Browsing by Author "Contreras Ojeda, Sara"

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A low-cost multi-modal medical imaging system with fringe projection profilometry and 3D freehand ultrasound
(2020-01-03) Meza, Jhacson; Simarra, Pedro; Contreras Ojeda, Sara; Romero, Lenny A.; Contreras Ortiz, Sonia Helena; Arámbula Cosío, Fernando; Marrugo Hernández, Andrés Guillermo
The growing need to perform surgical procedures, monitoring, and intervention of greater precision have led to the development of multimodal medical imaging systems. Multimodal images are a strategy to overcome the limitations of medical imaging technologies by combining the strengths of individual modalities or technologies. In this work, we propose a low-cost multimodal system that combines 3D freehand ultrasound with fringe projection profilometry to obtain information from the external and the internal structure of an object of interest. Both modalities are referred to a single coordinate system defined in the calibration to avoid post-processing and registration of the acquired images. The freehand ultrasound calibration results are similar to those previously reported in the literature using more expensive infrared tracking systems. The calibration reproducibility at the center point of the ultrasound image was 0.6202 mm for 8 independent calibrations. We tested our system on a breast phantom with tumors. Encouraging results show the potential of the system for applications in intraoperative settings.
Analysis and classification of lung and muscular tissues in ultrasound images using 2D wavelet transform and machine learning
(2020-11-03) Contreras Ojeda, Sara; Domínguez Jiménez, Juan Antonio; Contreras Ortiz, Sonia Helena
Ultrasound has been considered a safe and accurate alternative to radiography and computerized tomography to diagnose lung diseases such as pneumonia. However, speckle noise, artifacts or certain conditions can difficult image interpretation. For example, in some cases, the pleura line cannot be observed. This work proposes an approach for discriminating between lung and muscular tissues in ultrasound images. We evaluated the symlet and daubechies wavelets for feature extraction, principal component analysis and recursive backward elimination for feature selection, and supervised learning methods for classification. Statistical moments and the energy of the second horizontal coefficient and peak-to-peak root mean squared ratio were the features more outstanding over the rest. The best model was obtained with recursive backward elimination for feature selection and knearest neighbor for classification. Tissue classification was possible with a mean accuracy of 97.5% and area under the curve of 99%. These results offer great insights on the recognition of lung and muscular tissues, which could improve the effectiveness of automatic segmentation and analysis algorithms.
Analysis and classification of lung tissue in ultrasound images for pneumonia detection
(2020-01-03) Valdes-Burgos, L.; Contreras Ojeda, Sara; Domínguez Jiménez, Juan Antonio; López-Bueno J.; Contreras Ortiz, Sonia Helena
Pneumonia is an infection of the lungs caused by virus, bacteria or fungi. It affects mainly children under five and can be life-threatening. Diagnosis of pneumonia is usually performed using imaging techniques such as chest radiography, ultrasound, and CT. Several studies have shown that ultrasound is an effective, safe and cost-efficient technique for pneumonia detection. However, due to the low signal-to-noise ratio of the images, this technique is highly dependent on the experience of the practitioner. This paper proposes an approach for pneumonia detection from image texture features. We used empirical mode decomposition for feature extraction, principal component analysis for dimensionality reduction and supervised learning methods for classification. Results show that features of the first mode present large differences between healthy and pneumonia patients according to the Cohen’s d index. Pneumonia detection was possible with a rotation forest model with a mean accuracy of 83.33%.
Texture Analysis of Ultrasound Images for Pneumonia Detection in Pediatric Patients
(Institute of Electrical and Electronics Engineers Inc., 2019) Contreras Ojeda, Sara; Sierra-Pardo C.; Domínguez Jiménez, Juan Antonio; López-Bueno J.; Contreras Ortiz, Sonia Helena
Pneumonia is a condition that can be life-threatening and affects a high number of children around the world. Lung ultrasound can be used for the diagnosis of pneumonia, but requires high experience. This paper presents an approach for pneumonia detection based on texture analysis of ultrasound images. Several measures were taken in healthy tissues and pneumonia lesions, and the most significant features were identified by statistical analysis. The results of the analysis of variance and exploratory analysis suggest that detection of pneumonia is possible based on image texture features. © 2019 IEEE.