CA2531871A1 - System and method for detecting a protrusion in a medical image - Google Patents
System and method for detecting a protrusion in a medical image Download PDFInfo
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- CA2531871A1 CA2531871A1 CA002531871A CA2531871A CA2531871A1 CA 2531871 A1 CA2531871 A1 CA 2531871A1 CA 002531871 A CA002531871 A CA 002531871A CA 2531871 A CA2531871 A CA 2531871A CA 2531871 A1 CA2531871 A1 CA 2531871A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
- H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
- H04N13/106—Processing image signals
- H04N13/128—Adjusting depth or disparity
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computerised tomographs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30028—Colon; Small intestine
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S378/00—X-ray or gamma ray systems or devices
- Y10S378/901—Computer tomography program or processor
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- Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Signal Processing (AREA)
- Multimedia (AREA)
- Radiology & Medical Imaging (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
Abstract
A system and method for detecting a protrusion in a medical image are provided. The method comprises: acquiring a medical image (205), wherein the medical image is of an anatomical part; segmenting the medical image (210);
calculating a distance map of the medical image (215); calculating a gradient of the distance mapped medical image (220); and processing the gradient to detect a protrusion in the medical image (225). The gradient is processed by:
projecting a plurality of rays from a location in the distance mapped medical image; calculating a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped medical image; summing and scaling the value of each of the plurality of rays;
and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the protrusion. (FIG. 2)
calculating a distance map of the medical image (215); calculating a gradient of the distance mapped medical image (220); and processing the gradient to detect a protrusion in the medical image (225). The gradient is processed by:
projecting a plurality of rays from a location in the distance mapped medical image; calculating a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped medical image; summing and scaling the value of each of the plurality of rays;
and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the protrusion. (FIG. 2)
Claims (31)
1. A method for detecting a protrusion in a medical image, comprising:
segmenting a medical image;
calculating a distance map of the medical image;
calculating a gradient of the distance mapped medical image; and processing the gradient to detect a protrusion in the medical image.
segmenting a medical image;
calculating a distance map of the medical image;
calculating a gradient of the distance mapped medical image; and processing the gradient to detect a protrusion in the medical image.
2. The method of claim 1, further comprising:
acquiring the medical image.
acquiring the medical image.
3. The method of claim 2, wherein the medical image is acquired by one of a computed tomographic (CT), helical CT, x-ray, positron emission tomographic, fluoroscopic, ultrasound, and magnetic resonance (MR) imaging technique.
4. The method of claim 2, wherein the medical image is of an anatomical part.
5. The method of claim 1, wherein the processing step further comprises:
projecting a plurality of rays from a location in the distance mapped medical image;
calculating a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped medical image;
summing and scaling the value of each of the plurality of rays; and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the protrusion.
projecting a plurality of rays from a location in the distance mapped medical image;
calculating a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped medical image;
summing and scaling the value of each of the plurality of rays; and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the protrusion.
6. The method of claim 1, wherein the processing step further comprises:
projecting a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
calculating an absolute value of a difference between a length of each of the plurality of rays and a distance value at an end of each of the plurality of rays, wherein the length of each of the plurality of rays is a fraction of the original distance value from the location;
dividing a sum of the absolute value by the total number of the plurality of rays;
and detecting one of a sphere-like and polyp-like shape using the division result, wherein one of the sphere-like and polyp-like shapes is the protrusion.
projecting a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
calculating an absolute value of a difference between a length of each of the plurality of rays and a distance value at an end of each of the plurality of rays, wherein the length of each of the plurality of rays is a fraction of the original distance value from the location;
dividing a sum of the absolute value by the total number of the plurality of rays;
and detecting one of a sphere-like and polyp-like shape using the division result, wherein one of the sphere-like and polyp-like shapes is the protrusion.
7. The method of claim 1, wherein the processing step further comprises:
projecting a plurality of rays from a location comprising an original distance value in the distance mapped image;
determining a distance value for each of the plurality of rays that is a fraction of the distance from the location;
calculating a sphere-based response, wherein the sphere-based response is calculated by:
where d is the original distance value, l i is the length of a ray i, T is a total number of the plurality of rays, and S is a set of the plurality of rays such that l i < d; and detecting the protrusion using the sphere-based response.
projecting a plurality of rays from a location comprising an original distance value in the distance mapped image;
determining a distance value for each of the plurality of rays that is a fraction of the distance from the location;
calculating a sphere-based response, wherein the sphere-based response is calculated by:
where d is the original distance value, l i is the length of a ray i, T is a total number of the plurality of rays, and S is a set of the plurality of rays such that l i < d; and detecting the protrusion using the sphere-based response.
8. The method of claim 1, wherein the processing step further comprises:
projecting a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
determining a distance value for each of the plurality of rays that has a supplementary ray that has a distance value less than the original distance value;
calculating a hemisphere-based response, wherein the hemisphere-based response is calculated by:
where d is the original distance value, l i is the length of a ray i, T is a total number of the plurality of rays, and S is a set of the plurality of rays whose supplementary rays do not have a value less than the original distance value;
and detecting the protrusion using the hemisphere-based response.
projecting a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
determining a distance value for each of the plurality of rays that has a supplementary ray that has a distance value less than the original distance value;
calculating a hemisphere-based response, wherein the hemisphere-based response is calculated by:
where d is the original distance value, l i is the length of a ray i, T is a total number of the plurality of rays, and S is a set of the plurality of rays whose supplementary rays do not have a value less than the original distance value;
and detecting the protrusion using the hemisphere-based response.
9. The method of claim 1, wherein the processing step further comprises:
projecting a plurality of rays from an edge of the distance mapped medical image, wherein the plurality of rays follow the steepest gradient; and accumulating paths of the plurality of projected rays, wherein the accumulated paths form a response image for detecting the protrusion.
projecting a plurality of rays from an edge of the distance mapped medical image, wherein the plurality of rays follow the steepest gradient; and accumulating paths of the plurality of projected rays, wherein the accumulated paths form a response image for detecting the protrusion.
10. The method of claim 1, wherein the processing step further comprises:
projecting a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
determining a distance value for each of the plurality of rays that is a fraction of the distance from the location;
calculating a sphere-based response, wherein the sphere-based response is calculated by:
where d is the original distance value, F is a fractional value between 0 and 1, d i is the distance value at a point along one of the plurality of rays, l i is the length of one of the plurality of rays at a point i, and T is the total number of points taken from i=0 to i=F
* d;
calculating a gray-level difference of the distance mapped medical image, wherein the gray level difference is calculated by:
where r-k represents the sphere-based response for each ray k; and detecting the protrusion using the gray-level difference.
projecting a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
determining a distance value for each of the plurality of rays that is a fraction of the distance from the location;
calculating a sphere-based response, wherein the sphere-based response is calculated by:
where d is the original distance value, F is a fractional value between 0 and 1, d i is the distance value at a point along one of the plurality of rays, l i is the length of one of the plurality of rays at a point i, and T is the total number of points taken from i=0 to i=F
* d;
calculating a gray-level difference of the distance mapped medical image, wherein the gray level difference is calculated by:
where r-k represents the sphere-based response for each ray k; and detecting the protrusion using the gray-level difference.
11. The method of claim 1, wherein the protrusion is one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
12. The method of claim 1, further comprising:
storing a list of one or more detected protrusions; and filtering one or more false positives from the list, wherein one of the false positives is not one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
storing a list of one or more detected protrusions; and filtering one or more false positives from the list, wherein one of the false positives is not one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
13. A system for detecting a protrusion in a medical image, comprising:
a memory device for storing a program;
a processor in communication with the memory device, the processor operative with the program to:
segment a medical image;
calculate a distance map of the medical image;
calculate a gradient of the distance mapped medical image; and process the gradient to detect a protrusion in the medical image.
a memory device for storing a program;
a processor in communication with the memory device, the processor operative with the program to:
segment a medical image;
calculate a distance map of the medical image;
calculate a gradient of the distance mapped medical image; and process the gradient to detect a protrusion in the medical image.
14. The system of claim 13, wherein the processor is further operative with the program code to:
acquire the medical image, wherein the medical image is of an anatomical part.
acquire the medical image, wherein the medical image is of an anatomical part.
15. The system of claim 14, wherein the medical image is acquired by one of a computed tomographic (CT), helical CT, x-ray, positron emission tomographic, fluoroscopic, ultrasound, and magnetic resonance (MR) imaging technique.
16. The system of claim 13, wherein the processor is further operative with the program code when processing the gradient to:
project a plurality of rays from a location in the distance mapped medical image;
calculate a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped medical image;
summing and scaling the value for each of the plurality of rays; and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the protrusion.
project a plurality of rays from a location in the distance mapped medical image;
calculate a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped medical image;
summing and scaling the value for each of the plurality of rays; and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the protrusion.
17. The system of claim 13, wherein the processor is further operative with the program code when processing the gradient to:
project a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
calculate an absolute value of a difference between a length of each of the plurality of rays and a distance value at an end of each of the plurality of rays, wherein the length of each of the plurality of rays is a fraction of the original distance value from the location;
divide a sum of the absolute value by the total number of the plurality of rays;
and detect one of a sphere-like and polyp-like shape using the division result, wherein one of the sphere-like and polyp-like shapes is the protrusion.
project a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
calculate an absolute value of a difference between a length of each of the plurality of rays and a distance value at an end of each of the plurality of rays, wherein the length of each of the plurality of rays is a fraction of the original distance value from the location;
divide a sum of the absolute value by the total number of the plurality of rays;
and detect one of a sphere-like and polyp-like shape using the division result, wherein one of the sphere-like and polyp-like shapes is the protrusion.
18. The system of claim 13, wherein the processor is further operative with the program code when processing the gradient to:
project a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
determine a distance value for each of the plurality of rays that is a fraction of the distance from the location;
calculate a sphere-based response of the plurality of rays;
calculate a hemisphere-based response of the plurality of rays; and detect the protrusion using the sphere and hemisphere-based responses.
project a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
determine a distance value for each of the plurality of rays that is a fraction of the distance from the location;
calculate a sphere-based response of the plurality of rays;
calculate a hemisphere-based response of the plurality of rays; and detect the protrusion using the sphere and hemisphere-based responses.
19. The system of claim 13, wherein the processor is further operative with the program code when processing the gradient to:
project a plurality of rays from an edge of the distance mapped medical image, wherein the plurality of rays follow the steepest gradient; and accumulate paths of the plurality of rays, wherein the accumulated paths form a response image for detecting the protrusion.
project a plurality of rays from an edge of the distance mapped medical image, wherein the plurality of rays follow the steepest gradient; and accumulate paths of the plurality of rays, wherein the accumulated paths form a response image for detecting the protrusion.
20. The system of claim 13, wherein the processor is further operative with the program code when processing the gradient to:
project a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
determine a distance value for each of the plurality of rays that is a fraction of the distance from the location;
calculate a sphere-based response of the plurality of rays;
calculate a gray-level difference of the distance mapped medical image; and detect the protrusion using the sphere-based response and the gray-level difference.
project a plurality of rays from a location comprising an original distance value in the distance mapped medical image;
determine a distance value for each of the plurality of rays that is a fraction of the distance from the location;
calculate a sphere-based response of the plurality of rays;
calculate a gray-level difference of the distance mapped medical image; and detect the protrusion using the sphere-based response and the gray-level difference.
21. The system of claim 13, wherein the protrusion is one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
22. The system of claim 13, wherein the processor is further operative with the program code when processing the gradient to:
store a list of one or more detected protrusions; and filter one or more false positives from the list, wherein one or more of the false positives is not one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
store a list of one or more detected protrusions; and filter one or more false positives from the list, wherein one or more of the false positives is not one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
23. A computer program product comprising a computer useable medium having computer program logic recorded thereon for detecting a protrusion in a medical image, the computer program logic comprising:
program code for segmenting a medical image;
program code for calculating a distance map of the medical image;
program code for calculating a gradient of the distance mapped medical image;
and program code for processing the gradient to detect a protrusion in the medical image.
program code for segmenting a medical image;
program code for calculating a distance map of the medical image;
program code for calculating a gradient of the distance mapped medical image;
and program code for processing the gradient to detect a protrusion in the medical image.
24. The system of claim 23, further comprising:
program code for acquiring the medical image.
program code for acquiring the medical image.
25. The system of claim 24, wherein the image is acquired by one of a computed tomographic (CT), helical CT, x-ray, positron emission tomographic, fluoroscopic, ultrasound, and magnetic resonance (MR) imaging technique.
26. The system of claim 23, further comprising:
program code for storing a list of one or more detected protrusions; and program code for filtering one or more false positives from the list, wherein one or more of the false positives is not one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
program code for storing a list of one or more detected protrusions; and program code for filtering one or more false positives from the list, wherein one or more of the false positives is not one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
27. The system of claim 23, wherein the protrusion is one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
28. A system for detecting a protrusion in a medical image, comprising:
means for acquiring a medical image;
means for segmenting the acquired medical image;
means for calculating a distance map of the medical image;
means for calculating a gradient of the distance mapped medical image; and means for processing the gradient to detect a protrusion in the medical image.
means for acquiring a medical image;
means for segmenting the acquired medical image;
means for calculating a distance map of the medical image;
means for calculating a gradient of the distance mapped medical image; and means for processing the gradient to detect a protrusion in the medical image.
29. The system of claim 28, further comprising:
means for storing a list of one or more detected protrusions; and means for filtering one or more false positives from the list, wherein one or more of the false positives is not one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
means for storing a list of one or more detected protrusions; and means for filtering one or more false positives from the list, wherein one or more of the false positives is not one of a nodule, lesion, polyp, pre-cancerous growth, and cancerous growth.
30. A method for detecting a polyp in an image of a colon, comprising:
acquiring the image of the colon;
segmenting a surface of the colon from a nearby structure;
calculating a distance map of the segmented surface;
calculating a gradient of the distance mapped image; and processing the gradient to detect the polyp in the colon, wherein the gradient is processed by:
projecting a plurality of rays from a location in the distance mapped image;
calculating a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped image;
summing and scaling the value for each of the plurality of rays; and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the polyp.
acquiring the image of the colon;
segmenting a surface of the colon from a nearby structure;
calculating a distance map of the segmented surface;
calculating a gradient of the distance mapped image; and processing the gradient to detect the polyp in the colon, wherein the gradient is processed by:
projecting a plurality of rays from a location in the distance mapped image;
calculating a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped image;
summing and scaling the value for each of the plurality of rays; and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the polyp.
31. The method of claim 30, wherein the image is acquired by one of a computed tomographic (CT), helical CT, x-ray, positron emission tomographic, fluoroscopic, ultrasound, and magnetic resonance (MR) imaging technique.
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US48679903P | 2003-07-11 | 2003-07-11 | |
| US60/486,799 | 2003-07-11 | ||
| US10/849,576 US7369638B2 (en) | 2003-07-11 | 2004-05-19 | System and method for detecting a protrusion in a medical image |
| US10/849,576 | 2004-05-19 | ||
| PCT/US2004/016108 WO2005015483A1 (en) | 2003-07-11 | 2004-05-20 | System and method for detecting a protrusion in a medical image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2531871A1 true CA2531871A1 (en) | 2005-02-17 |
| CA2531871C CA2531871C (en) | 2012-09-18 |
Family
ID=33567966
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2531871A Expired - Fee Related CA2531871C (en) | 2003-07-11 | 2004-05-20 | System and method for detecting a protrusion in a medical image |
Country Status (6)
| Country | Link |
|---|---|
| US (1) | US7369638B2 (en) |
| EP (1) | EP1644866A1 (en) |
| CN (1) | CN1823337B (en) |
| AU (1) | AU2004264196B2 (en) |
| CA (1) | CA2531871C (en) |
| WO (1) | WO2005015483A1 (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100686289B1 (en) * | 2004-04-01 | 2007-02-23 | 주식회사 메디슨 | Apparatus and method for forming 3d ultrasound image using volume data in the contour of a target object image |
| AT502127B1 (en) * | 2005-07-04 | 2008-10-15 | Advanced Comp Vision Gmbh Acv | METHOD FOR SEGMENTING DATA STRUCTURES |
| DE102005036998B4 (en) * | 2005-08-05 | 2014-11-20 | Siemens Aktiengesellschaft | Device for the automatic detection of abnormalities in medical image data |
| JP2007260144A (en) * | 2006-03-28 | 2007-10-11 | Olympus Medical Systems Corp | Medical image treatment device and medical image treatment method |
| US9965838B2 (en) * | 2007-09-17 | 2018-05-08 | Koninklijke Philips N.V. | Caliper for measuring objects in an image |
| US8126244B2 (en) * | 2007-09-21 | 2012-02-28 | Siemens Medical Solutions Usa, Inc. | User interface for polyp annotation, segmentation, and measurement in 3D computed tomography colonography |
| WO2012063204A1 (en) * | 2010-11-12 | 2012-05-18 | Koninklijke Philips Electronics N.V. | Identifying individual sub-regions of the cardiovascular system for calcium scoring |
| CN103501699B (en) | 2011-02-24 | 2016-01-13 | 卡丹医学成像股份有限公司 | For isolating potential abnormal method and apparatus in imaging data and it is to the application of medical image |
| CN111340756B (en) * | 2020-02-13 | 2023-11-28 | 北京深睿博联科技有限责任公司 | Medical image lesion detection merging method, system, terminal and storage medium |
| US20230043645A1 (en) * | 2021-08-04 | 2023-02-09 | Magentiq Eye Ltd | Systems and methods for detection and analysis of polyps in colon images |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5452367A (en) * | 1993-11-29 | 1995-09-19 | Arch Development Corporation | Automated method and system for the segmentation of medical images |
| US6470092B1 (en) * | 2000-11-21 | 2002-10-22 | Arch Development Corporation | Process, system and computer readable medium for pulmonary nodule detection using multiple-templates matching |
| US20020164061A1 (en) | 2001-05-04 | 2002-11-07 | Paik David S. | Method for detecting shapes in medical images |
-
2004
- 2004-05-19 US US10/849,576 patent/US7369638B2/en not_active Expired - Fee Related
- 2004-05-20 CN CN2004800198657A patent/CN1823337B/en not_active Expired - Fee Related
- 2004-05-20 WO PCT/US2004/016108 patent/WO2005015483A1/en active Search and Examination
- 2004-05-20 CA CA2531871A patent/CA2531871C/en not_active Expired - Fee Related
- 2004-05-20 AU AU2004264196A patent/AU2004264196B2/en not_active Ceased
- 2004-05-20 EP EP04753008A patent/EP1644866A1/en not_active Ceased
Also Published As
| Publication number | Publication date |
|---|---|
| CA2531871C (en) | 2012-09-18 |
| CN1823337A (en) | 2006-08-23 |
| US7369638B2 (en) | 2008-05-06 |
| AU2004264196A1 (en) | 2005-02-17 |
| AU2004264196B2 (en) | 2009-05-28 |
| US20050008205A1 (en) | 2005-01-13 |
| EP1644866A1 (en) | 2006-04-12 |
| CN1823337B (en) | 2012-03-07 |
| WO2005015483A1 (en) | 2005-02-17 |
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