Adaptive filtering

Details Adaptive filtering Adaptive filtering

1997-09-11

2002-10-08

Patel, Jayanti K. (Department: 2623)

Image analysis

Applications

Biomedical applications

C600S442000, C600S562000

Reexamination Certificate

active

06463167

ABSTRACT:

FIELD OF THE INVENTION
The present invention relates to the field of medical image processing and especially to image adaptive image processing.
BACKGROUND OF THE INVENTION
One of the mainstays of modern medicine is the number of available medical imaging techniques. Some of the main medical imaging techniques are X-ray CT (Computed Tomography), MRI (Magnetic Resonance Imaging), ULS (Ultra Sound) and NM (Nuclear Medicine). The physical basis of the acquired image differs between the techniques. In X-ray CT, different structures in the body are differentiated by their different X-ray density; in MRI, different structures (and some functional characteristics) are differentiated by their different type and density of hydrogen bonds; in US, different structures are differentiated by their different ultrasound propagation characteristics; and in NM, differently functioning structures are differentiated by their different metabolic processes.
Images acquired by any of the above imaging techniques are far from perfect. Image quality is constrained by many factors, most notably, the allowed safe radiation levels. In addition, the internal parts of the human body are always in motion, so imaging or image acquisition time is also a limitation on image quality.
In many cases, the acquisition process can be optimized either to enhance certain types of details in the images or to reduce the noise levels in the images. Alternatively, the acquired images are post-processed using well known image processing techniques to enhance image quality parameters. Typically, enhancement of one parameter of image quality comes at the expense of a second parameter, for example, edge detection usually adds noise to the image. In high gradient portions of the image, edge enhancement may add significant artifacts. Other types of image processing also add artifacts to the image which may be mistaken by the diagnostician to be pathological indications. Sometimes, the processing masks pathological indications, which would otherwise be apparent in the image.
One solution to the trade-off between image enhancement and artifact addition is to store several image sets, one for each type of reconstruction or post processing. This solution is problematical in two respects, first, the volume of stored data is significantly increased, and second, the diagnostician must correlate between images to ascertain whether a pathological indication is actually an artifact.
Some image processing techniques, such as median filtering and Sobel filtering, have a built in responsiveness to local texture, and so, generate fewer artifacts than other image processing techniques.
Another solution to the trade-off is to process only regions of interest (ROI) in the image which seem to require special processing. However, using ROIs can add severe artifacts to the image at the ROI border.
Yet another solution to the trade-off is to automatically extract features from the image and to apply specific processing to particular features.
SUMMARY OF THE INVENTION
It is an object of some aspects of the present invention to provide a method of post-processing images which is self-limiting to certain portions of the image.
It is another object of some aspects of the present invention to provide a method of post-processing images which generates a minimum of artifacts.
It is yet another object of some aspects of the present invention to provide a method of post-processing images in which portions of the image which correspond to different body tissues are post-processed differently.
In medical imaging, image pixel values often corresponds to an absolute physical quantity, for example, x-ray CT values correspond to tissue density, and NM values correspond to tissue absorption of radioactive materials. The acquired image pixel values represent tissue characteristics, such as x-ray absorption, perfusion and functionality. This is in contrast to most types of non-medical imaging. In photography, for example, an image pixel value corresponds to the amount of light reflected by an image feature, which is usually a value quite different from the reflectivity of the image feature, due to the complexity of the lighting environment, distance, etc.
In X-ray CT, pixel values are known as CT numbers, which are used to identify the tissue corresponding to the pixel. In this context it is useful to note that most body organs are composed of relatively large regions which have a substantially uniform tissue structure. Diseased portions of the tissue usually have a different and/or non-uniform tissue structure. In other imaging modalities, such as MRI, ultrasound and NM, the pixel values can also be used to identify tissue characteristics, as will be described more fully below.
In one preferred embodiment of the invention, the post-processing method uses an image processing technique which operates differently, depending on the pixel value, i.e., on the tissue type. In one example, the image processing technique enhances edges in the bone, but not in muscles or in liver tissue.
Additionally or alternatively, the image processing technique is responsive to local tissue texture. For example, edges are enhanced in portions of the lungs having a low local gradient but not in portions of the lungs having a high local gradient.
Additionally or alternatively, the image processing technique is responsive to boundaries between different tissue types. For example, edges could be enhanced inside muscle but not in the boundary area between muscle and bone.
Further additionally or alternatively, the image processing technique is responsive to characteristics of the boundary between neighboring tissues, such as the width thereof. For example, edges would be enhanced in a narrow boundary, but not in a wide boundary. In another example, edges would be enhanced in a low gradient boundary, but not in a high gradient boundary.
Additionally or alternatively, the image processing technique is responsive to the type of boundary between the two neighboring tissues. For example, edges would be enhanced in the boundary between muscle and fat, but not between muscle and bone.
In one preferred embodiment of the invention, the image processing technique determines the tissue type of a pixel based on the local pixel value, such as by using a look-up table. One example where individual pixel values is important is detecting bone remains in old bones. Alternatively, the image processing technique determines the tissue type based on an average (weighted) pixel value of several neighboring pixels and/or on the local texture and/or on the local variance and/or on other local image measures. Preferably, a minimum portion size is determined based on the noise level of the image. In addition, single pixels located adjacent to tissue boundaries can be classified on a pixel by pixel basis.
Additionally or alternatively, the image processing technique segments the image into single tissue areas. One way of identifying that two regions on an image are actually parts of a single organ uses contiguous image slices. Usually, the two regions will be connected in
3
D through the contiguous image slices.
In a preferred embodiment of the invention, after such value-based segmentation, other supplementary techniques may be used to complete the segmentation. These techniques may be manual techniques (operator input) or may be based on artifacts created by the segmentation. In addition, these supplementary techniques may be based on factors other than tissue CT numbers. For example, an operator may indicate a pixel which has been classified as muscle and reclassify it as bone. Such reclassification will preferably extend to all the similarly classified pixels which are connected to the pixel.
The image processing techniques of some embodiments of the present invention result in more medically correct results than previously known techniques since the type of parameter enhancement depends on the tissue. In particular, the enhancement technique can be chosen so that it minimizes false positives or false negatives

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