New Approach To Automatic Detection Of Strange Objects In Body Scan Images

In this article we present a new approach to automatic detection of strange objects in body scan images specifically in the region of the arms. This methodology is based on a combination of textures, a classifier (K-means or MLP) and a post-processing step. The tests were performed on 23 body scan images of volunteers. The accuracy of this approach is verified by the similarity and sensitivity coefficient with the count of identified and unidentified strange objects. The results indicate that the classifier K-means obtained 92.3% and 78.7% for the similarity and sensitivity coefficient, respectively, while the MLP neural network obtained 100% and 61.9% for the same coefficients. Given these results, it confirms the effectiveness of the methodology and discusses the use of MLP classifier for applications with strict visual inspection stage and the use of K-means classifier in applications where the incidence of false positives hinders the inspection result.

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