The overall quantity of nuclear medicine procedures has increased remarkably in recent years, making them a daily tool capable of reaching wide sectors of the population. Regarding the nuclear medicine therapeutic applications, it is worth noting that there is an increasing demand of novel techniques and greater variety of radioisotopes requiring accurate patient-specific dosimetry aimed at evaluating lethal damage to the tumor while maintaining acceptable dose levels in healthy tissues. Image-guided internal dosimetry appears as particularly suitable for theranostics procedures, which allow the joint implementation of diagnose and treatment. In this case, the correct segmentation of the images is critical for the identification of different tissues and organs. On the other hand, modern tools based on data science and artificial intelligence have spread in several fields, particularly in the digital image processing. The use of machine learning models for digital image processing appears as a promising opportunity to complement clinical analysis by experts. This paper reports about an unsupervised segmentation heuristic algorithm using clustering and machine learning techniques together, based on the use of two algorithms: K-Means and HDBSCAN. The results obtained highlight the capacity of automatic segmentation by means of clustering algorithms, becoming a useful tool to assist clinician experts and shorten the segmentation times.
Electrofused magnesium oxide classification using digital image processing and machine learning techniques
