Rapid Method for Feature Extraction Using RADIOMICS Applied to Medical Imaging
- ,
- Oscar Linares,
- Luis Vivanco-Aldon,
- Martin Campos-Martinez,
- Humberto Quispe-Peña,
- Julia Sobrino-Mesias
- ,
- Universidad Continental, Huancayo,
- Universidad Privada Peruano Alemana,
- Universidad Autónoma de Ica
Publication Information
Output type
Original language
EnglishPages from-to (Number of pages)
Pages 1627-1633 (7 pages)Publication milestones
- Published - 2023
Publication status
Publisher
Institute of Electrical and Electronics Engineers Inc.Publication series
- Publication series name: Proceedings of the 2nd International Conference on Edge Computing and Applications, ICECAA 2023
ISBN (Electronic)
9798350347579Publication IDs
- Scopus: 85170825041
Host publication title
Proceedings of the 2nd International Conference on Edge Computing and Applications, ICECAA 2023Abstract
In the studies of medical images, being able to classify the objects present in the images is of vital importance; these objects can be some structure of the human body, some malformation, and tumors, among others. One of the fundamental tasks is to be able to find the characteristics that help to classify the desired object; these characteristics can be found manually using mainly shape and color descriptors. In the present work we describe a methodology of how to use the RADIOMICS tool, to carry out the search for the characteristics automatically, we indicate the necessary steps and the procedures to be carried out. To demonstrate the methodology, we use the mammography modality in the detection and classification of micro calcifications, where the problem is related to being able to find them in a high-density image, taking as a starting point that their representation in the image is very small. We start the methodology with the analysis of the original image in DICOM format, then we carry out the location and marking of the images and finally as a result we present the description of the characteristics found as well as the recommendation to be used with the different classification algorithms. The methodology presented is scalable and can be used in different imaging modalities.
