Scan-Conversion Algorithm for Ridge Point Detection on Tubular Objects

An improved scan conversion algorithm for ultrasound compound scanning is proposed. In this algorithm, the input data in the spatial domain is sampled by the concentric square raster sampling (CSRS) method, and the display pixel data are filled by one-dimensional linear interpolation. The reconstruction error of the proposed algorithm is much smaller than that of other algorithms, because only one-dimensional, rather than two-dimensional, interpolation is involved. This algorithm greatly simplifies implementation of a real-time digital scan converter (DSC) for spatial compounding of ultrasound images.

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Kriging Interpolation Based Ultrasound Scan Conversion Algorithm

2006 IEEE International Conference on Information Acquisition ◽

10.1109/icia.2006.305782 ◽

2006 ◽

Cited By ~ 3

Author(s):

Liyong Ma ◽

Jiachen Ma ◽

Yi Shen

Keyword(s):

Kriging Interpolation ◽

Ultrasound Scan ◽

Conversion Algorithm ◽

Scan Conversion

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A Real-Time Scan Conversion Algorithm on Commercially Available Microprocessors

Ultrasonic Imaging ◽

10.1177/016173469601800402 ◽

1996 ◽

Vol 18 (4) ◽

pp. 241-260 ◽

Cited By ~ 1

Author(s):

Chris Basoglu ◽

Yongmin Kim ◽

Vikram Chalana

Keyword(s):

Imaging System ◽

Direct Memory Access ◽

Substantial Improvement ◽

Computing Power ◽

New Approach ◽

Conversion Algorithm ◽

Video Processor ◽

Scan Conversion ◽

Programmable Processors ◽

Time Scan

We have developed a new ultrasound scan conversion algorithm that can be executed very efficiently on modern microprocessors. Our algorithm is designed to handle the address calculations and input and output (I/O) data loading concurrently with the interpolation. The processing unit's computing power can be dedicated to performing pixel interpolations while the other operations are handled by an independent direct memory access (DMA) controller. By making intelligent use of the I/O transfer capabilities of the DMA controller, the algorithm avoids spending the processing unit's valuable computing cycles in address calculations and nonactive pixel blanking. Furthermore, the new approach speeds up the computation by utilizing the ability of superscalar and very long instruction word (VLIW) processors to perform multiple operations in parallel. Our scan conversion algorithm was implemented on a multimedia and imaging system based on the Texas Instruments TMS320C80 Multimedia Video Processor (MVP). Computing cycles are spent only on predeterminable nonzero output pixels. For example, an execution time of 11.4 ms was achieved when there are 101,829 nonzero output pixels. This algorithm demonstrates a substantial improvement over previous scan conversion algorithms, and its optimized implementation enables modern commercially available programmable processors to support scan conversion at video rates.

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