A computer-aided method based on geometrical texture features for a precocious detection of fetal Hydrocephalus in ultrasound images

Abstract

BACKGROUD: Hydrocephalus is the most common anomaly of the fetal head characterized by an excessive accumulation of fluid in the brain processing. The diagnostic process of fetal heads using traditional evaluation techniques are generally time consuming and error prone. Usually, fetal head size is computed using an ultrasound (US) image around 20–22 weeks, which is the gestational age (GA). Biometrical measurements are extracted and compared with ground truth charts to identify normal or abnormal growth. METHODS: In this paper, an attempt has been made to enhance the Hydrocephalus characterization process by extracting other geometrical and textural features to design an efficient recognition system. The superiority of this work consists of the reduced time processing and the complexity of standard automatic approaches for routine examination. This proposed method requires practical insidiousness of the precocious discovery of fetuses’ malformation to alert the experts about the existence of abnormal outcome. The first task is devoted to a proposed pre-processing model using a standard filtering and a segmentation scheme using a modified Hough transform (MHT) to detect the region of interest. Indeed, the obtained clinical parameters are presented to the principal component analysis (PCA) model in order to obtain a reduced number of measures which are employed in the classification stage. RESULTS: Thanks to the combination of geometrical and statistical features, the classification process provided an important ability and an interesting performance achieving more than 96% of accuracy to detect pathological subjects in premature ages. CONCLUSIONS: The experimental results illustrate the success and the accuracy of the proposed classification method for a factual diagnostic of fetal head malformation.

Improving Parotid Gland Tumor Segmentation and Classification Using Geometric Active Contour Model and Deep Neural Network Framework

“…Several classification methods can be employed for pattern recognition. Nevertheless, for multiple categorization works [32,33], it has been confirmed that DNN presents a highquality tool. To acquire authentic classification results, we have selected the PG region of different images for assembling the training dataset of the network.…”

Fear Facial Emotion Recognition Based on Angular Deviation

“…Numerous classifiers can be used for pattern acknowledgement. It has been substantiated that the neural network method is an efficient tool for different classification works [25]. We have selected the most discriminative characteristics to prepare the training dataset of the network.…”

“…PCA consists in finding a subspace allowing us to classify the new components in order of decreasing importance in order to reduce the dimension of the original space. The transition matrix of the new space is obtained by maximizing the variance between the original variables [25,26]:…”

“…PCA consists in finding a subspace allowing us to classify the new components in order of decreasing importance in order to reduce the dimension of the original space. The transition matrix of the new space is obtained by maximizing the variance between the original variables [25,26]: where S x = Var(X) is the covariance matrix of the original components and is the set of principal axes that define the new space. The following table summarizes the main steps of the PCA Algorithm 1.…”

ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals

“…To secure a reasonable judgment amid the three tested neural network methods, the same network structure was trained for each one in different experiments based on suitable features. The projected ConvNet depends on advanced algorithms for specifying the best network structure [33]. In this context, the cross-validation procedure was employed to choose the relevant ConvNet structure in the training and test phases of ECG signal datasets [24].…”