Authors: José Raniery Ferreira Paulo Mazzoncini de AzevedoMarques Marcelo Costa Oliveira
Publish Date: 2016/08/23
Volume: 12, Issue: 3, Pages: 509-517
Abstract
Lung cancer is the leading cause of cancerrelated 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 contentbased 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 nodulesA 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 Secondorder texture features were extracted from a cooccurrence matrix Relevant features were selected by a correlationbased 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 recallFor this type of study formal consent is not required This study used a public image database which all protected health information PHI contained within the DICOM headers of the images were removed in accordance with Health Insurance Portability and Accountability Act HIPAA guidelines
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