Development of a Robust Depth-Pressure Estimation Algorithm for a Vision-Based Breast Self-Examination Guidance System

TL;DR

This paper presents a new depth-pressure estimation algorithm for a vision-based breast self-examination guidance system, improving early detection of breast cancer using accessible technology and optimized feature extraction methods.

Contribution

The study introduces a universal depth-pressure estimation algorithm tailored for breast self-examination guidance, utilizing optimized feature extraction for practical use on average technology.

Findings

Law's Textures Histogram and Local Binary Pattern Global Histogram are most effective.

Combining feature schemes improves estimation accuracy.

Algorithm is suitable for use by average consumers.

Abstract

In the case of breast cancer, as with most cancers, early detection can significantly improve a person's chances of survival. This makes it important for there to be an effective and accessible means of regularly checking for manifestations of the disease. A vision-based guidance system (VBGS) for breast self-examination (BSE) is one way to improve a person's ability to detect the cancerous systems. In response to this need, this study sought to develop a depth-pressure estimation algorithm for the proposed VBGS. A large number of BSE videos were used to train the model, and these samples were segmented according to breast size, which was found to be a differentiation factor in the depth-pressure estimation. The result was an algorithm that was applicable for universal use. In addition to these, several feature extraction schemes were tested with the objective of making the algorithm functional on average technology. It was found that Law's Textures Histogram and Local Binary Pattern Global Histogram were the most effective in estimating pressure using visual data. Moreover, combinations of the two schemes further improved the accuracy of the model in estimation. The resulting algorithm was thereby fit to be used by the average consumer.