Segmentation in medical images

• US 7,336,809 B2
• Filed: 09/30/2002
• Issued: 02/26/2008
• Est. Priority Date: 11/23/2001
• Status: Expired due to Term

• Current Assignee: MeVis Medical Solutions AG (Varex Imaging Corporation)
• Original Assignee: R2 Technology, Inc. (Hologic Incorporated)
• Inventors: Zeng, Xiaolan, Schneider, Alexander C., Zhang, Wei
• Primary Examiner(s): Mehta; Shavesh M.
• Assistant Examiner(s): BHATNAGAR, ANAND P
• Application Number:
  US10/261,196
• Publication Number:
  US 20030099385A1
• Time in Patent Office: 1,975 Days
• Field of Search: 382/173, 382/174, 382/175, 382/176, 382/177, 382/178, 382/179, 382/180, 382/130, 382/131, 382/132, 382/128, 128/922
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/20156 Automatic seed setting
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 2207/30064 Lung nodule
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models
• CPC Class Codes: G06T 7/155 involving morphological operators
• CPC Class Codes: G06T 7/187 involving region growing; involving region merging; involving connected component labelling
• CPC Class Codes: Y10S 128/922 including image analysis

Abstract

A method and system of volume segmentation is disclosed. To address throughput speed and accuracy requirements, the segmentation is divided into two stages: presegmentation and detailed segmentation. In presegmentation, a digital image volume is processed to identify anatomical structures from image backgrounds in a gross manner possibly at reduced image resolution. Detailed segmentation then involves refining and segmenting further the fine details within the anatomical structures identified by presegmentation. One result of the overall volume segmentation algorithm is a volume in which segmented regions of interest, such as nodules, are identified.

Claims

1. A method of segmenting information to identify organ nodules in a series of digital images comprising:

forming from the digital images a series of reduced resolution images having a spatial resolution that is coarser than that of said digital images;

processing the reduced resolution images to identify a reduced resolution body region and a reduced resolution background region;

using the identification of the reduced resolution body region and the reduced resolution background region to identify a body region and a background region in the digital images;

processing an organ region in the digital images to identify a boundary of an organ; and

processing the digital images to identify a region of interest in the organ.

2. The method of claim 1 wherein the organ boundary is a pleural boundary.

3. The method of claim 2 wherein the region of interest is a pleural nodule.

4. The method of claim 1 wherein processing the reduced resolution images to identify a body region and a background region comprises:

identifying in the reduced resolution images seed points at image voxels having gray level intensities exceeding a first predetermined threshold;

growing volumes from the seed points to include voxels having gray level intensities exceeding a second predetermined threshold;

identifying the body region as the largest volume grown; and

growing the background region inwards from the periphery of the reduced resolution image to the volume identified as the body region.

5. The method of claim 4 further comprising growing remaining unidentified voxels and selecting a largest or first two largest volumes as a lung field.