Villie Morocho

Hepatic Steatosis detection using the co-occurrence matrix in tomography and ultrasound images

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.

Optical Flow as a Tool for Cardiac Motion Estimation

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.

Level set algorithms comparison for multi-slice CT left ventricle segmentation

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.

Left ventricle myocardium segmentation in multi-slice computerized tomography

This paper briefly describes a left ventricle myocardium segmentation method in multi-slice computerized tomography images. The segmentation technique is based on level-sets deformable contours. The proposed method has two stages: in the first stage the left ventricle internal wall or endocardium is segmented. In the second stage the external wall is segmented starting with an initialization based on the endocardial segmented shape. This algorithm is intended for incorporation within a software platform for visualization and processing of cardiac images. This platform would help the diagnosis of the left ventricle hypertrophy (LVH).

Cardiac Motion Estimation in Magnetic Resonance Images Using Optical Flow

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 mid-wall. 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 the 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.

Video and imaging gastroenterological medical equipment oriented to telemedicine

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.

Phenomenological modelling and computer simulation of a clinker kiln

This article uses a two-dimensional phenomenological model representing the dynamic behaviour of a clinker kiln, and includes another model to estimate the height of the solid material (crude) inside the kiln. These models are parameterized according to design specifications and operating conditions of a real industrial kiln, which belongs to a cement production plant. The whole model consists of 16 partial differential equations, one ordinary nonlinear differential equation and a set of algebraic equations, and is computationally simulated using the finite elements method. The model allows analyzing temperature profiles in the kiln, as well as qualitative characteristics associated with fractions of final components in the clinker production.

Analysis of Thermographic Patterns using Open CV

The core of the cement production process is the clinker kiln. Proper operation of the kiln depends on factors such as the timely monitoring of its thermal behavior under different operation conditions. This work includes a systematization of empirical knowledge of skilled kiln operators, linking it with the analysis of thermography images of the kiln using Open CV. The paper includes an integration of interventions implemented by the operators, in terms of a log described in natural language. The work highlights potential uses of the knowledge of experienced operators, when this is combined with techniques based on image analysis and artificial intelligence.

Design of a Supervisory Control System for a Clinker Kiln Operation

The Clinker kiln is the core of the cement production process, which is a heavy infrastructure, where a mixture of limestone and clay undergoes physical and chemical transformations at high temperatures. The process has a very complex non-linear behavior, which hinders its control automatically. This article presents the design of a fuzzy logic-based supervisory system for control tasks upon the Clinker kiln operation [1]. The proposed supervisory system, which has been tested offline in an industrial kiln, uses process variables as the temperature in the burning area, the percentage of oxygen and the back-end temperature, to take appropriate control actions to face operating kiln conditions. The considered control variables in the design are the fuel flow to the burner, the air fan speed and the kiln rotational speed. As a result, the supervisory control system tests showed an improvement in kiln stability and clinker production.