Method of segmenting a radiographic image into diagnostically relevant and diagnostically irrelevant regions

• US 20050018893A1
• Filed: 07/24/2003
• Published: 01/27/2005
• Est. Priority Date: 07/24/2003
• Status: Active Grant

• Current Assignee: CareStream Health Incorporated
• Original Assignee: Eastman Kodak Company
• Inventors: Luo, Hui, Wang, Xiaohui
• Granted Patent: US 7,391,895 B2
• Time in Patent Office: Days
• US Class Current: 382/132
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/20061 Hough transform
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06V 10/267 by performing operations on regions, e.g. growing, shrinking or watersheds
• CPC Class Codes: G06V 10/28 Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
• CPC Class Codes: G06V 2201/03 Recognition of patterns in medical or anatomical images

Abstract

A method of segmenting a radiographic image into diagnostically relevant and diagnostically irrelevant regions comprising: acquiring a digital radiographic image including a matrix of rows and columns of pixels; detecting the initial background left point of a histogram of the image; detecting the foreground of the image; regenerating the background of the image by region growing; validating the background of the image; merging the background and foreground regions of the image as diagnostically irrelevant regions; and extracting the anatomy region of the image as the diagnostically relevant region.

Claims

1. A method of segmenting a radiographic image into diagnostically relevant and diagnostically irrelevant regions comprising:

acquiring a digital radiographic image including a matrix of rows and columns of pixels;

detecting the initial background left point of a histogram of said image;

detecting the foreground of said image;

regenerating the background of said image by region growing;

validating the background of said image;

merging the background and foreground regions of said image as diagnostically irrelevant regions; and

extracting the anatomy region of said image as the diagnostically relevant region.

2. The method of claim 1 wherein said acquiring a digital radiographic image includes acquiring a digital radiographic image from one of the following:

a diagnostic imaging device, a direct digital radiography or computer radiography device, and x-ray film digitizer, a digital radiographic image archive.

3. The method of claim 1 wherein said detecting the initial background left point includes:

evaluating each row and column of image pixels for all rising and falling transitions for all candidate backgrounds to foreground (BF) and background to skinline (BS) transitions and record the transition range, transition width, the maximum transition slope and the transition high point;

building transition range cumulative histogram and auto-selecting new transition range threshold;

building a transition high point histogram and find a first estimate of a background left point;

iteratively fine tuning the background left point; and

verifying the background left point.

4. The method of claim 1 wherein said detecting the foreground includes:

providing as inputs said acquired digital radiographic image and said initial background left point;

using a smart edge detection process to classify all significant transitions in said image;

conducting a Hough Transform to delineate all the lines that are possible collimation blades;

finding candidate partition blade pairs if said image has several radiation fields;

lining a divide-and-conquer process to partition said image into sub-images containing only one radiation field; and

identifying the best collimation for each sub-image to detect the foreground.

5. The method of claim 1 wherein said regenerating the background uses the region growing method from a set of known “

seed”

background pixels.

6. The method of claim 1 wherein said validating said background includes determining whether the ratio between the background region exceeds a certain threshold and whether the dynamic range of all the non-foreground region is greater than a minimum threshold and if one or both are not then the background detected is invalid.

7. The method of claim 1 wherein said merging the background and foreground regions of said image includes removing any transition gaps between said regions.

8. The method of claim 1 wherein said extracting the anatomy region is carried out by subtracting the merged foreground and background regions from said acquired image.

9. The method of claim 8 wherein extracting the anatomy region is followed by a refining process using image labeling to identify a plurality of largest connected regions as the anatomy regions.

10. The method of claim 3 wherein iteratively fine tuning the background left point uses either major peak or major background or both as features for selecting different thresholds in order to control the iteration.