Apparatus and method for forming 3D ultrasound image

• US 20050240104A1
• Filed: 01/04/2005
• Published: 10/27/2005
• Est. Priority Date: 04/01/2004
• Status: Active Grant

• Current Assignee: Medison Co., Ltd. (Samsung Electronics Co. Ltd.)
• Original Assignee: Medison Co., Ltd. (Samsung Electronics Co. Ltd.)
• Inventors: Kim, Nam Chul, Kim, Sang Hyun, Shim, Jaeyoon, Kwak, Byoung Joo
• Granted Patent: US 7,507,204 B2
• Time in Patent Office: Days
• US Class Current: 600/437
• CPC Class Codes: A61B 8/00 Diagnosis using ultrasonic, sonic or infrasonic waves imaging of objects using sonar G01S15/00
• CPC Class Codes: A61B 8/461 Displaying means of special interest
• CPC Class Codes: A61B 8/467 characterised by special input means
• CPC Class Codes: A61B 8/469 for selection of a region of interest
• CPC Class Codes: A61B 8/483 involving the acquisition of a 3D volume of data
• CPC Class Codes: Y10S 128/916 Ultrasound 3-D imaging

Abstract

The present invention relates to a 3D ultrasound diagnostic forming a 3D ultrasound image only with volume data exiting within contour by automatically detecting the contour of a target object, comprising: a first unit for generating a region of interest (ROI) box on a 2D ultrasound image; a second unit for detecting a contour of a target object in the ROI box; and a third unit for forming a 3D ultrasound image by rendering volume data existing in the detected contour.

Claims

1. An apparatus for forming a 3-dimensioanl (3D) ultrasound image, comprising:

a first means for generating a region of interest (ROI) box on a 2D ultrasound image;

a second means for detecting a contour of a target object in the ROI box; and

a third means for forming a 3D ultrasound image by rendering volume data existing in the detected contour.

2. The apparatus as recited in claim 1, further comprising a first determination means for determining whether a size of the ROI box is suitable for a size of the 2D ultrasound image of the target object.

3. The apparatus as recited in claim 2, further comprising a second determination means for determining whether the contour of the 2D ultrasound image of the target object is detectable.

4. The apparatus as recited in claim 1, wherein the first means comprises:

a first unit for segmenting the 2D ultra sound image into an image of the target object and an image of a background;

a second unit for setting the ROI box with a predetermined size according to types of the target object on the 2D ultra sound image; and

a third unit for adjusting a size of the ROI box to be suitable for a size of the target object.

5. The apparatus as recited in claim 4, wherein the third unit performs the following functions:

after performing binarization for the target object image in the ROI box, removing a noise region in the binarization region of the target object image;

generating a binarization image for the target object by providing predetermined pixel values to the binarization region and a surface curved line for the binarization image; and

adjusting bounds of the ROI box by moving each bound to pixels closest to the surface curved line.

6. The apparatus as recited in claim 2, wherein the first determination means determines whether a size of the ROI box is suitable by calculating square mean error values of predetermined standard histograms for each type of 2D target object images of target object and a histogram for the target object image in the ROI box.

7. The apparatus as recited in claim 3, wherein the second determination means performs the following functions:

extracting pixels forming boundary of target object by matching pixels of a mask of a predetermined type with pixels of the target object image in the ROI box one to one;

determining a capability of contour detection of the target object by applying a ratio of the number of the boundary pixels to all the number of pixels matched with the mask one to one and variance of pixels forming the boundary according to following equation;

D_i=α

R_1i+β

R_2i

wherein, i is the number of the 2D ultrasound image for the same type of target objects previously stored in a memory, D_i is a determination numerical value representing the capability of the contour detection, R_1i is a ratio of the number of the boundary pixels to all the number of pixels matched with the mask one to one, and R_2i is a variance of pixels forming the boundary, and wherein α and

β

, which are coefficients obtained through a contour detection experiment of various 2D ultrasound images for the same type of target objects, are values when a square mean error value defined in following equation is minimized;

$\in <br><br>=\underset{i}{\sum <br><br>}<br><br>{\left(D_i-\left(\mathrm{\alpha <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>R_{1<br><br>i}+\mathrm{\beta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>R_{2<br><br>i}\right)\right)}^2$

wherein, to minimize the square mean error value, a partial differentiation of the square mean error value is performed for each α and

β

, and wherein the α and

β

are determined as the coefficients when resulting values of the partial differentiation are “

0”

.

8. The apparatus as recited in claim 1, wherein the second means performs the following functions:

performing brightness contrast stretching for the target object image for sufficient brightness contrast of the target object in the ROI box;

after performing binarization of the target object in the ROI box, removing a noise region from a binarization region of the target object;

extracting pixels existing in a surface of the binarization region which meets the bounds of the ROI box by adjusting the bounds of the ROI box; and

detecting the contour of the target object by connecting the extracted pixels.

9. A method for forming a 3-dimensional (3D) ultrasound image, comprising the steps of:

a) generating a region of interest (ROI) box on a 2D ultrasound image;

b) detecting a contour of a target object in the ROI box; and

c) forming a 3D ultrasound image by rendering volume data existing in the detected contour.

10. The method as recited in claim 9, further comprising the step of determining whether a size of the ROI box is suitable for a size of the 2D ultrasound image of the target object.

11. The method as recited in claim 10, further comprising the step of determining whether the contour of the 2D ultrasound image of the target object is detectable.

12. The method as recited in claim 9, wherein the step a) comprises the steps of:

a1) segmenting the 2D ultrasound image into an image of the target object and an image of a background neighboring the target object;

a2) setting a ROI box with a predetermined size on the 2D ultrasound image; and

a3) adjusting the size of the ROI box to be suitable for a size of the 2D ultrasound image of the target object.

13. The method as recited in claim 12, wherein the step a3) comprises the steps of:

a3-1) after performing binarization for the target object image in the ROI box, removing a noise region in the binarization region of the target object image;

a3-2) generating a binarization image for the target object by providing predetermined pixel values to the binarization region and a surface curved line for the binarization image; and

a3-3) adjusting bounds of the ROI box by moving each bound to pixels closest to the surface curved line.

14. The method as recited in claim 10, wherein the step d) comprises the step of determining a size of the ROI box by calculating square mean error values of predetermined standard histograms for each type of 2D target object images of target object and a histogram for the target object image in the ROI box.

15. The method as recited in claim 11, wherein the step e) comprises the steps of:

e1) extracting pixels forming boundary of target object by matching pixels of a mask of a predetermined type with pixels of the target object image in the ROI box one to one; and

e2) determining a capability of contour detection of the target object by applying a ratio of the number of the boundary pixels to all the number of pixels matched with the mask one to one and a variance of pixels forming the boundary according to following equation;

D_i=R_1i+R_2i

wherein, i is the number of the 2D ultrasound image for the same type of target objects previously stored in a memory, D_i is a determination numerical value representing the capability of the contour detection, R_1i is a ratio of the number of the boundary pixels to all the number of pixels matched with the mask one to one, and R_2i is a variance of pixels forming the boundary, and wherein α and

β

, which are coefficients obtained through a contour detection experiment of various 2D ultrasound images for the same type of target objects, are values when a square mean error value defined in following equation is minimized;

$\in <br><br>=\underset{i}{\sum <br><br>}<br><br>{\left(D_i-\left(\mathrm{\alpha <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>R_{1<br><br>i}+\mathrm{\beta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>R_{2<br><br>i}\right)\right)}^2$

wherein, to minimize the square mean error value, a partial differentiation of the square mean error value is performed for each α and

β

, and wherien the α and

β

are determined as the coefficients when resulting values of the partial differentiation are “

0”

.

16. The method as recited in claim 9, wherein the step b) comprises the steps of:

b1) performing brightness contrast stretching for the target object image for sufficient brightness contrast of the target object in the ROI box;

b2) after performing binarization of the target object in the ROI box, removing a noise region from a binarization region of the target object;

b3) extracting pixels existing in a surface of the binarization region which meets the bounds of the ROI box by adjusting the bounds of the ROI box; and

b4) detecting the contour of the target object by connecting the extracted pixels.