# Software Implementation of Optimized Bicubic Interpolated Scan Conversion in Echocardiography

arXiv: https://arxiv.org/abs/2005.11269 · HTML: https://mlsanity.com/paper/2005.11269/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 5 of 5)

- [Effects of Image Size on Deep Learning](https://mlsanity.com/paper/2101.11508/) (2021)
- [Evaluation of Extra Pixel Interpolation with Mask Processing for Medical Image Segmentation with Deep Learning](https://mlsanity.com/paper/2302.11522/) (2023)
- [Stochastic Rounding for Image Interpolation and Scan Conversion](https://mlsanity.com/paper/2110.12983/) (2021)
- [Toward Optimal Adenovirus Detection Using YOLO26](https://mlsanity.com/paper/2607.17799/) (2026)
- [Stride Independent Patching for Deep Learning](https://mlsanity.com/paper/2610.12216/) (2026)

## Abstract (arXiv)

This paper introduces a novel approach leveraging objective image quality assessment (IQA) metrics to optimize the outcomes of traditional bicubic (BIC) image interpolation and interpolated scan conversion algorithms. Specifically, feature selection through line chart data visualization and computing the IQA metrics scores are used to estimate the IQA-guided coefficient-k that up-dates the traditional BIC algorithm weighting function. The resulting optimized bicubic (OBIC) algorithm was subjectively and objectively evaluated using natural and ultrasound images. Results showed that the overall performance of the OBIC algorithm was equivalent to 92.22% of 180 occurrences when compared to the BIC algorithm, while it was 57.22% of 180 occurrences when compared to other algorithms. On top of that, the OBIC interpolated scan conversion algorithm generally produced crisper and better contrast cropped ultrasound sectored images than the BIC algorithm, as well as other interpolated scan conversion algorithms mentioned.