0000000000067890
AUTHOR
Andrés Larroza
showing 7 related works from this author
Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction.
2017
[EN] Purpose: To investigate the ability of texture analysis to differentiate between infarcted nonviable, viable, and remote segments on cardiac cine magnetic resonance imaging (MRI). Methods: This retrospective study included 50 patients suffering chronic myocardial infarction. The data were randomly split into training (30 patients) and testing (20 patients) sets. The left ventricular myocardium was segmented according to the 17-segment model in both cine and late gadolinium enhancement (LGE) MRI. Infarcted myocardium regions were identified on LGE in short-axis views. Nonviable segments were identified as those showing LGE 50%, and viable segments those showing 0 < LGE < 50% transmural …
Identifying the primary site of origin of MRI brain metastases from lung and breast cancer following a 2D radiomics approach
2017
Detection of brain metastases in patients with undiagnosed primary cancer is unusual but still an existing phenomenon. In these cases, identifying the cancer site of origin is non-feasible by visual examination of magnetic resonance (MR) images. Recently, radiomics has been proposed to analyze differences among classes of visually imperceptible imaging characteristics. In this study we analyzed 46 T1-weighted MR images of brain metastases from 29 patients: 29 of lung and 17 of breast origin. A total of 43 radiomics texture features were extracted from the metastatic lesions. Support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers were implemented to evaluate the classificati…
Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic…
2017
[EN] The purpose of this study was to differentiate acute from chronic myocardial infarction using machine learning techniques and texture features extracted from cardiac magnetic resonance imaging (MRI). The study group comprised 22 cases with acute myocardial infarction (AMI) and 22 cases with chronic myocardial infarction (CMI). Cine and late gadolinium enhancement (LGE) MRI were analyzed independently to differentiate AMI from CMI. A total of 279 texture features were extracted from predefined regions of interest (ROIs): the infarcted area on LGE MRI, and the entire myocardium on cine MRI. Classification performance was evaluated by a nested cross-validation approach combining a feature…
Texture analysis for infarcted myocardium detection on delayed enhancement MRI
2017
Detection of infarcted myocardium in the left ventricle is achieved with delayed enhancement magnetic resonance imaging (DE-MRI). However, manual segmentation is tedious and prone to variability. We studied three texture analysis methods (run-length matrix, co-occurrence matrix, and autoregressive model) in combination with histogram features to characterize the infarcted myocardium. We evaluated 10 patients with chronic infarction to select the most discriminative features and to train a support vector machine (SVM) classifier. The classifier model was then used to segment five human hearts from the STACOM DE-MRI challenge at MICCAI 2012. The Dice coefficient was used to compare the segmen…
A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma
2017
[EN] Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictiv…
Comment on “Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibili…
2017
We have read with great interest the article published by Tiwari et al, “Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.”[1][1] In their article, they refer to our work regarding brain metastasis
Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI
2015
Purpose To develop a classification model using texture features and support vector machine in contrast-enhanced T1-weighted images to differentiate between brain metastasis and radiation necrosis. Methods Texture features were extracted from 115 lesions: 32 of them previously diagnosed as radiation necrosis, 23 as radiation-treated metastasis and 60 untreated metastases; including a total of 179 features derived from six texture analysis methods. A feature selection technique based on support vector machine was used to obtain a subset of features that provide optimal performance. Results The highest classification accuracy evaluated over test sets was achieved with a subset of ten features…