Medical image ROI (Region of Interest) compression method based on lifting wavelet and PCNN (Pulse Coupled Neural Network)

A technology of medical images and compression methods, which is applied in image coding, image data processing, instruments, etc., and can solve the problems of complex orthonormal basis establishment process and so on.

Inactive Publication Date: 2010-12-08
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

In the past, the image coding compression algorithm based on PCNN segmentation and reconstruction using orthogonal basis (see literature [9]: MA Yi-de, LI Lian, DAIRuo-lan. Automated image segmentation ...

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  • Medical image ROI (Region of Interest) compression method based on lifting wavelet and PCNN (Pulse Coupled Neural Network)
  • Medical image ROI (Region of Interest) compression method based on lifting wavelet and PCNN (Pulse Coupled Neural Network)
  • Medical image ROI (Region of Interest) compression method based on lifting wavelet and PCNN (Pulse Coupled Neural Network)

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[0065] Such as figure 1 As shown, the method is generally divided into a preprocessing process, a lossy compression process, a lossless compression process, and a restoration process. In the preprocessing process, there are automatic and manual methods for the segmentation of the region of interest. Automatic segmentation is based on the bimodal characteristics of the gray histogram of the pathological area in the medical image, using single or multiple thresholds for segmentation, but the multi-threshold automatic segmentation method is not complete, it is time-consuming, and has a large gap with the doctor's judgment. gap. Therefore, this paper adopts the artificial interactive difference image segmentation method for image segmentation. In this method, the doctor uses the mouse to circle the pathological area of ​​interest (such as figure 2 shown), and then use the subtraction method to separate the region of interest from the non-interest region. The difference method r...

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Abstract

The invention publishes a medical image ROI compression method based on a lifting wavelet and a PCNN, which comprises the following steps of: circling a region of interest by a doctor, and separating the region of interest from a region of no interest by a difference image method; adopting lossless compression in the region of interest, constructing compactly supported biorthogonal wavelet transformation through a lifting scheme, and then carrying out Huffman encoding; adopting lossy compression in the region of no interest, segmenting gray-value pixel approximate points through the PCNN, carrying out ignition operation, and then carrying out run-length encoding; and finally carrying out inverse transformation restoration, merging the region of interest and the region of no interest, and eliminating a boundary discontinuity problem through linear interpolation. Experimental results prove that the region of interest can be flexibly selected and controlled by the compression method, the used information for doctor diagnosis can be completely reserved, and the compression ratio is higher. Meanwhile, the computation for an ROI mask and the computation and the encoding for a wavelet coefficient difference value are omitted, the compression and decompression time and the algorithm complexity are reduced, and the image processing and transmitting efficiency is improved.

Description

technical field [0001] The invention designs a medical image ROI compression method based on lifting wavelet and PCNN. Background technique [0002] Compared with ordinary images, medical images have higher resolution, more quantization levels, and a larger amount of data. With the development of PACS (Picture Archiving and Communication Systems) image communication systems and remote real-time medical care, there is an urgent need for medical image compression, that is, under the premise of ensuring image quality. , to convert the bitmap information of the medical image into an array form with reduced data volume. At present, image compression can be roughly divided into lossless compression and lossy compression. Lossless compression can completely restore the original image without loss of detail information, but its compression rate is generally between 50% and 80%, and the amount of data after compression is still very large. Lossy compression cannot completely restor...

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Application Information

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IPC IPC(8): H03M7/30G06T9/00
Inventor 郭业才段宇平
Owner NANJING UNIV OF INFORMATION SCI & TECH
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