FILM TO DICOM CONVERSION

• US 20180075191A1
• Filed: 10/16/2017
• Published: 03/15/2018
• Est. Priority Date: 03/06/2014
• Status: Active Grant

• Current Assignee: Ricoh Company Limited
• Original Assignee: Ricoh Company Limited
• Inventors: Sankhe, Pritesh, Gupta, Mithun Das
• Granted Patent: US 10,354,754 B2
• Time in Patent Office: Days
• 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:

detecting lines within an image of a scanned radiology film, the image including a plurality of sub-images in a grid pattern;

identifying gridlines between sub-images based on a clustering of the detected lines;

separating sub-images based on the gridlines; and

storing the separated sub-images.

2. The method of claim 1, wherein identifying the gridlines between sub-images based on the clustering of the lines comprises:

clustering the detected 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, wherein identifying the gridlines between sub-images based on the clustering of the lines comprises:

determining a common repeating distance between gridlines identified by the clustering; and

identifying missing gridlines by extrapolating according to the common repeating distance from the gridlines identified by the clustering.

4. The method of claim 1, wherein identifying the gridlines between the sub-images comprises:

discarding false positives from the detected lines based on a statistical analysis of lengths of the detected lines; and

identifying gridlines between sub-images based on a clustering of a subset of the detected lines remaining after discarding the false positives.

5. The method of claim 4, wherein discarding the false positives from the detected lines comprises:

finding a Simple Moving Average of the lengths of the detected lines sorted by distance from an origin;

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

discarding a line as a false positive responsive to the Simple Moving Average corresponding to the line being less than a cutoff number of standard deviations from the mean.

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

detecting horizontal and vertical edges of the text regions in horizontal and vertical directions;

connecting the detected horizontal and vertical edges into clusters by performing morphological dilation along the horizontal direction and along the vertical direction; and

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

7. The method of claim 1, further comprising:

detecting text regions within the sub-images;

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.

8. The method of claim 7, 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.

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

detecting lines within an image of a scanned radiology film, the image including a plurality of sub-images in a grid pattern;

identifying gridlines between sub-images based on a clustering of the detected lines;

separating sub-images based on the gridlines; and

storing the separated sub-images.

10. The computer-readable medium of claim 9, wherein identifying the gridlines between sub-images based on the clustering of the lines comprises:

clustering the detected lines within a short distance of each other; and

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

11. The computer-readable medium of claim 9, wherein identifying the gridlines between sub-images based on the clustering of the lines comprises:

determining a common repeating distance between gridlines identified by the clustering; and

identifying missing gridlines by extrapolating according to the common repeating distance from the gridlines identified by the clustering.

12. The computer-readable medium of claim 9, wherein identifying the gridlines between the sub-images comprises:

discarding false positives from the detected lines based on a statistical analysis of lengths of the detected lines; and

identifying gridlines between sub-images based on a clustering of a subset of the detected lines remaining after discarding the false positives.

13. The computer-readable medium of claim 12, wherein discarding the false positives from the detected lines comprises:

finding a Simple Moving Average of the lengths of the detected lines sorted by distance from an origin;

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

discarding a line as a false positive responsive to the Simple Moving Average corresponding to the line being less than a cutoff number of standard deviations from the mean.

14. The computer-readable medium of claim 9, further comprising detecting text regions within the sub-images by:

detecting horizontal and vertical edges of the text regions in horizontal and vertical directions;

connecting the detected horizontal and vertical edges into clusters by performing morphological dilation along the horizontal direction and along the vertical direction; and

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

15. The computer-readable medium of claim 9, wherein the computer-readable medium further comprises computer program instructions for:

detecting text regions within the sub-images;

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.

16. The computer-readable medium of claim 15, wherein the computer-readable medium further comprises computer program instructions for:

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.

17. A system comprising:

a processor; and

a non-transitory, computer-readable medium comprising executable computer program instructions, the instructions executable by the processor to perform steps comprising;

detecting lines within an image of a scanned radiology film, the image including a plurality of sub-images in a grid pattern;

identifying gridlines between sub-images based on a clustering of the detected lines;

separating sub-images based on the gridlines; and

storing the separated sub-images.

18. The system of claim 17, wherein identifying the gridlines between sub-images based on the clustering of the lines comprises:

clustering the detected lines within a short distance of each other; and

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

19. The system of claim 17, wherein identifying the gridlines between sub-images based on the clustering of the lines comprises:

determining a common repeating distance between gridlines identified by the clustering; and

identifying missing gridlines by extrapolating according to the common repeating distance from the gridlines identified by the clustering.

20. The system of claim 17, wherein the computer-readable medium further comprises computer program instructions for:

detecting text regions within the sub-images;

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

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

outputting the DICOM-compliant file.