Proceedings Article | 17 May 2002
KEYWORDS: Image enhancement, Image processing, Convolution, Image filtering, Medical imaging, Digital mammography, X-rays, Boxcar filters, Surgery, Radiography
Unsharp masking is a widely used image enhancement method in medical imaging, e.g., in computed radiography, digital radiography, and digital mammography. It mainly consists of 3 steps: (1) convolving an input image with a lowpass filter, (2) obtaining a highpass-filtered image by subtracting the lowpass-filtered image from the original image, and (3) adding the weighted highpass-filtered image to the original image. It is computationally expensive, e.g., convolving a 2k x 2k image with a 21 x 21 kernel alone requires about 3.7 billion arithmetic operations. To support this high computational demand for unsharp masking, hardware-based solutions using ASIC, FPGA and FPLD have been developed and used. While they have reasonably met the computing requirement, they suffer from limited flexibility. On the other hand, software solutions using programmable processors are more flexible and can easily change algorithmic parameters, such as filter kernel size, and incorporate new features, but they have not been able to meet the fast computing requirement. Modern programmable mediaprocessors, such as MAP-CA and Texas Instruments TMS320C64x, can meet both fast computing and flexibility requirements due to their high computing power and full programmability. In this paper, we present an efficient implementation of adaptive unsharp masking on a MAP-CA mediaprocessor. For a 2k x 2k 16-bit image, our adaptive unsharp masking operation with a 149 x 149 boxcar kernel takes only 300 ms. This fast unsharp masking not only reduces the overall processing time in imaging modalities, but also allows the operator to adjust the selected parameters interactively for optimal image quality. Our implementation on the MAP-CA can be easily extended to other high-performance mediaprocessors, such as TMS320C64x and Pentium 4.
