Method for displaying medical images

• US 10,165,987 B2
• Filed: 07/31/2015
• Issued: 01/01/2019
• Est. Priority Date: 08/01/2014
• Status: Active Grant

• Current Assignee: Centre Hospitalier Universitaire d'Angers, Universite D'Angers
• Original Assignee: Centre Hospitalier Universitaire d'Angers, Universite D'Angers
• Inventors: Cales, Paul
• Primary Examiner(s): Cunningham, Gregory F
• Application Number:
  US15/500,601
• Publication Number:
  US 20170215814A1
• Time in Patent Office: 1,250 Days
• Field of Search: 382128
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0095 by applying light and detecting acoustic waves, i.e. photoacoustic measurements
• CPC Class Codes: A61B 5/015 By temperature mapping of body part
• CPC Class Codes: A61B 5/0515 Magnetic particle imaging
• CPC Class Codes: A61B 5/0522 Magnetic induction tomography
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/245 specially adapted for magnetoencephalographic [MEG] signals
• CPC Class Codes: A61B 5/4244 liver
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/463 characterised by displaying multiple images or images and diagnostic data on one display
• CPC Class Codes: A61B 8/08 Detecting organic movements or changes, e.g. tumours, cysts, swellings A61B8/02 - A61B8/06 take precedence
• CPC Class Codes: G06T 11/001 Texturing; Colouring; Generation of texture or colour inpainting G06T5/77
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10104 Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10108 : Single photon emission computed tomography [SPECT]
• CPC Class Codes: G06T 2207/10116 : X-ray image
• CPC Class Codes: G06T 2207/10132 : Ultrasound image
• CPC Class Codes: G06T 2207/30056 : Liver; Hepatic
• CPC Class Codes: G06T 2207/30096 : Tumor; Lesion
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/73 : using feature-based methods

Abstract

A method for displaying at least two medical images, each medical image highlighting at least one feature, including steps of obtaining a 2D medical image, identifying at least two features on the obtained medical image, generating masks showing the identified features, each mask highlighting at least one of the identified features, generating a set of at least two medical images, each medical image highlighting at least one of the identified features by superimposing at least one of the masks on the obtained medical image, and displaying the set of at least two generated medical images, one after the other. The invention also relates to a microprocessor comprising a computer algorithm to perform the method of the invention and to a system comprising said microprocessor and a visualizing means.

Claims

1. A method for displaying at least two medical images, each medical image highlighting at least one feature, said method comprising the steps of:

a) obtaining at least one 2D medical image of whole or part of the liver of a subject;

b) identifying on the medical image of step a) at least two features;

c) generating masks showing the features of step b), each mask highlighting at least one feature identified in step b), wherein said masks are selected from total fibrosis mask, porto-septal fibrosis mask, perisinusoidal fibrosis mask, portal fibrosis mask, stellar fibrosis mask, bridging fibrosis mask, simple septa mask, biopsy edge mask, biopsy specimen surface mask, Arantius furrow perimeter mask, Arantius furrow surface mask, liver perimeter mask, liver surface mask, spleen perimeter mask, spleen surface mask, liver segment I perimeter mask, liver segment IV perimeter mask, liver segment IV surface mask, liver and spleen surfaces mask and liver and spleen perimeters mask;

d) generating a set of at least two medical images, each medical image highlighting at least one feature identified in step b), by superimposing at least one mask of step c) on the medical image of step a); and

displaying the set of at least two medical images of step d), one after the other;

wherein an automated algorithm is used in step b) for identifying the features of step b) and for generating the masks of step c).

2. The method according to claim 1, wherein the medical image of step a) is an image recovered by radiology, anatomy, pathology, histo-pathology, anatomo-pathology, cytology, nuclear medicine, endoscopy or biology.

3. The method according to claim 1, wherein the features are:

a lesion, selected from the group comprising whole fibrosis, bridging fibrosis, septa, porto-septal fibrosis, perisinusoidal fibrosis, portal fibrosis or stellar fibrosis, or

a morphometric data, selected from the group comprising perimeter of an organ or fragment thereof and surface of an organ or fragment thereof.

4. The method according to claim 1, wherein the features are perisinusoidal fibrosis and porto-septal fibrosis.

5. The method according to claim 1, wherein the features are perisinusoidal fibrosis, portal fibrosis and stellar fibrosis.

6. The method according to claim 1, further comprising:

identifying a first feature on the medical image, generating a first mask corresponding to this feature and coloring the first mask in a first color;

identifying a second feature on the medical image, generating a second mask corresponding to this feature and coloring the second mask in a second color which is different from the first color; and

superimposing the masks onto the medical image,

thereby producing a medical image highlighting the said features, wherein each feature appears in a different color.

7. The method according to claim 1, further comprising:

identifying a first feature on the medical image, generating a first mask corresponding to this feature and coloring the first mask in a first color;

identifying a second feature on the medical image, generating a second mask corresponding to this feature and coloring the second mask in a second color which is different from the first color;

repeating the previous step, wherein every additional mask is colored in a different color; and

superimposing the masks onto the medical image,

thereby producing a medical image highlighting the said features, wherein each feature appears in a different color.

8. The method according to claim 1, further comprising:

identifying a first feature on the medical image, generating a first mask corresponding to this feature, and superimposing the first mask onto the medical image, thereby producing a first medical image highlighting the first feature; and

repeating the previous step, thereby producing additional medical images each highlighting one additional feature,

thereby producing a set of medical images wherein each image highlights one feature.

9. The method according to claim 1, wherein said method further comprises measuring at least one descriptor on the medical image.

10. The method according to claim 1, wherein said method further comprises measuring at least one descriptor on the medical image, said descriptor being selected from fractal dimension of porto-septal fibrosis, fractal dimension of perisinusoidal fibrosis, ratio of perisinusoidal fibrosis area, whole area of stellar fibrosis, portal area of stellar fibrosis, mean portal distance, number of bridges, portal ratio of the bridges, mean bridge thickness, mean granularity percentage, mean nodularity percentage, fragmentation index, edge linearity percentage, density heterogeneity, Arantius furrow width, mean liver perimeter, mean spleen perimeter, ratio of spleen to liver surface, frontal or sagittal length of liver segment I, and whole spleen perimeter.

11. The method according to claim 1, wherein the color of the mask highlighting a feature reflects the abnormality level of a descriptor associated with said feature.

12. The method according to claim 1, wherein the feature is a lesion and wherein the color of the mask highlighting said lesion reflects the severity of the lesion.

13. The method according to claim 1, wherein the set of at least two images further comprises a legend or additional information.

14. The method according to claim 1, wherein the method is computerized.

15. A microprocessor, comprising a memory having program code stored thereon that embodies a computer algorithm that, upon execution by the microprocessor, causes the microprocessor to perform the method according to claim 1.

16. A system, comprising:

a microprocessor according to claim 15; and

a visualizing means to present the set of at least two medical images.