uses gray-scale features to find a second feature area of the second image frame; Obtain a second vector of the center position of the observation area in the second image frame to the center position of the second feature area in the second image frame; the processing unit corrects the observation area by the difference between the second vector and the first vector A position in a third image frame.
在本發明的一實施例中,上述的處理單元自第一影像幀決定一輪廓影像;自輪廓影像決定至少第一特徵區域,其中第一特徵區域中的部分輪廓影像的像素具有相同的像素聯通標誌;處理單元自第一特徵區域所對應的部分第一影像幀取得灰階特徵。 In an embodiment of the present invention, the processing unit determines a contour image from the first image frame; the contour image determines at least a first feature area, wherein a portion of the contour image pixels in the first feature area have the same pixel connection Flag; the processing unit obtains gray-scale features from a portion of the first image frame corresponding to the first feature area.
在本發明的一實施例中,上述的處理單元取得第一影像幀的斷開(open)影像以及閉合(close)影像;斷開(open)影像為數位影像處理技術中形態學(morphology)的侵蝕(erosion)後再膨脹(dilation)的影像,閉合(close)影像為影像幀膨脹(dilation)後再侵蝕(erosion)的影像;且處理單元將斷開影像減去閉合影像後取絕對值的影像決定為輪廓影像。 In an embodiment of the present invention, the processing unit obtains an open image and a close image of the first image frame; the open image is a morphology of digital image processing technology The erosion and dilation images, the closed image is the image frame dilation and erosion image; and the processing unit takes the absolute value after subtracting the closed image from the open image The image is determined to be an outline image.
在本發明的一實施例中,上述的處理單元以像素聯通判斷將輪廓影像區分為多個像素群,並將這些像素群中像素數目最多的像素群決定為第一特徵區域。 In an embodiment of the present invention, the above-mentioned processing unit divides the contour image into a plurality of pixel groups based on pixel connectivity determination, and determines the pixel group with the largest number of pixels among these pixel groups as the first feature region.
在本發明的一實施例中,上述的處理單元將輪廓影像區分為多個子影像,並在每個子影像中決定第一特徵區域。處理單元對應第一影像幀將第二影像幀分為多個子影像,並在每個第二影像幀的子影像中以灰階特徵尋找第二特徵區域。 In an embodiment of the invention, the above-mentioned processing unit divides the contour image into a plurality of sub-images, and determines the first feature region in each sub-image. The processing unit divides the second image frame into a plurality of sub-images corresponding to the first image frame, and finds the second feature area with gray-scale features in the sub-images of each second image frame.
在本發明的一實施例中,上述的處理單元將第一特徵區域 中部分第一影像幀的灰度共生矩陣(Gray-Level Co-occurrence Matrix,GLCM)決定為灰階特徵。 In an embodiment of the present invention, the above-mentioned processing unit determines a gray-level co-occurrence matrix (GLCM) of a part of the first image frame in the first feature area as a gray-scale feature.
在本發明的一實施例中,上述的影像擷取元件包括一超音波探頭,且處理單元在使用者經輸入界面在動態影像中的第一影像幀決定觀察區域後,處理單元自超音波探頭取得對應至觀察區域中的部分影像幀的都卜勒訊號。 In an embodiment of the present invention, the above-mentioned image capturing element includes an ultrasound probe, and the processing unit selects the observation area after the user determines the observation area in the first image frame in the dynamic image via the input interface. Obtain the Doppler signal corresponding to some video frames in the observation area.
由上述可知,藉由灰階特徵來尋找動態影像中的每個影像幀的特徵區域的位置,本發明所提出的影像校正方法可以有效校正觀察區域在每個影像幀中的位置,本發明所提出的檢測裝置也可以針對校正過的影像幀中的觀察區域提供準確的檢測結果。 As can be seen from the above, by using gray-scale features to find the position of the feature area of each image frame in the dynamic image, the image correction method proposed by the present invention can effectively correct the position of the observation area in each image frame. The proposed detection device can also provide accurate detection results for the observation area in the corrected image frame.
A1、A2、D1、D2‧‧‧觀察區域 A1, A2, D1, D2 ‧‧‧ observation area
B1、B2、E11、E21、E31、E41‧‧‧特徵區域 B1, B2, E11, E21, E31, E41
C‧‧‧區域 C‧‧‧Region
F1、F2、F3、F4‧‧‧影像幀 F1, F2, F3, F4 ‧‧‧ video frame
F41~F44‧‧‧子影像 F41~F44
S11~S17、S121、S124、S21~S29‧‧‧步驟 S11~S17, S121, S124, S21~S29‧‧‧
V1、V41~V44‧‧‧第一向量 V1, V41~V44 ‧‧‧ first vector
V2、V51~V54‧‧‧第二向量 V2, V51~V54 ‧‧‧ second vector
V3、V6‧‧‧校正向量 V3, V6‧‧‧ correction vector
X1、X2、X3、X4、X5、X6‧‧‧中心位置 X1, X2, X3, X4, X5, X6
50‧‧‧生物體 50‧‧‧ organism
100‧‧‧檢測裝置 100‧‧‧Detection device
110‧‧‧影像擷取元件 110‧‧‧Image capture component
120‧‧‧處理單元 120‧‧‧Processing unit
130‧‧‧顯示單元 130‧‧‧Display unit
140‧‧‧輸入界面 140‧‧‧ input interface
圖1是本發明一實施例中檢測裝置的示意圖;圖2是本發明一實施例中影像校正方法的流程示意圖;圖3A至3D是本發明第一實施例中影像校正方法的影像幀示意圖;圖4是本發明一實施例的影像校正方法中決定第一特徵區域的流程示意圖;圖5是本發明一實施例的影像校正方法中決定第一特徵區域的另一流程示意圖;圖6A至6D是本發明第二實施例中影像校正方法的影像幀示意圖;圖7是本發明第二實施例中影像校正方法的流程示意圖。 1 is a schematic diagram of a detection device in an embodiment of the present invention; FIG. 2 is a schematic flowchart of an image correction method in an embodiment of the present invention; FIGS. 3A to 3D are schematic image frames of an image correction method in the first embodiment of the present invention; 4 is a schematic flowchart of determining a first characteristic region in an image correction method according to an embodiment of the invention; FIG. 5 is another schematic flowchart of determining a first characteristic region in an image correction method according to an embodiment of the invention; FIGS. 6A to 6D It is a schematic diagram of an image frame of the image correction method in the second embodiment of the present invention; FIG. 7 is a schematic flowchart of the image correction method in the second embodiment of the present invention.
本發明所提出的檢測裝置及其所使用的影像校正方法適於應用於可以提供影像的檢測裝置,較佳為可以提供生物影像、醫學影像的檢測裝置。舉例而言,檢測裝置例如是超音波裝置等適於即時提供生物影像、醫學影像的檢測裝置,本發明並不限於此。本發明所屬領域中具有通常知識者可以視需求將本發明所提出的檢測裝置及其所使用的影像校正方法應用至其他生物影像裝置或醫學影像裝置,較佳為需要針對檢測裝置所提供的影像中選取特定觀察區域的檢測裝置,以下本說明書將以超音波裝置舉例說明,其並非用以限定本發明。。 The detection device and the image correction method used in the present invention are suitable for a detection device that can provide images, preferably a detection device that can provide biological images and medical images. For example, the detection device is, for example, an ultrasound device and the like suitable for providing biological images and medical images in real time, and the invention is not limited thereto. Those with ordinary knowledge in the field of the present invention can apply the detection device proposed by the invention and the image correction method used to other biological imaging devices or medical imaging devices as needed, preferably for images provided by the detection device In the following, the detection device of a specific observation area is selected. The following description will use an ultrasound device as an example, which is not intended to limit the present invention. .
圖1是本發明一實施例中檢測裝置的裝置示意圖。請參照圖1,在本發明的實施例中,檢測裝置100包括影像擷取元件110、處理單元120、顯示單元130以及輸入界面140,其中處理單元120連接影像擷取單元110、顯示單元130以及輸入界面140。檢測裝置100適於檢測一生物體50,並藉由影像擷取單元110自生物體50取得動態影像供顯示單元130顯示。 FIG. 1 is a schematic diagram of a detection device in an embodiment of the invention. Please refer to FIG. 1. In an embodiment of the present invention, the
具體而言,本實施例的影像擷取單元110可以朝生物體50發出感測訊號,並藉由接收被生物體50反射的感測訊號或是穿透生物體50的感測訊號來產生對應至生物體50的動態影像,亦可以是藉由接收來自外界的訊號來產生對應至生物體50的動態影像。換句話說,本發明並不限於本說明書實施例中影像擷取元件110自生物體50取得動態影像的方式。 Specifically, the
以下將以超音波影像來舉例說明。本實施例的檢測裝置100例如是超音波檢測裝置;影像擷取元件110例如是超音波探頭(Ultrasonic transducer)或超音波感測器(Ultrasonic sensor),用以檢測生物體50並取得一動態影像。動態影像例如是由複數個影像幀所形成,這些影像幀為二 維超音波影像,較佳為超音波在亮度模式(Brightness mode,B-mode)下取得的超音波影像,這些超音波影像各自形成上述的這些影像幀來形成動態影像。 The following will use ultrasound images as examples. The
本實施例的處理單元120可以自影像擷取元件110取得動態影像,使檢測裝置100可以顯示動態影像並對此動態影像輸入指令。具體而言,處理單元120例如是檢測裝置100中的中央處理器(Central Processing Unit,CPU),亦可以是檢測裝置100所連接的電腦裝置中的CPU,本發明不限於此。 The
本實施例的處理單元120連接至顯示單元130,檢測裝置100可以藉由顯示單元130顯示處理單元120自影像擷取元件110取得的動態影像。顯示單元130例如是液晶顯示螢幕(Liquid Crystal Display,LCD),用以顯示來自影像擷取元件110的動態影像,本發明並不限於顯示單元130的種類。 The
輸入界面140連接至處理單元120,用以接收來自例如是醫生、檢驗師、操作者等使用者的指令。本實施例的輸入界面140例如包括鍵盤、搖桿、軌跡球、滑鼠,更可以是設置於顯示單元130的觸控模組。在本實施例中,使用者可以根據顯示單元130所顯示的動態影像來透過輸入界面140輸入指令,例如藉由輸入界面140決定動態影像中一觀察區域的位置。 The
以下將一併參照上述的檢測裝置100以及元件的標號來說明本發明所提出的檢測裝置100以及影像校正方法。應當理解,儘管術語「第一」、「第二」等在本文中可以用於描述各種元件、區域、影像,但是 這些元件、區域、影像不應受這些術語限制。這些術語僅用於將一個元件、區域、影像與令一個元件、區域、影像分開。因此,下面討論的第一元件、區域、影像可以被稱為第二元件、區域、影像而不脫離本文的教導。 Hereinafter, the
圖2是本發明一實施例中影像校正方法的流程示意圖。請參照圖2,本實施例的影像校正方法先在動態影像中的第一影像幀決定觀察區域(步驟S11)。此處所述的第一影像幀可以是動態影像中的這些影像幀的任一影像幀,較佳為使用者在操作影像擷取元件110時,使用者選來用以決定觀察區域的影像幀。 2 is a schematic flowchart of an image correction method according to an embodiment of the invention. Referring to FIG. 2, the image correction method of this embodiment first determines the observation area in the first image frame in the dynamic image (step S11 ). The first image frame described herein may be any of these image frames in the dynamic image, preferably the image frame selected by the user to determine the observation area when the user operates the
為了清楚說明本發明所提出的影像校正方法,以下將以簡化的影像示意圖來說明本發明的檢測裝置及影像校正方法,其並非用以限定本發明。請先參照圖2,在使用者在第一影像幀決定觀察區域(步驟S11)後,本實施例的影像校正方法在第一影像幀中取得觀察區域的中心位置(步驟S12)。 In order to clearly illustrate the image correction method proposed by the present invention, the following will illustrate the detection device and image correction method of the present invention with a simplified image schematic diagram, which is not intended to limit the present invention. Referring to FIG. 2 first, after the user determines the observation area in the first image frame (step S11), the image correction method of this embodiment obtains the center position of the observation area in the first image frame (step S12).
在取得觀察區域的中心後,本實施例的影像校正方法接著在第一影像幀中決定一第一特徵區域以及第一特徵區域的灰階特徵(步驟S13)。所述的灰階特徵有關於圖像中灰階值的相關資訊,相關資訊包括分布方向、相鄰間隔、變化幅度,且處理單元120可以依據灰階特徵在動態影像中的其他影像幀找到第一特徵區域的位置。舉例而言,第一特徵區域可以是灰階強度變化較劇烈之區域,該區域可能是肌肉等組織,可以作為定位的標的。 After obtaining the center of the observation area, the image correction method of this embodiment then determines a first feature area and the grayscale features of the first feature area in the first image frame (step S13). The gray-scale feature has relevant information about the gray-scale value in the image. The relevant information includes the distribution direction, the adjacent interval, and the range of change, and the
在找到第一特徵區域以及灰階特徵(步驟S13)之後,本 實施例的影像校正方法取得第一向量(步驟S14)。第一向量有關於觀察區域的中心位置與第一特徵區域的中心位置之間的相對距離以及相對方向,本實施例以觀察區域的中心位置至第一特徵區域的中心位置的向量為例,但本發明不限於此。 After finding the first feature area and the gray-scale feature (step S13), the image correction method of this embodiment obtains the first vector (step S14). The first vector relates to the relative distance and relative direction between the center position of the observation area and the center position of the first characteristic area. In this embodiment, the vector from the center position of the observation area to the center position of the first characteristic area is used as an example, but The present invention is not limited to this.
在取得第一向量、第一特徵區域以及第一特徵區域的灰階特徵後,本實施例的影像校正方法接著以上述的灰階特徵在第二影像幀決定第二特徵區域(步驟S15)。上述第二影像幀例如是影像擷取元件110在取得第一影像幀後取得的影像幀,處理單元120藉由灰階特徵在第二影像幀中決定第二特徵區域,亦即藉由自第一特徵區域取得的灰階特徵來在第二影像幀中找到對應的區域。 After obtaining the first vector, the first feature area, and the gray-scale features of the first feature area, the image correction method of this embodiment then determines the second feature area in the second image frame using the gray-scale features described above (step S15). The second image frame is, for example, an image frame obtained by the
本實施例的影像校正方法根據第二特徵區域以及觀察區域取得第二向量(步驟S16)。在本實施例中,處理單元120以灰階特徵決定第二特徵區域時,觀察區域在第一影像幀中的位置與觀察區域在第二影像幀中的位置相同,上述第二向量為觀察區域的中心位置與第二特徵區域的中心位置之間的相對距離以及相對方向,本實施例以觀察區域的中心位置至第二特徵區域的中心位置決定為第二向量,但本發明不限於此。 The image correction method of this embodiment obtains the second vector according to the second feature area and the observation area (step S16). In this embodiment, when the
本實施例的影像校正方法在取得第二向量後,比較第一向量以及第二向量。本實施例的影像校正方法取得第二向量和第一向量之間的差值,並以此差值校正觀察區域(步驟S17)。藉由自第二影像幀取得的第二向量和第一影像幀取得的第一向量,本實施例的影像校正方法可以藉由第二向量和第一向量的差異校正觀察區域在下一張影像幀(亦即第三影像幀)的位置。本實施例的影像校正方法根據動態影像中特徵區域在每個 影像幀中的位置變化來校正觀察區域的位置,藉以提供良好的校正效果。舉例而言,當影像擷取元件110是超音波探頭,且觀察區域為使用者用以擷取都卜勒超音波訊號的區域時,藉由上述的影像校正方法檢測裝置100可以提供準確的都卜勒超音波訊號。本發明並不限於利用第二向量和第一向量之間的差異校正觀察區域在第三影像幀的位置,在其他實施例中更可以以第二向量和第一向量的差異即時校正觀察區域在第二影像幀的位置,本發明不限此,端視檢測裝置100的處理效能以及校正的準確需求而定。以下將再以影像幀的示意圖具體說明本發明所提出的影像校正方法以及檢測裝置。 The image correction method of this embodiment compares the first vector and the second vector after obtaining the second vector. The image correction method of this embodiment obtains the difference between the second vector and the first vector, and corrects the observation area with this difference (step S17). By the second vector obtained from the second image frame and the first vector obtained from the first image frame, the image correction method of this embodiment can correct the observation area in the next image frame by the difference between the second vector and the first vector (That is, the third image frame). The image correction method of this embodiment corrects the position of the observation area according to the change of the position of the characteristic area in the dynamic image in each image frame, thereby providing a good correction effect. For example, when the
圖3A至3D為本發明第一實施例中影像幀的示意圖,為了清楚說明,圖式中以陰影線繪示影像幀中的黑色區域或深色區域,其中填色方式並非用以限定本發明。請參照圖3A所繪示的影像幀的示意圖,以下將一併參照上述檢測裝置100的元件標號來清楚說明本發明的影像校正方法以及檢測裝置,其中觀察區域A1經使用者以輸入界面140決定後,處理單元120會取得觀察區域A1的中心位置X1,亦即觀察區域A1的中心X1在第一影像幀F1的位置。以B-mode超音波影像以及都卜勒超音波為例,觀察區域A1例如是使用者選定來以都卜勒超音波檢測的區域,例如是觀察區域C中血液的流速以及流向。 FIGS. 3A to 3D are schematic diagrams of image frames in the first embodiment of the present invention. For the sake of clarity, the black or dark areas in the image frames are hatched in the drawings, and the color filling method is not used to limit the present invention. . Please refer to the schematic diagram of the image frame shown in FIG. 3A. The image calibration method and the detection device of the present invention will be clearly explained with reference to the component labels of the
在使用者藉由輸入界面140在第一影像幀F1選定觀察區域A1後,處理單元120取得觀察區域A1在第一影像幀F1中的位置X1。接著,處理單元120在第一影像幀F1中決定第一特徵區域B1,同時取得第一特徵區域B1的灰階特徵。處理單元120將觀察區域A1的中心位置X1至第一特徵 區域B1的中心位置X2決定為第一向量V1。 After the user selects the observation area A1 in the first image frame F1 through the
請參照圖3B,在取得第一向量V1後,由於檢測者手持檢測裝置時的位移,在第二影像幀F2取得第二特徵區域B2以及第二向量V2。具體而言,處理單元120在取得第一向量V1後經一段時間接著再自影像擷取元件110取得第二影像幀F2,並在第二影像幀F2中以第一特徵區域B1的灰階特徵找到第二特徵區域B2。處理單元120在觀察區域A1的中心位置X1和第二特徵區域B2的中心位置X3之間決定第二向量V2。 Referring to FIG. 3B, after acquiring the first vector V1, the second characteristic region B2 and the second vector V2 are acquired in the second image frame F2 due to the displacement of the detector when the detector holds the detection device. Specifically, the
請參照圖3C,在取得第一向量V1和第二向量V2後,依據第二向量V2和第一向量V2之間的差值決定校正向量V3。處理單元120在取得與觀察區域A1的中心位置X1相關的第一向量V1和第二向量V2後,藉由算出他們的差異來得到校正向量V3,並根據此校正向量V3提供一校正指令。 Referring to FIG. 3C, after obtaining the first vector V1 and the second vector V2, the correction vector V3 is determined according to the difference between the second vector V2 and the first vector V2. After obtaining the first vector V1 and the second vector V2 related to the center position X1 of the observation area A1, the
請參照圖3D,在第三影像幀A2中,觀察區域A2可以根據校正向量V3校正。具體而言,當處理單元120取得上述的校正向量V3時,處理單元120可以根據校正向量V3校正觀察區域A1的位置,並將觀察區域A1校正為觀察區域A2。由於校正向量V3是根據影像幀中的特徵區域所產生,因此藉由本實施例的影像校正方法,觀察區域可以維持在使用者所要觀察的區域上,藉以提供良好的檢測效果。 Referring to FIG. 3D, in the third image frame A2, the observation area A2 can be corrected according to the correction vector V3. Specifically, when the
進一步而言,本發明所提出的影像校正方法還可以藉由輪廓影像來決定第一特徵區域。圖4是本發明的一實施例中影像校正方法決定第一特徵區域的流程示意圖。請參照圖4,在本實施例的影像校正方法中,使用者決定觀察區域(步驟S11)後,取得觀察區域的中心位置(步 驟S12)。在取得觀察區域的中心位置(步驟S12)後,本實施例的影像校正方法自第一影像幀決定一輪廓影像(步驟S121)。具體而言,本實施例的影像校正方法以像素變化最大的區域作為特徵區域,藉由灰階形態學來選出灰階變化較強烈的區域。以數學上的形態學(Morphology),本實施例的影像校正方法找出第一影像幀的邊界圖形,亦即找出第一影像幀中個圖形的輪廓以形成為輪廓圖形。 Further, the image correction method proposed by the present invention can also determine the first feature area by the contour image. FIG. 4 is a schematic flowchart of determining the first characteristic region by the image correction method according to an embodiment of the invention. Referring to FIG. 4, in the image correction method of this embodiment, after the user determines the observation area (step S11), the center position of the observation area is obtained (step S12). After obtaining the center position of the observation area (step S12), the image correction method of this embodiment determines a contour image from the first image frame (step S121). Specifically, in the image correction method of this embodiment, the region with the largest pixel change is used as the characteristic region, and the region with the stronger gray-scale change is selected by the gray-scale morphology. Using mathematical morphology (Morphology), the image correction method of this embodiment finds the boundary graphics of the first image frame, that is, finds the contours of the graphics in the first image frame to form the contour graphics.
在取得第一影像幀的輪廓影像後,本實施例的影像校正方法以像素聯通方式對影像中的像素判斷(步驟S122)。由於本實施例的影像校正方法以灰階形態學取出影像中的輪廓圖形,藉由例如是八聯通的像素聯通方式可以將輪廓圖形區分為多個區塊,再將這些區塊中最大的一個定為第一特徵區域(步驟S13)。具體而言,藉由八聯通判斷輪廓影像(步驟S12)後,輪廓影像會有各自不同的像素聯通標誌,而這些像素中像素聯通標誌相同並被分為同一組的最大區域就會被決定為第一特徵區域。經由八聯通判斷將輪廓影像分區後,這些區域中像素數量最高的區域就是第一特徵區域。本實施例以八聯通為例,在其他實施例中更可以以四聯通或其他數量、形狀的像素聯通方式來判斷影像幀中的像素,本發明不限於此。 After obtaining the outline image of the first image frame, the image correction method of this embodiment determines the pixels in the image by pixel connection (step S122). Since the image correction method of this embodiment uses gray-scale morphology to extract the contour graphics in the image, the contour graphics can be divided into multiple blocks by, for example, eight-connected pixel connection, and then the largest one of these blocks can be distinguished. The first feature region is determined (step S13). Specifically, after judging the contour image by eight connections (step S12), the contour image will have different pixel connection marks, and the largest area of these pixels that are the same and are divided into the same group will be determined as The first characteristic area. After the contour image is partitioned by Eight Connectivity, the area with the highest number of pixels in these areas is the first feature area. In this embodiment, eight connections are used as an example. In other embodiments, four connections or other numbers and shapes of pixel connections can be used to determine the pixels in the image frame. The present invention is not limited thereto.
圖5是本發明一實施例的影像校正方法中決定第一特徵區域的另一流程示意圖。本發明實施例中的影像校正方法可以藉由斷開(open)影像或閉合(Close)影像來決定輪廓影像。請參照圖5,在本實施例的影像校正方法中,在決定觀察區域(步驟S11)以及取得觀察區域的中心位置(步驟S12)後,可以取得第一影像幀的斷開影像(步驟 S123)以及閉合影像(步驟S124)。上述的斷開影像以及閉合影像為灰階形態學中的影像處理方式,上述斷開影像為第一影像幀侵蝕(Erosion)後再膨脹(Dilation)後的影像;閉合影像為第一影像幀膨脹後再侵蝕的影像。藉由取得斷開影像減去閉合影像的絕對值,本實施例的影像校正方法可以取得輪廓影像(步驟S121)。藉著,再藉由八聯通判斷輪廓影像中的像素(步驟S122),以在輪廓影像中決定第一特徵區域(步驟S13)。 FIG. 5 is another schematic flowchart of determining the first characteristic region in the image correction method according to an embodiment of the invention. The image correction method in the embodiment of the present invention can determine the contour image by opening the image or closing the image. Referring to FIG. 5, in the image correction method of this embodiment, after determining the observation area (step S11) and acquiring the center position of the observation area (step S12), the broken image of the first image frame can be obtained (step S123) And the closed image (step S124). The above-mentioned open image and closed image are image processing methods in grayscale morphology. The open image is the image after the first image frame is eroded (Erosion) and then expanded (Dilation); the closed image is the first image frame expansion Eroded images later. By obtaining the absolute value of the closed image minus the closed image, the image correction method of this embodiment can obtain the contour image (step S121). By determining the pixels in the contour image through eight connections (step S122), the first feature area is determined in the contour image (step S13).
另一方面,在本發明所提出的檢測裝置以及其所使用的影像校正方法中,灰階特徵是有關第一特徵區域所對應的部分第一影像幀中灰階分布、變化的特徵資訊。具體而言,本發明所提出的影像校正方法可以藉由灰度共生矩陣(Gray-Level Co-occurrence Matrix,GLCM)取得第一特徵區域所對應的部分第一影像幀的灰階特徵。換句話說,藉由取得第一特徵區域所對應的部分影像幀的灰度共生矩陣,本發明實施例所提出的影像校正方法可以在其他影像幀中根據此灰度共生矩陣找到第一特徵區域所對應的那部分影像幀,並藉由第一特徵區域的位置變化來校正觀察區域,藉以使檢測裝置可以提供良好的檢測功能。 On the other hand, in the detection device and the image correction method used in the present invention, the gray-scale feature is feature information about gray-scale distribution and change in a part of the first image frame corresponding to the first feature region. Specifically, the image correction method proposed by the present invention can obtain the gray-scale features of a portion of the first image frame corresponding to the first feature area by a gray-level co-occurrence matrix (GLCM). In other words, by obtaining the gray level co-occurrence matrix of the part of the image frame corresponding to the first feature area, the image correction method provided by the embodiment of the present invention can find the first feature area according to the gray level co-occurrence matrix in other image frames The corresponding part of the image frame, and the observation area is corrected by the position change of the first characteristic area, so that the detection device can provide a good detection function.
本發明所提出的檢測裝置以及其所用的影像校正方法還可以將影像幀區分為多個子影像來校正觀察區域。圖6A至圖6D為本發明第二實施例的影像校正方法中影像幀的示意圖,其中為了清楚說明本實施例的影像校正方法,影像幀中的黑色區域或深色區域以陰影線繪示,部分圖形中省略繪示填色的部分,藉以清楚標示本發明中影像幀的各區域,其並非用以限定本發明。 The detection device and the image correction method used in the present invention can also divide the image frame into multiple sub-images to correct the observation area. FIGS. 6A to 6D are schematic diagrams of image frames in the image correction method of the second embodiment of the present invention. To clearly illustrate the image correction method of this embodiment, black areas or dark areas in the image frame are hatched, Some of the graphics are omitted from the color filling, so as to clearly mark each area of the image frame in the present invention, which is not intended to limit the present invention.
請參照圖6A,在本發明第二實施例的影像校正方法中,影 像幀F4被區分為多個子影像F41、F42、F43、F44。詳細而言,在使用者在影像幀F4中決定觀察區域D1並取得中心位置X5後,本實施例的影像校正方法將影像幀F4區分為子影像F41、F42、F43、F44。請參照圖6B,本實施例的影像校正方法在子影像F41中取得特徵區域E11;在子影像F42中取得特徵區域E21;在子影像F43中取得特徵區域E31;在子影像F44中取得特徵區域E41。取得這些特徵區域E11、E21、E31、E41後,本實施例的影像校正方法根據觀察區域D1的中心位置X5決定第一向量V41、V42、V43以及V44,同時對這些特徵區域E11、E21、E31、E41中的影像幀取得灰階特徵。 Referring to FIG. 6A, in the image correction method of the second embodiment of the present invention, the image frame F4 is divided into a plurality of sub-images F41, F42, F43, and F44. In detail, after the user determines the observation area D1 in the video frame F4 and obtains the center position X5, the image correction method of this embodiment divides the video frame F4 into sub-images F41, F42, F43, and F44. Referring to FIG. 6B, the image correction method of this embodiment obtains the characteristic region E11 in the sub-image F41; obtains the characteristic region E21 in the sub-image F42; obtains the characteristic region E31 in the sub-image F43; obtains the characteristic region in the sub-image F44 E41. After obtaining these characteristic regions E11, E21, E31, and E41, the image correction method of this embodiment determines the first vectors V41, V42, V43, and V44 according to the center position X5 of the observation region D1, and at the same time for these characteristic regions E11, E21, E31 , The image frame in E41 obtains gray-scale features.
具體而言,在本發明的第二實施例中,上述特徵區域E11、E21、E31以及E41的取得例如是可以藉由上述的灰階形態學的方式經由輪廓影像、八聯通判斷來決定這些區域,再找出這些區域所對應的影像幀中的影像,藉由取得這些部分的影像的灰度共生矩陣來作為上述的灰階特徵。 Specifically, in the second embodiment of the present invention, the acquisition of the characteristic regions E11, E21, E31, and E41 can be determined by, for example, the above-described gray-scale morphology through contour images and eight-connection judgment. Then, find the images in the image frames corresponding to these areas, and obtain the gray level co-occurrence matrix of these partial images as the above-mentioned gray-scale features.
請參照圖6C,在另一張影像幀中,藉由上述的灰階特徵各自在子影像中找到第二特徵區域E12、E22、E32以及E42。根據觀察區域A1的中心位置X5,在此影像幀中可以決定多個第二向量V51、V52、V53以及V54。在本實施例中,在每個子影像中可以藉由第二向量和第一向量的差異來判斷觀察區域D1的校正方式,其中藉由第二向量V51和第一向量V41的差異、第二向量V52和第一向量V42的差異、第二向量V53和第一向量V43的差異以及第二向量V54和第一向量V44的差異可以提供校正向量來進行觀察區域D1的效正。 Referring to FIG. 6C, in another image frame, the second feature regions E12, E22, E32, and E42 are found in the sub-images by the gray-scale features described above. According to the center position X5 of the observation area A1, a plurality of second vectors V51, V52, V53, and V54 can be determined in this video frame. In this embodiment, the correction method of the observation area D1 can be judged by the difference between the second vector and the first vector in each sub-image, where the difference between the second vector V51 and the first vector V41, the second vector The difference between V52 and the first vector V42, the difference between the second vector V53 and the first vector V43, and the difference between the second vector V54 and the first vector V44 can provide a correction vector to correct the observation area D1.
請參照圖6D,藉由上述的第二向量和第一向量的差異所得到的校正向量V6,本實施例的影像校正方法可以將觀察區域D1的中心位置X5校正為中心位置X6,並校正為觀察區域D2,以維持適當的觀察位置來提供良好的檢測效果。 Referring to FIG. 6D, by the correction vector V6 obtained by the difference between the second vector and the first vector described above, the image correction method of this embodiment can correct the center position X5 of the observation area D1 to the center position X6 and correct it to Observe the area D2 to maintain an appropriate observation position to provide a good detection effect.
圖7是本發明第二實施例中影像校正方法的流程示意圖。請參照圖7,在本實施例的影像校正方法在第一影像幀決定觀察區域(步驟S21)後,觀察區域的中心位置會在第一影像幀中決定(步驟S22)。在取得觀察區域的中心位置後將第一影像幀分割為子畫面(步驟S23)。 7 is a schematic flowchart of an image correction method in a second embodiment of the invention. Referring to FIG. 7, after the image correction method of this embodiment determines the observation area in the first image frame (step S21), the center position of the observation area is determined in the first image frame (step S22). After acquiring the center position of the observation area, the first video frame is divided into sub-screens (step S23).
在本實施例中,第一特徵區域可以藉由輪廓影像取得。詳細而言,在分割出子畫面(步驟S23)後,本實施例的影像校正方法取得第一影像幀的輪廓影像(步驟S24),並根據每個子畫面的輪廓影像決定第一特徵區域(步驟S25)。在取得這些第一特徵區域後,根據這些第一特徵區域的位置以及觀察區域的位置決定出複數個第一向量,同時再根據這些第一特徵區域所對應的部分子影像取得灰階特徵(步驟S26),以作為後續校正觀察區域的依據。 In this embodiment, the first characteristic area can be obtained by the contour image. In detail, after the sub-screen is divided (step S23), the image correction method of this embodiment acquires the contour image of the first video frame (step S24), and determines the first feature region according to the contour image of each sub-screen (step S23) S25). After obtaining the first feature regions, a plurality of first vectors are determined according to the positions of the first feature regions and the position of the observation region, and the grayscale features are obtained based on the partial sub-images corresponding to the first feature regions (step S26), as a basis for subsequent correction of the observation area.
在取得下一影像幀作為第二影像幀後,本實施例的影像校正方法根據灰階特徵找出第二影像幀中各子影像中的第二特徵區域(步驟S27),並根據這些第二特徵區域決定複數個第二向量(步驟S28)。在取得這些第一向量和第二向量後,再根據這些第二向量和第一向量之間的差異來校正觀察區域的位置(步驟S29),藉以使觀察區域在動態影像的影像幀中可以維持在適當的位置。 After obtaining the next image frame as the second image frame, the image correction method of this embodiment finds the second feature region in each sub-image in the second image frame according to the grayscale features (step S27), and The feature area determines a plurality of second vectors (step S28). After obtaining the first vector and the second vector, the position of the observation area is corrected according to the difference between the second vector and the first vector (step S29), so that the observation area can be maintained in the video frame of the dynamic image In the right place.
綜上所述,本發明所提出的影像校正方法可以校正觀察區 域在動態影像中的各個影像幀的位置。在使用者在一影像幀決定好觀察區域後,可以根據影像幀中特徵區域的位置以及觀察區域的位置來訂出第一向量,接著再在其他影像幀中尋找特徵區域,並根據特徵區域在其他影像幀中的位置來校正觀察區域在其他影像幀中的位置,藉以提供適當的影像校正方法。藉由特徵區域及其灰階特徵,本發明所提出的檢測裝置可以在每個影像幀中維持觀察區域在適當的位置,藉以提供良好的檢測功能。 In summary, the image correction method proposed by the present invention can correct the position of each image frame in the dynamic image of the observation area. After the user determines the observation area in an image frame, he can order the first vector according to the position of the characteristic area in the image frame and the position of the observation area, and then find the characteristic area in other image frames, and then find the characteristic area according to the characteristic area. The position in the other image frame is used to correct the position of the observation area in the other image frame, so as to provide an appropriate image correction method. With the feature regions and their gray-scale features, the detection device provided by the present invention can maintain the observation region in an appropriate position in each image frame, thereby providing good detection functions.
S11~S17‧‧‧步驟 S11~S17‧‧‧Step
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