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Rapid Method for Feature Extraction Using RADIOMICS Applied to Medical Imaging | IEEE Conference Publication | IEEE Xplore

Rapid Method for Feature Extraction Using RADIOMICS Applied to Medical Imaging


Abstract:

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, ...Show More

Abstract:

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.
Date of Conference: 19-21 July 2023
Date Added to IEEE Xplore: 16 August 2023
ISBN Information:
Conference Location: Namakkal, India

I. Introduction

Reviewing the literature, we found works where they use RADIOMICS, we found in studies about the study of meningiomas for which a radiomic quality score was used, together with a multivariate prediction model for individual diagnosis or prognosis and the initiative for the standardization of image biomarkers., a study of art was carried out in works published in PubMed MEDLINE and Embase where studies related to meningiomas were identified, a total of 138 articles were obtained where various techniques for classification were appreciated, to which 25 relevant articles were organized according to the aforementioned guidelines, from which it is concluded that the radiomic quality for meningioma is insufficient, the recognition of the radiomic quality guidelines, the multivariate prediction model and the individual prognosis was of better quality of the radiomic studies of meningioma and allow its clinical application [1].

References

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