Browsing by Author "Medina Molina, Ruben"

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Cardiac Motion Estimation in Magnetic Resonance Images Using Optical Flow
(IEEE COMPUTER SOCIETY, 2016-06-01) Medina Molina, Ruben; Morocho Zurita, Carlos Villie; Vanegas Peralta, Pablo Fernando
This paper reports an optical based method for quantification of cardiac motion in MRI images. The cardiac motion is quantified in a Short-Axis (SAX) slice located at the mid-cavity of the left ventricle. In this slice, the left ventricle wall is segmented for extracting the endocardium, the epicardium and the midwall. The velocity field for these contours is available after performing the optical flow estimation for the given slice. Post-processing of this motion field enables estimation of the radial displacement. The core of this cardiac motion quantification method is a sparse based algorithm for optical flow estimation. This algorithm is presented and compared with several classical optical flow estimation algorithms. The comparison is performed using several video sequences available from the Middlebury benchmark where the ground truth optical flow is known. The algorithms with better performance are also tested using a 4 D cardiac Magnetic Resonance Image (MRI) sequence. Results show that he sparse algorithm estimates an optical flow field that represents the motion of contraction during the systole interval. Moreover, results about radial displacement estimation on real MRI sequences for a normal subject and a patient with hypertrophy show that the proposed quantification method could be useful for quantification of left ventricle motion.
Characterizing artifacts in RR stress test time series
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-16) Astudillo Salinas, Darwin Fabián; Medina Molina, Ruben; Palacio Baus, Kenneth Samuel; Solano Quinde, Lizandro Damián; Wong De Balzan, Sara
Electrocardiographic stress test records have a lot of artifacts. In this paper we explore a simple method to characterize the amount of artifacts present in unprocessed RR stress test time series. Four time series classes were defined: Very good lead, Good lead, Low quality lead and Useless lead. 65 ECG, 8 lead, records of stress test series were analyzed. Firstly, RR-time series were annotated by two experts. The automatic methodology is based on dividing the RR-time series in non-overlapping windows. Each window is marked as noisy whenever it exceeds an established standard deviation threshold (SDT). Series are classified according to the percentage of windows that exceeds a given value, based upon the first manual annotation. Different SDT were explored. Results show that SDT close to 20% (as a percentage of the mean) provides the best results. The coincidence between annotators classification is 70.77% whereas, the coincidence between the second annotator and the automatic method providing the best matches is larger than 63%. Leads classified as Very good leads and Good leads could be combined to improve automatic heartbeat labeling.
CinC Challenge 2013: Comparing three algorithms to extract fetal ECG
(SPIE, 2015-11-17) Loja, J; Astudillo Salinas, Darwin Fabián; Medina Molina, Ruben; Palacio Baus, Kenneth Samuel; Velecela, E; Wong De Balzan, Sara
This paper reports a comparison between three fetal ECG (fECG) detectors developed during the CinC 2013 challenge for fECG detection. Algorithm A1 is based on Independent Component Analysis, A2 is based on fECG detection of RS Slope and A3 is based on Expectation-Weighted Estimation of Fiducial Points. The proposed methodology was validated using the annotated database available for the challenge. Each detector was characterized in terms of its performance by using measures of sensitivity, (Se), positive predictive value (P+) and delay time (td). Additionally, the database was contaminated with white noise for two SNR conditions. Decision fusion was tested considering the most common types of combination of detectors. Results show that the decision fusion of A1 and A2 improves fQRS detection, maintaining high Se and P+ even under low SNR conditions without a significant tdincrease.
Hepatic Steatosis detection using the co-occurrence matrix in tomography and ultrasound images
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2015-09-02) Medina Molina, Ruben; Morocho Zurita, Carlos Villie; Vanegas Peralta, Pablo Fernando
Hepatic Steatosis (HS) or Fatty Liver is a disease due to fat accumulation within hepatocytes. This disease requires treatment to avoid clinical complications such as hepatic inflammation, fibrosis and finally chronic hepatic damage and hepatic carcinoma. An algorithm for performing the manual segmentation was used. A polygon is traced for representing the region of interest in tomography (CT) images as well as in Ultrasound (US) images. These regions are then subdivided in a set of windows of size 4×4. For each of the windows the co-occurrence matrix is estimated as well as several descriptive statistical parameters. From these matrices, 9 descriptive statistical parameters were estimated. A Binary Logistic Regression (BLR) model was fitted considering as dependent variable the presence or absence of the disease and the descriptive statistical parameters as predictor variables. The model attains classification results of HS with a sensibility of 95.45% in US images and 93.75% in CT images in the venous phase.
Level set algorithms comparison for multi-slice CT left ventricle segmentation
(SPIE, 2015-11-17) Medina Molina, Ruben; La Cruz Puente Alexandra; Morocho Zurita, Carlos Villie; Ordoñes, A; Pesántez, D; Vanegas Peralta, Pablo Fernando
The comparison of several Level Set algorithms is performed with respect to 2D left ventricle segmentation in Multi-Slice CT images. Five algorithms are compared by calculating the Dice coefficient between the resulting segmentation contour and a reference contour traced by a cardiologist. The algorithms are also tested on images contaminated with Gaussian noise for several values of PSNR. Additionally an algorithm for providing the initialization shape is proposed. This algorithm is based on a combination of mathematical morphology tools with watershed and region growing algorithms. Results on the set of test images are promising and suggest the extension to 3{D MSCT database segmentation.
Mobile teleradiology system suitable for m-health services supporting content and semantic based image retrieval on a grid infrastructure
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-16) La Cruz Puente Alexandra; Espinoza Mejía, Jorge Mauricio; Medina Molina, Ruben; Pérez Rocano, Wilson Rodrigo; Saquicela Galarza, Víctor Hugo; Solano Quinde, Lizandro Damián; Vega, F; Vidal, M.-E
Teleradiology systems tackle the problem of transferring radiological images between medical image workstations for facilitating different medical activities, e.g., diagnosis, treatment and follow up a patient, medical training, or consulting second opinion. Nowadays, m-Health (aka mobile health) is becoming popular because of high quality of mobile displays, although remains a work in progress. In this paper a mobile teleradiology system is reported, which main contribution is the development of a platform: (1) supported by a Grid infrastructure, (2) using biomedical ontologies for adding semantic annotations on medical images, and (3) supporting semantic and content-based image retrieval. Images are located physically in different repositories like; hospitals and diagnostic imaging centers. All these features make the system ubiquitous, portable, and suitable for m-Health services.
Open source cardiology electronic health record development for DIGICARDIAC implementation
(SPIE, 2015-11-17) Medina Molina, Ruben; Rojas Reyes, Rosendo Iván; Huiracocha Tutivén, María de Lourdes
This article presents the development of a Cardiology Electronic Health Record (CEHR) system. Software consists of a structured algorithm designed under Health Level-7 (HL7) international standards. Novelty of the system is the integration of high resolution ECG (HRECG) signal acquisition and processing tools, patient information management tools and telecardiology tools. Acquisition tools are for management and control of the DIGICARDIAC electrocardiograph functions. Processing tools allow management of HRECG signal analysis searching for indicative patterns of cardiovascular pathologies. Telecardiology tools incorporation allows system communication with other health care centers decreasing access time to the patient information. CEHR system was completely developed using open source software. Preliminary results of process validation showed the system efficiency.
Optical Flow as a Tool for Cardiac Motion Estimation
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2015-07-14) Medina Molina, Ruben; Morocho Zurita, Carlos Villie; Vanegas Peralta, Pablo Fernando
A sparse based algorithm for optical flow estimation is presented and compared with several classical optical flow estimation algorithms. The comparison is performed using several video sequences available from the Middlebury benchmark where the ground truth optical flow is known. The sparse algorithm attains competitive results with average angular errors as low as 2.09° and average magnitude errors as low as 0.100. The algorithms are also tested using a 4 - D cardiac Magnetic Resonance Image (MRI) sequence. The sparse algorithm estimates an optical flow field that represents the motion of contraction during the systole interval.
Semiautomatic validation of RR time series in an ECG stress test database
(SPIE, 2015-11-17) Armijos, J; Astudillo Salinas, Darwin Fabián; Garciá, D; Medina Molina, Ruben; Palacio Baus, Kenneth Samuel; Wong De Balzan, Sara
This paper reports an automatic method for characterizing the quality of the RR-time series in the stress test database known as DICARDIA. The proposed methodology is simple and consists in subdividing the RR time series in a set of windows for estimating the quantity of artifacts based on a threshold value that depends on the standard deviation of RR-time series for each recorded lead. In a first stage, a manual annotation was performed considering four quality classes for the RR-time series (Reference lead, Good Lead, Low Quality Lead and Useless Lead). Automatic annotation was then performed varying the number of windows and threshold value for the standard deviation of the RR-time series. The metric used for evaluating the quality of the annotation was the Matching Ratio. The best results were obtained using a higher number of windows and considering only three classes (Good Lead, Low Quality Lead and Useless). The proposed methodology allows the utilization of the online available DICARDIA Stress Test database for different types of research.
Video and imaging gastroenterological medical equipment oriented to telemedicine
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-30) Molina, J; Bartolome, M; La Cruz Puente Alexandra; Medina Molina, Ruben; Morocho Zurita, Carlos Villie
This paper describes a system for management of clinical information in gastroenterology. The system consists of two blocks (hardware and software), both developed during the investigation. The hardware interface is connected to endoscopy equipment for video and image acquisition. Then, the patient electronic health record (EHR) is created using a software designed for including the relevant images and video sequences selected by the medical staff. The patient EHR is stored locally as well as in a remote server where authorized users can review and eventually edit the information within a telemedicine protocol.