COMPUTER-IMPLEMENTED LESION DETECTION METHOD AND APPARATUS

Abdel Douiri; Musib Siddique; Xujiong Ye; Richard Boyes; Gareth  Beddoe; Gregory Slabaugh

COMPUTER-IMPLEMENTED LESION DETECTION METHOD AND APPARATUS

Abdel Douiri (Inventor), Musib Siddique (Inventor), Xujiong Ye (Inventor), Richard Boyes (Inventor), Gareth Beddoe (Inventor), Gregory Slabaugh (Inventor)

Research output: Patent

Abstract

An automated detection method of normal and abnormal lesions from image data is presented. This method is based on local intensity dispersion analysis and can be used in many medical computed-aided detection (CAD) systems. A complete explanation is presented for a colon CAD system, but the invention can be utilized in other imaging modalities and can be extended as well to other CAD applications. Conventional CTC Colon CAD lesion detectors are often based on simple shape-specific methods and therefore they can miss some deformed lesions that have complex shapes. Embodiments of the invention extract highly probable lesions including and not limited to certain classes of shapes, using a fast and a robust method based on local intensity dispersion information provided by the Hessian-matrix at each voxel. Furthermore, the processing requirements are minimal, and the implementation is simple in comparison to other known techniques.

Original language	English
Patent number	WO2010034968 A1
IPC	G06T7/00
Publication status	Published - 1 Apr 2010

Keywords

medical device
computed-aided diagnosis
computed-aided detection

Access to Document

1 Conference paper
1 Article

SHAPE-BASED CT LUNG NODULE SEGMENTATION USING FIVE-DIMENSIONAL MEAN SHIFT CLUSTERING AND MEM WITH SHAPE INFORMATION
Ye, X., Siddique, M., Douiri, A., Beddoe, G. & Slabaugh, G., Jan 2009, 2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2. NEW YORK: IEEE, p. 482-485 4 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference paper
3 Citations (Scopus)
Local diffusion regularization method for optical tomography reconstruction by using robust statistics
Douiri, A., Schweiger, M., Riley, J. & Arridge, S., 15 Sept 2005, In: Optics Letters. 30, 18, p. 2439-2441 3 p.
Research output: Contribution to journal › Article › peer-review
23 Citations (Scopus)

Cite this

@misc{d1c366b500a64232b6ffee0e82df5d4b,

title = "COMPUTER-IMPLEMENTED LESION DETECTION METHOD AND APPARATUS",

abstract = "An automated detection method of normal and abnormal lesions from image data is presented. This method is based on local intensity dispersion analysis and can be used in many medical computed-aided detection (CAD) systems. A complete explanation is presented for a colon CAD system, but the invention can be utilized in other imaging modalities and can be extended as well to other CAD applications. Conventional CTC Colon CAD lesion detectors are often based on simple shape-specific methods and therefore they can miss some deformed lesions that have complex shapes. Embodiments of the invention extract highly probable lesions including and not limited to certain classes of shapes, using a fast and a robust method based on local intensity dispersion information provided by the Hessian-matrix at each voxel. Furthermore, the processing requirements are minimal, and the implementation is simple in comparison to other known techniques.",

keywords = "medical device, computed-aided diagnosis, computed-aided detection ",

author = "Abdel Douiri and Musib Siddique and Xujiong Ye and Richard Boyes and Gareth Beddoe and Gregory Slabaugh",

year = "2010",

month = apr,

day = "1",

language = "English",

type = "Patent",

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TY - PAT

T1 - COMPUTER-IMPLEMENTED LESION DETECTION METHOD AND APPARATUS

AU - Douiri, Abdel

AU - Siddique, Musib

AU - Ye, Xujiong

AU - Boyes, Richard

AU - Beddoe, Gareth

AU - Slabaugh, Gregory

PY - 2010/4/1

Y1 - 2010/4/1

N2 - An automated detection method of normal and abnormal lesions from image data is presented. This method is based on local intensity dispersion analysis and can be used in many medical computed-aided detection (CAD) systems. A complete explanation is presented for a colon CAD system, but the invention can be utilized in other imaging modalities and can be extended as well to other CAD applications. Conventional CTC Colon CAD lesion detectors are often based on simple shape-specific methods and therefore they can miss some deformed lesions that have complex shapes. Embodiments of the invention extract highly probable lesions including and not limited to certain classes of shapes, using a fast and a robust method based on local intensity dispersion information provided by the Hessian-matrix at each voxel. Furthermore, the processing requirements are minimal, and the implementation is simple in comparison to other known techniques.

AB - An automated detection method of normal and abnormal lesions from image data is presented. This method is based on local intensity dispersion analysis and can be used in many medical computed-aided detection (CAD) systems. A complete explanation is presented for a colon CAD system, but the invention can be utilized in other imaging modalities and can be extended as well to other CAD applications. Conventional CTC Colon CAD lesion detectors are often based on simple shape-specific methods and therefore they can miss some deformed lesions that have complex shapes. Embodiments of the invention extract highly probable lesions including and not limited to certain classes of shapes, using a fast and a robust method based on local intensity dispersion information provided by the Hessian-matrix at each voxel. Furthermore, the processing requirements are minimal, and the implementation is simple in comparison to other known techniques.

KW - medical device

KW - computed-aided diagnosis

KW - computed-aided detection

M3 - Patent

M1 - WO2010034968 A1

ER -

COMPUTER-IMPLEMENTED LESION DETECTION METHOD AND APPARATUS

Abstract

Keywords

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Research output

SHAPE-BASED CT LUNG NODULE SEGMENTATION USING FIVE-DIMENSIONAL MEAN SHIFT CLUSTERING AND MEM WITH SHAPE INFORMATION

Local diffusion regularization method for optical tomography reconstruction by using robust statistics

Cite this