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In the case of breast cancer, as with most cancers, early detection cansignificantly improve a person's chances of survival. This makes it importantfor there to be an effective and accessible means of regularly checking formanifestations of the disease. A vision-based guidance system (VBGS) for breastself-examination (BSE) is one way to improve a person's ability to detect thecancerous systems. In response to this need, this study sought to develop adepth-pressure estimation algorithm for the proposed VBGS. A large number ofBSE videos were used to train the model, and these samples were segmentedaccording to breast size, which was found to be a differentiation factor in thedepth-pressure estimation. The result was an algorithm that was applicable foruniversal use. In addition to these, several feature extraction schemes weretested with the objective of making the algorithm functional on averagetechnology. It was found that Law's Textures Histogram and Local Binary PatternGlobal Histogram were the most effective in estimating pressure using visualdata. Moreover, combinations of the two schemes further improved the accuracyof the model in estimation. The resulting algorithm was thereby fit to be usedby the average consumer.

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