BRAIN VENTRICLE ANALYSIS

• US 20110194741A1
• Filed: 09/30/2009
• Published: 08/11/2011
• Est. Priority Date: 10/07/2008
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Wang, Jingnan, Ekin, Ahmet
• Application Number:
  US13/122,976
• Publication Number:
  US 20110194741A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/4088 Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• CPC Class Codes: A61B 6/00 Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment instruments measuring radiation intensity for application in the field of nuclear medicine, e.g. in vivo counting, G01T1/161; apparatus for taking X-ray photographs G03B42/02
• CPC Class Codes: A61B 6/501 for diagnosis of the head, e.g. neuroimaging or craniography
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: G06T 2200/36 Review paper; Tutorial; Survey
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/41 based on statistical description of texture
• CPC Class Codes: G06T 7/62 of area, perimeter, diameter or volume
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A system for analyzing a brain ventricle (8) is described. The system comprises an edge detector (52) for identifying an edge point (17) on an edge of the brain ventricle. Also, a length measurer (53) is provided for establishing a length measure of a path (10) starting from a central point (5) of the brain ventricle and terminating at the edge point (17). The edge detector (52) is arranged for detecting an edge point at an end of a lobe of the brain ventricle, the length measure corresponding to an extent of the lobe.

Claims

1. A system for analyzing a brain ventricle (8) represented in a medical image dataset, comprising

an edge detector (52) for identifying an edge point (17) on an edge of the brain ventricle;

and

a length measurer (53) for establishing a length measure of a path (10) comprising a central point (5) of the brain ventricle and terminating at the edge point (17).

2. The system of claim 1, the path starting at the central point.

3. The system of claim 1, the edge detector (52) being arranged for detecting an edge point at an end of a lobe of the brain ventricle, the length measure corresponding to an extent of the lobe.

4. The system of claim 3, the brain ventricle comprising a lateral ventricle and the lobe comprising a posterior right lobe of the lateral ventricle.

5. The system of claim 1, the edge detector (52) being arranged for selecting two edge points of the brain ventricle which are connected by a line (9) intersecting the center point (5), such that a distance between the two edge points is substantially minimal, the length measurer being arranged for establishing the substantially minimal distance.

6. The system according to claim 2, the edge detector (52) being arranged for identifying a plurality of edge points (7) on the edge of the brain ventricle;

and

the length measurer (53) being arranged for establishing the length from the central point (5) to each of the plurality of edge points (7), to obtain a plurality of lengths.

7. The system according to claim 6, further comprising a statistics module (62) for computing a statistical quantity of the plurality of lengths.

8. The system of claim 2, further comprising

a central point detector (51) for identifying a first central point (5) of a first part (8) of the brain ventricle;

means for identifying a region (14) of the image, a boundary of the region (14) being based on the first central point (5); and

means for identifying a second part (15) of the brain ventricle comprising an intersection of the first part (8) of the brain ventricle with the region (14);

the center point detector (51) further being arranged for identifying a second central point (16) of the second part (15) of the brain ventricle.

9. The system of claim 8, the length measurer (53) being arranged for establishing the length measure as a length of a path (18) from the first central point (5) to the edge point (17) via the second central point (16).

10. The system of claim 1, the medical image comprising a 2D cross section of a 3D medical image dataset.

11. The system of claim 1, further comprising a decision support system (55) for classifying the medical image, based on the length measure.

12. A medical workstation comprising the system according to claim 1 and an output (58) for generating a human-readable representation of the length measure and a graphical indication of the path in the medical image.

13. A medical imaging apparatus for acquiring a medical image, the medical imaging apparatus comprising the system according to claim 1 and a scanner (57) for generating the medical image.

14. A method of analyzing a brain ventricle represented in a medical image dataset, comprising

identifying (81) an edge point on an edge of the brain ventricle;

and

establishing (82) a length measure of a path through the ventricle, the path terminating at the edge point and comprising a central point of the brain ventricle.

15. A computer program product comprising computer instructions for causing a processor system to perform the method according to claim 14.