José Silvestre Silva

Phase Symmetry Approach Applied to Children Heart Chambers Segmentation: A Comparative Study

Segmentation of echocardiographic images presents a great challenge because these images contain strong speckle noise and artifacts. Besides, most ultrasound segmentation methods are semiautomatic, requiring initial contour to be manually identified in the images. In this paper, we propose an algorithm based on the phase symmetry approach and level set evolution, in order to extract simultaneously all heart cavities in a fully automatic way. The level set evolution uses a new logarithmic-based stopping function, which demonstrates to perform well in the boundary extraction. We compared our method with other level set approaches, the watershed technique, and the manual segmentation made by two physicians. The experimental work was based on echocardiography images of children. Similarity metrics, namely Pratt function, pixel mean error, and similarity angle have been used for the performance evaluation of the different methods. The results indicate that our method has a performance of at least 4% superior to the other methods able to segment the four chambers. Even for the two worst boundary extraction cases (right ventricle and left atrium), the performance of the proposed method is still better than the other techniques.

Image Denoising Methods for Tumor Discrimination in High-Resolution Computed Tomography

Pixel accuracy in images from high-resolution computed tomography (HRCT) is ultimately limited by reconstruction error and noise. While for visual analysis this may not be relevant, for computer-aided quantitative analysis in either densitometric, or shape studies aiming at accurate results, the impact of pixel uncertainty must be taken into consideration. In this work, we study several denoising methods: geometric mean filter, Wiener filtering, and wavelet denoising. The performance of each method was assessed through visual inspection, profile region intensity analysis, and global figures of merit, using images from brain and thoracic phantoms, as well as several real thoracic HRCT images.

A method for corneal nerves automatic segmentation and morphometric analysis

The segmentation and morphometric analysis of corneal sub-basal nerves, from corneal confocal microscopy images, has gained recently an increased interest. This interest arises from the possibility of using changes in these nerves as the basis of a simple and non-invasive method for early detection and follow-up of peripheral diabetic neuropathy, a major cause of chronic disability in diabetic patients. Here, we propose one method for automatic segmentation and analysis of corneal nerves from images obtained in vivo through corneal confocal microscopy. The method is capable of segmenting corneal nerves, with sensitivity near 90% and a percentage of false recognitions with an average of 5.3%. The nerves tortuosity was calculated and shows statistically significant differences between healthy controls and diabetic individuals, in accordance to what is reported in the literature.

A level set segmentation method of the four heart cavities in pediatric ultrasound images

Echocardiography is the most used medical imaging in pediatric cardiology. It is a fundamental tool to analyze the major heart disease and abnormalities since it is non invasive and simple to use for physicians even when the children are wiggle. Ultrasound images are very noisy, making the segmentation a difficult, not accurate and time consuming task. In this work we propose an automatic segmentation method to extract the four heart cavity boundaries using a new pre-processing algorithm, based on phase symmetry. Experimental results using real echocardiographic images of children show good performance of the proposed method, providing a reliable tool to segment the heart walls that can be helpful for clinical practice.

Algorithm Versus Physicians Variability Evaluation in the Cardiac Chambers Extraction

Congenital heart diseases are present in eight of every 1000 newborns. The diagnosis of those pathologies usually depends on the available imaging methods. A correct diagnosis requires a detailed observation of the heart chambers, wall motions, valves function, and quantitative evaluation of the cavity volumes. For that goal numerous automatic algorithms have been proposed to segment the echocardiographic images. In this paper, the authors evaluate the performance of a level set algorithm based on the phase symmetry approach and on a new logarithmic-based stopping function to extract the heart cavity contours simultaneously, and in a fully automatic way. The extracted cardiac borders are then statistically compared with the ones manually sketched by four physicians on a set of 240 cavities. Nonparametric statistical tests are conducted on the data using several figures of merit, in order to study the inter- and intraobserver variabilities among the four physicians and the level set algorithm, concerning to the extracted contours. The results show there is a great concordance about all the used similarity indexes. A higher interobserver variability was found among the physicians than the variability obtained when the algorithm versus physician performance is compared. The statistical analysis suggests the proposed algorithm produces results similar to the ones provided by the physicians.

Fast volumetric registration method for tumor follow-up in pulmonary CT exams.

An oncological patient may go through several tomographic acquisitions during a period of time, needing an appropriate registration. We propose an automatic volumetric intrapatient registration method for tumor follow‐up in pulmonary CT exams. The performance of our method is evaluated and compared with other registration methods based on optimization techniques. We also compared the metrics behavior to inspect which metric is more sensitive to changes due to the presence of lung tumors. PACS numbers: 87.57.nj; 87.57.Q‐; 87.57.N‐

Detection and 3D representation of pulmonary air bubbles in HRCT volumes

Bubble emphysema is a disease characterized by the presence of air bubbles within the lungs. With the purpose of identifying pulmonary air bubbles, two alternative methods were developed, using High Resolution Computer Tomography (HRCT) exams. The search volume is confined to the pulmonary volume through a previously developed pulmonary contour detection algorithm. The first detection method follows a slice by slice approach and uses selection criteria based on the Hounsfield levels, dimensions, shape and localization of the bubbles. Candidate regions that do not exhibit axial coherence along at least two sections are excluded. Intermediate sections are interpolated for a more realistic representation of lungs and bubbles. The second detection method, after the pulmonary volume delimitation, follows a fully 3D approach. A global threshold is applied to the entire lung volume returning candidate regions. 3D morphologic operators are used to remove spurious structures and to circumscribe the bubbles. Bubble representation is accomplished by two alternative methods. The first generates bubble surfaces based on the voxel volumes previously detected; the second method assumes that bubbles are approximately spherical. In order to obtain better 3D representations, fits super-quadrics to bubble volume. The fitting process is based on non-linear least squares optimization method, where a super-quadric is adapted to a regular grid of points defined on each bubble. All methods were applied to real and semi-synthetical data where artificial and randomly deformed bubbles were embedded in the interior of healthy lungs. Quantitative results regarding bubble geometric features are either similar to a priori known values used in simulation tests, or indicate clinically acceptable dimensions and locations when dealing with real data.

Comparison of a segmentation algorithm to six expert imagiologists in detecting pulmonary contours on x-ray CT images

Quantitative evaluation of the performance of segmentation algorithms on medical images is crucial before their clinical use can be considered. We have quantitatively compared the contours obtained by a pulmonary segmentation algorithm to contours manually-drawn by six expert imaiologists on the same set of images, since the ground truth is unknown. Two types of variability (inter-observer and intra-observer) should be taken into account in the performance evaluation of segmentation algorithms and several methods to do it have been proposed. This paper describes the quantitative evaluation of the performance of our segmentation algorithm using several figures of merit, exploratory and multivariate data analysis and non parametric tests, based on the assessment of the inter-observer variability of six expert imagiologists from three different hospitals and the intra-observer variability of two expert imagiologists from the same hospital. As an overall result of this comparison we were able to claim that the consistency and accuracy of our pulmonary segmentation algorithm is adequate for most of the quantitative requirements mentioned by the imagiologists. We also believe that the methodology used to evaluate the performance of our algorithm is general enough to be applicable to many other segmentation problems on medical images.

Fast pulmonary contour extraction in x-ray CT images: a methodology and quality assessment

Segmentation of thoracic X-Ray Computed Tomography images is a mandatory pre-processing step in many automated or semi- automated analysis tasks such us region identification, densitometric analysis, or even for 3D visualization purposes when a stack of slices has to be prepared for surface or volume rendering. In this work, we present a fully automated and fast method for pulmonary contour extraction and region identification. Our method combines adaptive intensity discrimination, geometrical feature estimation and morphological processing resulting into a fast and flexible algorithm. A complementary but not less important objective of this work consisted on a quality assessment study of the developed contour detection technique. The automatically extracted contours were statistically compared to manually drawn pulmonary outlines provided by two radiologists. Exploratory data analysis and non-parametric statistical tests were performed on the results obtained using several figures of merit. Results indicate that, besides a strong consistence among all the quality indexes, there is a wider inter-observer variability concerning both radiologists than the variability of our algorithm when compared to each one of the radiologists. As an overall conclusion we claim that the consistence and accuracy of our detection method is more than acceptable for most of the quantitative requirements mentioned by the radiologists.

Automatic corneal nerves recognition for earlier diagnosis and follow-up of diabetic neuropathy

Peripheral diabetic neuropathy is a major cause of chronic disability in diabetic patients. Morphometric parameters of corneal nerves may be the basis of an ideal method for early diagnosis and assessment of diabetic neuropathy. We developed a fully automatic algorithm for corneal nerve segmentation and morphometric parameters extraction. Luminosity equalization was done using local methods. Images structures were enhanced through phase-shift analysis, followed by Hessian matrix computation for structure classification. Nerves were then reconstructed using morphological methods. The algorithm was evaluated using 10 images of corneal nerves, by comparing with manual tracking. The average percent of nerve correctly segmented was 88.5% ± 7.2%. The percent of false nerve segments was 3.9% ± 2.2%. The average difference between automatic and manual nerve lengths was -28.0 ± 30.3 μm. Running times were around 3 minutes. The algorithm produced good results similar to those reported in the literature.