A Novel Approach for the Segmentation of Breast Thermal Images Combining Image Processing and Collective Intelligence

Most studies analyzing medical images at some stage require the demarcation of boundaries of biological structures. This process is called segmentation. In some contexts, current techniques present satisfactory results, but in others, like breast segmentation in thermographies, it remains an open problem. Several studies have investigated the use of automated solutions for this problem. However, the automatic process does not always present a satisfactory result, requiring the active involvement of a specialist for validating it and re-segmenting images when necessary. As such task can be expensive and take too long to be completed, this scenario drives the exploration of alternative approaches for the segmentation process. Hence, in this work we propose an alternative that combines traditional techniques of image processing with techniques of collective intelligence, which is based on the wisdom of crowds to solve problems in a faster and less expensive way. We present SegMedBC, a prototype in which the methods previously mentioned are applied to improve the segmentation process. Furthermore, an experimental study is carried out to validate the involvement of lay users in this activity.