SYSTEM AND METHOD FOR AUTOMATED DETECTION AND SEGMENTATION OF TUMOR BOUNDARIES WITHIN MEDICAL IMAGING DATA

• US 20090148007A1
• Filed: 11/18/2005
• Published: 06/11/2009
• Est. Priority Date: 11/19/2004
• Status: Active Grant

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Zhao, Luyin, Lee, Kwok Pun
• Granted Patent: US 8,265,355 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06F 18/28 Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30068 Mammography; Breast
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06V 10/772 Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries

Abstract

A method for segmenting regions within a medical image includes evaluating a set of candidate segmentations generated from an initial segmentation. Based on distance calculations for each candidate using derivative segmentations, the best candidate is recommended to clinician if it is better than the initial segmentation. This recommender realizes a most stable segmentation that will benefit follow-up computer aided diagnosis (i.e. classifying lesion to benign/malignant).

Claims

1. A method for classifying regions within a medical image, comprising the steps of:

training a classifier on a set of medical image training data, which training data includes segmented regions where a clinical ground truth classifying the segmented regions is known;

acquiring non-training medical image data for investigation;

processing the training data to identify and segment regions of morphological interest, using a process for computer assisted detection (CAD);

processing the segmented regions to extract a full feature set for each of the segmented regions; and

classifying the regions of interest using the feature sub-set;

wherein, the step of training includes using a recommender to realize a stable segmentation.

2. The method as set forth in claim 1, wherein the step of training further includes starting with an initial segmentation or boundary for the region, automatically generating perturbations in the parameters defining the initial segmentation, to realize a plurality of candidate segmentations, and recommending one of the candidate segmentations if the recommender determines that it is “

better”

than the initial segmentation.

3. The method set forth in claim 2, wherein the step of operating further includes a second parameter adjustment process, within which a derivative segmentation is generated for the initial and candidate segmentations.

4. A segmenter for segmenting or delineating a particular region of interest within a medical image, and generate a candidate segmentation for the region, wherein the segmenter includes a recommender to generate a plurality of segmentations by varying or perturbing the candidate segmentation boundaries, wherein if the segmenter determines the recommended segmentation is better suited for post segmentation processing, it recommends changes to the clinician candidate region, based on the perturbations in order to improve the segmented accuracy.

5. The segmenter of claim 4, wherein the recommender operates upon each variation, perturbing each to determine if it is a more desirable segmentation.

6. A classifier, comprising:

a CAD system and segmenter as set forth in claim 4; and

a false positive reduction system, in communication with the CAD system and segmenter, which includes;

a feature extractor;

a genetic algorithm in communication with the feature extractor which generates an optimal feature subset extracted by, and provided from the feature extractor; and

a support vector machine (SVM) in communication with the SVM and feature extractor for classifying each segmented region in accord with the subset of features, with a minimum of false positives.