Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2002-07-17
2003-07-15
Lateef, Marvin M. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C382S172000, C382S172000
Reexamination Certificate
active
06592523
ABSTRACT:
BACKGROUND OF INVENTION
This invention generally relates to imaging for the purpose of medical diagnosis. In particular, the invention relates to methods for imaging tissue and blood flow by detecting ultrasonic echoes reflected from a scanned region of interest in a human body.
Conventional ultrasound scanners are capable of operating in different imaging modes. In the B mode, for example, two-dimensional images can be generated in which the brightness of each display pixel is derived from the value or amplitude of a respective acoustic data sample representing the echo signal returned from a respective focal position within a scan region.
In B-mode imaging, an ultrasound transducer array is activated to transmit beams focused at respective focal positions in a scan plane. After each transmit firing, the echo signals detected by the transducer array elements are fed to respective receive channels of a receiver beam-former, which converts the analog signals to digital signals, imparts the proper receive focus time delays and sums the time-delayed digital signals. For each transmit firing, the resulting vector of raw acoustic data samples represents the total ultrasonic energy reflected from a succession of ranges along a receive beam direction. Alternatively, in multi-line acquisition two or more receive beams can be acquired following each transmit firing.
In conventional B-mode imaging, each vector of raw acoustic data samples is envelope detected and the resulting acoustic data is compressed (e.g., using a logarithmic compression curve). The compressed acoustic data is output to a scan converter, which transforms the acoustic data format into a video data format suitable for display on a monitor having a conventional array of rows and columns of pixels. This video data is typically referred to as “raw pixel intensity values”. The frames of raw pixel intensity data are mapped to a gray scale for video display. Each gray-scale image frame, typically referred to as “gray-scale pixel intensity values”, is then sent to the video monitor for display. In the case where a one-to-one gray-scale mapping is in effect, the raw and gray-scale pixel intensity values will be one and the same.
While a number of image processing parameters may control the final image presentation, it is often difficult to determine which of these parameters, or which combination of the parameters, may be adjusted to provide the optimal image presentation. Often, the image processing techniques must be adjusted in accordance with empirical feedback from an operator, such as a physician or technician.
The facility with which a reconstructed discrete pixel image may be interpreted by an observer may rely upon intuitive factors of which the observer may not be consciously aware. For example, in medical diagnostic ultrasound imaging, a physician or radiologist may seek specific structures or specific features in an image such as bone, soft tissue or fluids. Such structures may be physically defined in the image by contiguous edges, contrast, texture, and so forth.
The presentation of such features often depends heavily upon the particular image processing technique employed for converting the detected values representative of each pixel to modified values used in the final image. The image processing technique employed can therefore greatly affect the ability of an observer or an analytical device to recognize salient features of interest. The technique should carefully maintain recognizable structures of interest, as well as abnormal or unusual structures, while providing adequate textural and contrast information for interpretation of these structures and surrounding background. Ideally the technique will perform these functions in a computationally efficient manner so that processing times, as well as hardware requirements, can be minimized.
In ultrasound imaging, acquired images can be corrupted by slowly varying multiplicative inhomogeneities or non-uniformities in spatial intensity. Such non-uniformities can hinder visualization of the entire image at a given time, and can also hinder automated image analysis. When the image is corrected for non-uniformity alone, noise in the dark regions of the original image becomes multiplicatively enhanced thereby providing an unnatural look to the image. Such images are usually not preferred by radiologists.
There is a need for a computationally efficient method of pre-filtering ultrasound images in real time to reduce noise prior to the performance of additional image enhancement steps such as non-uniformity equalization and contrast enhancement.
SUMMARY OF INVENTION
The invention is directed to improving ultrasound images by means of image filtering. The image filtering is especially useful in combination with subsequent image enhancement steps, namely, non-uniformity equalization and contrast enhancement, but the image filtering process of the invention is independent of the subsequent image enhancement processes utilized.
The invention provides an improved technique for enhancing discrete pixel ultrasound images which is computationally efficient and which maintains image quality. The technique combines multi-resolution decomposition with a segmentation-based technique that identifies structures within an image and separately processes the pixels associated with those structures. This combination exploits the redundancies of an image while also allowing the separate processing of structures and non-structures.
Because of the efficiency of the disclosed technique, real-time or near real-time ultrasound imaging may be performed without utilizing hardware-based noise reduction techniques that can result in degraded, inferior images. In an exemplary embodiment, multi-resolution decomposition occurs when an input is shrunk by a given factor, allowing for the exploitation of redundancies in the image during subsequent processing. The shrunken image is then processed using a segmentation-based technique that begins by identifying the structural elements within a blurred or smoothed image. The segmentation is based on both gradient threshold and the distance from the near field. This segmentation processing renders the structural details more robust and less susceptible to noise and selectively suppresses near-field artifacts. While small, isolated regions may be filtered out of the image, certain of these may be recovered to maintain edge and feature continuity.
Following identification of the structures, portions of the image, including structural and non-structural regions, are smoothed. Structural regions are smoothed to enhance structural features in dominant orientations, thereby providing consistency both along and across structures. Non-structural regions may be homogenized to provide an understandable background for the salient structures.
Upon completion of the segmentation-based processing, the image is expanded by the same factor used during shrinking, thereby returning the image to its original size. Original texture is then added back to non-structural regions by blending to further facilitate interpretation of both the non-structural and structural features. In addition, a small predetermined fraction of intensity-dependent, uniform random noise is added to the non-structure region pixels whose intensities are above a predetermined intensity threshold, thereby mitigating ultrasound speckle in the expanded image while leaving non-echogenic subregions of the non-structure region undisturbed.
One aspect of the invention is an ultrasound imaging system comprising a data acquisition system for acquiring acoustic data, an image processor for converting acoustic data into a set of pixel intensity values for each image, a display monitor for displaying images, and a computer programmed to perform the following steps:(a) shrinking an initial image by a predetermined factor to produce a shrunken image;(b) creating a first binary mask as a function of whether pixels of the shrunken image have gradients greater than a gradient threshold and locations more than a prede
Avinash Gopal B.
Ghosh Pinaki
GE Medical Systems Global Technology Company LLC
Jung William C.
Lateef Marvin M.
Ostrager Chong & Flaherty LLP
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