Apparatus and method for detecting objects in three-dimensional volume images

Abstract

The invention discloses an apparatus and method for detecting objects in a three-dimensional volumetric image. The apparatus includes a slice sampling unit which is configured to sample the three-dimensional volumetric image so as to obtain a plurality of two-dimensional images, a detection unit which is configured to detect two-dimensional regions of interest from each two-dimensional image in the plurality of two-dimensional images which are obtained by the slice sampling unit through sampling, a combination unit which is configured to combine three-dimensional regions of interest through utilizing the two-dimensional regions of interest which are detected by the detection unit, and a segmentation unit which is configured to perform three-dimensional segmentation on the three-dimensional regions of interest so as to extract targeted objects.

Description

technical field [0001] The present invention relates to an image processing apparatus and method, and more particularly, to an apparatus and method for detecting objects in a three-dimensional (3D) volumetric image. Background technique [0002] Almost all 3D computer-aided diagnosis (CAD) systems are designed to detect target objects in high-quality, high-resolution 3D modality medical images such as CT and MRI images. Most existing detection methods are directly applied to 3D images to detect 3D objects or 3D boundary regions. However, since 3D volume images usually have a large amount of data, scanning the entire volume image is very expensive, so most existing detection methods use simple image features and models for detection, which may affect the accuracy of detection results. [0003] Existing detection methods can be divided into three categories: histogram-based morphological matching, template-based and Harr-like feature-based methods. Among them, the first type...

AI Technical Summary

Problems solved by technology

Among them, the first type of detection method is only effective for high-quality 3D images, so the scope of application is very narrow; the second type of method needs to know the template of the target object in advance, but it is difficult to know the template in advance for the target substance with various appearances. Therefore, this The first method is also not applicable; the third method can only effectively monitor high-quality images with simple features, and this method is also not applicable when used to detect target individuals with multiple types and complex features

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