Related Experiment Video For Content-based image retrieval
Updated: Mar 16, 2026
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
Published on: January 10, 2025
Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval
José Raniery Ferreira1, Paulo Mazzoncini de Azevedo-Marques2, Marcelo Costa Oliveira3
1Center of Imaging Sciences and Medical Physics, Internal Medicine Department, Ribeirao Preto Medical School, University of Sao Paulo (USP), Av. dos Bandeirantes, 3900, Campus USP, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil. jose.raniery@usp.br.
Purpose:
Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules.
Methods:
A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall.
Results:
Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval.
Conclusion:
Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.
