Danilo Jodas - Academia.edu (original) (raw)

Papers by Danilo Jodas

VipIMAGE 2017, 2017

The segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images of carotid arteri... more The segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images of carotid arteries represents a crucial step towards the evaluation of cerebrovascular diseases. However, the automatic segmentation of the lumen is still difficult due to the usual low quality of the images and the presence of elements that might affect the accuracy of the results. In this article, we describe a fully automatic method to identify the location of the lumen in MR images of the carotid artery. A circularity index is used to assess the roundness of the regions identified by the K-means algorithm in order to obtain the one with the maximum value, which represents the potential lumen region. The boundary of the identified lumen region is then refined by an active contour algorithm. The proposed method achieved a maximum Dice coefficient of 0.91 ± 0.04 and 0.74 ± 0.16 in 181 postcontrast 3D-T1-weighted and 181 proton density-weighted MR images, respectively. Therefore, the method seems to be promising for identifying the correct location of the lumen in MR images.

The composition of atherosclerotic plaques in the carotid artery plays an important key to determ... more The composition of atherosclerotic plaques in the carotid artery plays an important key to determine their evolution and, consequently, the risk of reduction/obstruction of the blood flow through the artery. Computational algorithms have been proposed in many studies to segment, i.e. to identify [1], and assess atherosclerotic plaques and their main components in images [2]. The use of such algorithms allows, for example, the evaluation more efficiently of the risk to cerebral events. The aim of this work was the development of an algorithm to segment atherosclerotic plaques and the related main components in MR images of the carotid artery, which has two main steps: 1) segmentation of the lumen and arterial walls and 2) further segmentation of the atherosclerotic plaques. The first step provides the region containing the atherosclerotic plaques, i.e. the region between the lumen and arterial wall boundaries. In this step, the K-means algorithm is used to obtain the regions comprise...

Computers in Biology and Medicine, 2020

Segmentation methods have assumed an important role in image-based diagnosis of several cardiovas... more Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the centre of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 ± 0.11, 2.70 ± 1.69 pixels, 2.79 ± 1.89 pixels and 3.44 ± 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.

Neural Computing and Applications, 2019

The identification of atherosclerotic plaque components, extraction and analysis of their morphol... more The identification of atherosclerotic plaque components, extraction and analysis of their morphology represent an important role towards the prediction of cardiovascular events. In this article, the classification of regions representing calcified components in Computed Tomography Angiography (CTA) images of the carotid artery is tackle. The proposed classification model has two main steps: the classification per pixel and the classification per region. Features extracted from each pixel inside the carotid artery are submitted to four classifiers in order to determine the correct class, i.e. calcification or non-calcification. Then, geometrical and intensity features extracted from each candidate region resulting from the pixel classification step are submitted to the classification per region in order to determine the correct regions of calcified components. In order to evaluate the classification accuracy, the results of the proposed classification model were compared against ground truths of calcifications obtained from micro Computed Tomography images of excised atherosclerotic plaques that were registered with in vivo

Computers in biology and medicine, Dec 1, 2016

Investigation of the carotid artery plays an important role in the diagnosis of cerebrovascular e... more Investigation of the carotid artery plays an important role in the diagnosis of cerebrovascular events. Segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images is the first step towards evaluating any possible cardiovascular diseases like atherosclerosis. However, the automatic segmentation of the lumen is still a challenge due to the low quality of the images and the presence of other elements such as stenosis and malformations that compromise the accuracy of the results. In this article, a method to identify the location of the lumen without user interaction is presented. The proposed method uses the modified mean roundness to calculate the circularity index of the regions identified by the K-means algorithm and return the one with the maximum value, i.e. the potential lumen region. Then, an active contour is employed to refine the boundary of this region. The method achieved an average Dice coefficient of 0.78±0.14 and 0.61±0.21 in 181 3D-T1-weighted and 181 p...

Medical image analysis, 2017

Image assessment of the arterial system plays an important role in the diagnosis of cardiovascula... more Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two exper...

Expert Systems with Applications, 2016

Evaluation of the composition of atherosclerotic plaques in images is an important task to determ... more Evaluation of the composition of atherosclerotic plaques in images is an important task to determine their pathophysiology. Visual analysis is still as the most basic and often approach to determine the morphology of the atherosclerotic plaques. In addition, computer-aided methods have also been developed

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012

ABSTRACT The use of mobile robots turns out to be interesting in activities where the action of h... more ABSTRACT The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route.

Procedia Computer Science, 2013

The use of mobile robots turns out to be interesting in activities where the action of human spec... more The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route.

Optimum-Path Forest, 2022

Devices, Circuits and …, Jan 1, 2004