FILM TO DICOM CONVERSION

• US 20150254401A1
• Filed: 03/06/2014
• Published: 09/10/2015
• Est. Priority Date: 03/06/2014
• Status: Active Grant

• Current Assignee: Ricoh Company Limited
• Original Assignee: Ricoh Company Limited
• Inventors: Gupta, Mithun Das, Sankhe, Pritesh
• Granted Patent: US 9,965,588 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: H04N 1/00127 Connection or combination of a still picture apparatus with another apparatus, e.g. for storage, processing or transmission of still picture signals or of information associated with a still picture
• CPC Class Codes: H04N 2201/3247 Data linking a set of images to one another, e.g. sequence, burst or continuous capture mode

Abstract

Embodiments digitize radiology films into DICOM format. Radiology films typically include an array of captured images laid out in a grid pattern. To comply with DICOM format, the scanned image of a radiology film is segmented into sub-images and text is extracted from the sub-images to generate DICOM metadata. The sub-images and extracted text metadata are then combined to generate a DICOM-compliant multi-image file.

Claims

1. A computer-implemented method comprising:

performing a Hough Transform to detect straight lines within an image of a scanned radiology film, the image including a plurality of sub-images in a grid pattern;

statistically analyzing results of the Hough Transform based on length of detected straight lines to identify gridlines between sub-images;

separating sub-images based on gridlines; and

storing the separated sub-images.

2. The method of claim 1, wherein statistically analyzing results of the Hough Transform comprises:

sorting detected straight lines by distance from an origin;

determining lengths of detected straight lines;

finding a Simple Moving Average of lengths of detected straight lines;

determining a mean and standard deviation of a distribution of the Simple Moving Average;

selecting a line for clustering responsive to the Simple Moving Average at a point being more than three standard deviations away from the mean;

clustering lines within a short distance of each other; and

identifying a mean position of each cluster as a position of a gridline.

3. The method of claim 1, further comprising detecting text regions within the sub-images.

4. The method of claim 3, wherein detecting text regions comprises:

applying Sobel operators along an X direction and along a Y direction;

performing morphological dilation along the X direction and along the Y direction;

performing a binary AND operation to identify text regions that have horizontal and vertical edges within the same region of a respective sub-image; and

performing connected component analysis to identify boundaries of the regions of a respective sub-image where text is present.

5. The method of claim 3, further comprising:

applying Minimum Cross Entropy thresholding to detected text regions; and

performing hole filling on the detected text regions.

6. The method of claim 3, further comprising:

performing optical character recognition to recognize text from a respective text region of a respective sub-image; and

storing the text associated with the respective sub-image.

7. The method of claim 6, further comprising:

accessing the separated sub-images and text associated with the respect sub-images;

formatting a DICOM-compliant multi-image file based on the sub-images and associated text; and

outputting the DICOM-compliant file.

8. A non-transitory computer-readable storage medium storing executable computer program instructions, the instructions executable to perform steps comprising:

performing a Hough Transform to detect straight lines within an image of a scanned radiology film, the image including a plurality of sub-images in a grid pattern;

statistically analyzing results of the Hough Transform based on length of detected straight lines to identify gridlines between sub-images;

separating sub-images based on gridlines; and

storing the separated sub-images.

9. The medium of claim 8, wherein statistically analyzing results of the Hough Transform comprises:

sorting detected straight lines by distance from an origin;

determining lengths of detected straight lines;

finding a Simple Moving Average of lengths of detected straight lines;

determining a mean and standard deviation of a distribution of the Simple Moving Average;

selecting a line for clustering responsive to the Simple Moving Average at a point being more than three standard deviations away from the mean;

clustering lines within a short distance of each other; and

identifying a mean position of each cluster as a position of a gridline.

10. The medium of claim 8, the steps further comprising detecting text regions within the sub-images.

11. The medium of claim 10, wherein detecting text regions comprises:

applying Sobel operators along an X direction and along a Y direction;

performing morphological dilation along the X direction and along the Y direction;

performing a binary AND operation to identify text regions that have horizontal and vertical edges within the same region of a respective sub-image; and

performing connected component analysis to identify boundaries of the regions of a respective sub-image where text is present.

12. The medium of claim 10, the steps further comprising:

applying Minimum Cross Entropy thresholding to detected text regions; and

performing hole filling on the detected text regions.

13. The medium of claim 10, the steps further comprising:

performing optical character recognition to recognize text from a respective text region of a respective sub-image; and

storing the text associated with the respective sub-image.

14. The medium of claim 13, the steps further comprising:

accessing the separated sub-images and text associated with the respect sub-images;

formatting a DICOM-compliant multi-image file based on the sub-images and associated text; and

outputting the DICOM-compliant file.

15. A system comprising:

a processor configured to execute modules; and

a memory storing the modules, the modules comprising;

an image segmentation module comprising;

a straight-line detector configured to perform a Hough Transform to detect straight lines within an image of a scanned radiology film, the image including a plurality of sub-images in a grid pattern, and configured to statistically analyze results of the Hough Transform based on length of detected straight lines to identify gridlines between sub-images;

a sub-image separator configured to separate sub-images based on gridlines; and

a sub-image storage configured to store the separated sub-images.

16. The system of claim 15, wherein the modules further comprise a metadata extraction module comprising a text region detection module configured to detect text regions within the sub-images.

17. The system of claim 16, wherein the metadata extraction module further comprises:

a text region processing module; and

a text recognition module configured to recognize text from text regions of respective sub-images.

18. The system of claim 17, wherein the modules further comprise a DICOM file generation module configured to format a DICOM-compliant multi-image file based on the sub-images and the recognized text.