Tomography analysis tool: an application for image analysis based on unsupervised machine learning

We developed a graphical user interface (GUI) to analyse tomographic images of superconducting Nb3Sn wires designed for the next generation accelerator magnets. The Tomography Analysis Tool (TAT) relies on the k-means algorithm, an unsupervised machine learning technique which is widely used to partition images into separated clusters. The GUI is compatible with both Linux and Windows operating systems. The software reliability was tested by optical inspecting the tomographic images superimposed on the clustered image obtained by the k-means algorithm. TAT was proven to correctly segment the various components of the Nb3Sn superconducting wires with single pixel precision. Finally, this software can be a useful tool for the scientific community to segment and analyse quickly and reproducibly tomographic images.