Ultrasound (US) images are necessary in obstetrics because they provide the most important clinical parameters for fetal health assessment during the second and third trimesters: head circumference, biparietal diameter, abdominal circumference and femur length. These fetometric indices are helpful for gestational age and fetal weight estimation; they are also helpful for obstetricians to diagnose fetal development abnormalities. However, these indices are obtained manually, which provokes high intra and interobserver variability and lack of repeatability. A fully automatic method to segment and measure femur’s length is presented in this paper. The proposed methodology incorporates texture information and introduces a novel curvature analysis to adequately detect the femur. It consists on pre–processing US images with an anisotropic diffusion filter, followed by morphological operations and thresholding to isolate femur–candidate regions. A normalized metric composed of intensity, length, centroid position and entropy is assigned to each region in order to select the most probable candidate to be femur. This selected region is afterwards thinned to a one–pixel line, whose curvature is analyzed with an angle threshold criterion to accurately locate femur’s extrema. The method was tested on 64 US images (20 taken on the second and 44 on the third trimester of pregnancy); a correlation coefficient of 0.984 and an error of 1.016±2.764 mm were achieved between expert–obtained manual measures and automatically calculated indices. Results are consistent, outperform those reported previously by other authors and show a high correlation with measures obtained by experts; therefore, the developed method is suitable to be adapted for clinical use.
An indirect method of tissue consistency measurement is proposed, based on intensity and texture features of conventional ultrasound (US) cervix images. Calibration and validation were carried out in five phantoms simulating different cervical firmness, as well as in short and long cervices. Several image features attributed to the histogram, the co–occurrence matrix and the run–length encoding matrix were extracted and analyzed to evaluate their ability to distinguish between degrees of phantoms’ firmness. The most indicative of firmness indices were selected by correlating their values with the phantoms’ elasticities determined through Young’s moduli. Also, a random forest classifier was implemented, allowing to identify the features that contribute the most to class separation between phantoms. Using both tests, six features were selected: mean, standard deviation, entropy, skewness and two RLE-matrix features. A 6–fold cross validation was used to evaluate the model, obtaining a 98.9±0.79% accuracy. Finally, a preliminary case study was conducted upon closed and opened cervical US images, classifying them between both groups using a random forest model, obtaining an 84.34% accuracy. The indicated tests show that intensity and texture features extracted from conventional US images provide indirect and less–invasive information than other methods regarding tissue consistency, and therefore may be used to measure changes in cervical firmness.
In this work we present a combination of segmentation and motion estimation methods applied to left ventricle evaluation in fetal echocardiographic images which are used for prenatal diagnosis. In our proposed scheme, several features of the ultrasound images are computed and used for both algorithms. A multiresolution framework is employed for the segmentation and motion estimation tasks. The segmentation is achieved using a multi-texture active appearance model based on the Hermite transform. The analysis is performed using the appearance models provided by Hermite coefficients up to third order. The multiresolution approach allows to obtain a robust segmentation to extract the shape of the left ventricle. The obtained results in the segmentation step are used for the motion estimation algorithm. The left ventricle is the structure used for evaluation. The main goal is to determinate the heart movement of fetal heart which can be used for disease detection, characterization and further analysis. Results of the motion estimation process are analyzed and compared with other techniques applied to heart ultrasound data.
Abdominal electrocardiography (AECG) is an indirect method for obtaining a continuous reading of fetal heart rate and is widely used during pregnancy as a method for assessing fetal well-being. Information obtained by AECG is used for early identification of fetal risk and may help in the anticipation of future complications; however, improper interpretation of the AECG recordings, related with inter- and intra-individual variability, may lead to inadequate treatments that can cause the death of the fetus. A set of 33 indices (4 maternal, 5 temporals, 23 time-frequency and 1 non-linear), extracted from AECG recordings and maternal information, were tested with a Random Forest (RF) classification method for the identification of normal fetuses and fetuses with intrauterine growth restriction. Because RFs may perform poorly when confronted with a high number of features compared to the number of training data available, a Genetic Algorithm (GA) was used to select the minimum set of features that improves the outcome of the RF. The accuracy of the RF method using the 33 indices was of 60%. After a run of the GA, the best individual in the last generation had an accuracy value of 85% and reduced the number of used indices from 33 to 11.
The thickness of the nuchal fold is one of the main markers for the detection of Down syndrome during the second trimester of pregnancy. In this paper are reported our preliminary results of the automatic segmentation and measurement of the nuchal fold thickness in ultrasound images of the fetal brain. The method is based on a 2D active shape model used to segment the brain structures involved in the measurement of the nuchal fold: cerebellum; brain midline; the outer edge of the occipital plate; and the outer skin edge. The algorithm was trained and tested in 10 different ultrasound images, using leave one out cross validation. We have obtained an average difference of 0.23 mm from the expert measurement of the nuchal fold, with a standard deviation of 0.1 mm.
During the first trimester of pregnancy fetal health assessment is especially important. In the clinical practice, the gestational sac (GS) volume estimation is manually done using a tedious procedure which is prone to physicians' subjectivity. The method proposed in this paper consists on a semiautomatic delimitation of the GS and a segmentation of its content with minimal expert intervention. It is based on spreading active contours (SAC), following a planimetric strategy to define the GS' edges. Additionally, an optimal thresholding method was used to separate solid matter and amniotic fluid. The comparison between manual GS segmentations and those obtained with the proposed SAC method, shows Dice similarities of 90% and a mean Hausdor distance of 5.63 ± 1.94 mm, while the correlation index between SAC and the clinical reference (VOCAL) is 0.997. However, with statistical tests (t-paired) a value of p < 0.05 was obtained, which suggests a difference in the measured volume by the compared methods. The proposed method (SAC) has shown to be reliable, besides of being easy to implement.
Three dimensional ultrasound imaging has become the main modality for fetal health diagnostics, with extensive use in fetal brain imaging. According to the fetal position and the stage of development of the fetal skull, a specific plane of image acquisition is required. In most cases for a single plane of acquisition, the image quality is limited by the shadows produced by the skull. In this work a new method for registration of multiple views of 3D ultrasound of the fetal brain is reported, which results in improved imaging of the internal brain structures. In the initial stage, texture, intensity and edge features are used, with a support vector machine (SVM) for the segmentation of the skull in each of the 3D ultrasound views to be registered. The segmentation of each skull is modelled as a set of points with the centre determined with a Gaussian mixture model, where each point is assigned a probability of membership to a Gaussian determined by the posterior probability assigned by the SVM. Our method has shown improved results compared to intensity based registration, with a 52% reduction in the target registration error (TRE), and a 39% reduction in the TRE compared to feature based registration. These are encouraging results for the future development of an automatic method for registration and fusion of multiple views of 3D fetal ultrasound.
In this work we present a segmentation framework applied to fetal cardiac images. One of the main problems of the segmentation in ultrasound images is the speckle pattern that makes difficult to model images features such as edges and homogeneous regions. Our approach is based on two main processes. The first one aims at enhancing the ultrasound image using a noise reduction scheme. The Hermite transform is used for this purpose. In the second process a Point Distribution Model (PDM), previously trained, is used for the segmentation of the desired object. The filtering process is then employed before the segmentation stage with the aim of improving the results. The obtained result in the filtering process is used as a way to make more robust the segmentation stage. We evaluate the proposed method in the segmentation of the left ventricle of fetal ultrasound data. Different metrics are used to validate and compare the performance with other methods applied to fetal echocardiographic images.
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