This application is the U.S. National Phase application under 35 U.S.C. § 371 of International Application No. PCT/EP2017/068826, filed on Jul. 26, 2017, which claims the benefit of European Application Serial No. 16182422.2, filed Aug. 2, 2016. These applications are hereby incorporated by reference herein.

FIELD OF THE INVENTION

The invention relates to an image processing system, to an image processing method, to a computer readable medium, to a computer program element.

BACKGROUND OF THE INVENTION

Lung cancer screening with CT is recognized as mortality reducing and cost efficient. Even in high risk groups, however, only 1 in 20 lung nodules is actually cancerous. One of the widely acknowledged risk factors for lung nodule malignancy is the location, that is, in which of the five lung lobes a detected nodule resides in. A good segmentation of the five pulmonary lobes is therefore desirable.

A wide range of publications describe ways for semi- or fully automated image processing methods to segment for the lung lobes. See for instance Shouliang Qi et al in “Automatic pulmonary fissure detection and lobe segmentation in CT chest images”, BioMedical Engineering OnLine, 13:59, 2014.

Existing approaches have been developed and tested on so-called diagnostic CT scans acquired with standard dose and thin-slice reconstruction.

However, CT (computed tomography) based lung screening is performed using low-dose or ultra-low dose scans with high image noise, and these are then reviewed using low resolution thick-slice reconstruction in which the thin pulmonary fissures may not be present with sufficient definition.

SUMMARY OF THE INVENTION

There may therefore be a need for alternative method and system to segment images.

The object of the present invention is solved by the subject matter of the independent claims where further embodiments are incorporated in the dependent claims. It should be noted that the following described aspect of the invention equally applies to the image processing method, to the computer program element and to the computer readable medium.

According to a first aspect of the invention there is provided an image processing system, comprising:an input interface configured for receiving an n[≥2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation;a constellation modifier configured to modify said input constellation into a modified constellation;a constellation evaluator configured to evaluate said input constellation to produce a score;a comparator configured to compare said score against a quality criterion; and an output interface for outputting said constellation if the score meets said criterion, said constellation being suitable to define a segmentation for said input image.

According to another aspect, there is provided an image processing system, comprising:an input interface configured for receiving an n[≥2]-dimensional input image with a set of anchor points defined in said input image, said set of anchor points forming an input constellation;a constellation modifier configured to modify said input constellation into a modified constellation by varying at least one coordinate of at least one of the anchor points and projecting at least one of the coordinates of the at least one anchor point of the input constellation onto a hyperplane through the anchor points, where the hyperplane is previously fitted to the anchor points in said input constellation, and where the varying is confined to within said hyperplane;a constellation evaluator configured to evaluate said modified constellation to produce a score;a comparator configured to compare said score against a quality criterion, said criterion evaluating a suitability of the modified constellation to define a segmentation for said input image; andan output interface for outputting said modified constellation if the score meets said criterion.

According to one embodiment, the constellation modifier is configured to vary at least one coordinate of at least one of the anchor points to obtain the modified constellation.

According to one embodiment, the constellation modifier is configured toapply a random operation to vary the at least one coordinate.

According to one embodiment, the constellation modifier operates to project at least one of initial coordinates of the at least one anchor point onto a hyperplane through the anchor points and where the varying is confined to within said hyperplane.

According to one embodiment, the image processing system comprises a surface fitter configured to fit an [n−1]-dimensional hypersurface to said modified input constellation, and wherein the constellation evaluator produces said score based on said hypersurface. IN this embodiment, the constellation defines said hypersurface and this may be output as the segmentation.

According to one embodiment, the image processing system comprises a classifier component configured to classify whether a segmented image portion in the input image lies to either side of the hypersurface or whether said segmented portion extends through the hypersurface.

According to one embodiment, the image processing system comprises a visualizer configured to visualize the input image with said hypersurface or said constellation on a display device.

According to one embodiment, the image processing system is configured to iterate through a plurality of constellations including said input constellation so as to improve, or to at least maintain, said score.

According to one embodiment, the number of anchor points remains fixed. According to one embodiment, the input image is a 3D image volume, in particular of a human or animal lung. The term hypersurface is used in the general sense herein. That is, if the input image is 3D, the hypersurface is a 2D surface in the usual sense in 3D. If the input image is 2D, the hyper-surface is a 1D curve in 2D.

According to another aspect there is provided a method of image processing, comprising the steps of:receiving an n[≥2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation;modifying said input constellation into a modified constellation;evaluating said input constellation to produce a score;comparing said score against a quality criterion; andoutputting said constellation if the score meets said criterion, said constellation defining a segmentation for said input image.

According to another aspect, there is provided a method of image processing, comprising the steps of:receiving an n[≥2]-dimensional input image with a set of anchor points defined in said input image, said set of anchor points forming an input constellation;modifying said input constellation into a modified constellation by varying at least one coordinate of at least one of the anchor points and projecting at least one of the coordinates of the at least one anchor point of the input constellation onto a hyperplane through the anchor points, where the varying is confined to within said hyperplane by varying at least one coordinate of at least one of the anchor points and projecting at least one of the coordinates of the at least one anchor point of the input constellation onto a hyperplane through the anchor points, where the hyperplane is previously fitted to the anchor points in said input constellation, and where the varying is confined to within said hyperplane;evaluating said modified constellation to produce a score;said score against a quality criterion, said criterion evaluating a suitability of the modified constellation to define a segmentation for said input image; andoutputting said modified constellation if the score meets said criterion.

According to one embodiment, the method comprising: fitting an [n−1]-dimensional hyper-surface to said modified input constellation, wherein the score in said evaluating step is based on said hypersurface. In this embodiment, it is the hypersurface that is output as the segmentation.

According to another aspect there is provided a computer program element, which, when being executed by one or more processing units is adapted to perform the method steps.

According to another aspect there is provided a computer readable medium having stored thereon the program element.

The proposed system affords an accurate and computationally efficient segmentation in particular for low resolution and high noise imagery, such as in lobe segmentation based on CT screening images. The system can be used in lung image segmentation tasks where one wished to segment lobe structures. The proposed approach is robust and fast enough to be applicable for low dose CT screening applications.

