Enhanced fingerprint classification through modified PCA with SVD and invariant moments.

This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.

On designing a configurable UAV autopilot for unmanned quadrotors.

Unmanned Aerial Vehicles (UAVs) and quadrotors are being used in an increasing number of applications. The detection and management of forest fires is continually improved by the incorporation of new economical technologies in order to prevent ecological degradation and disasters. Using an inner-outer loop design, this paper discusses an attitude and altitude controller for a quadrotor. As a highly nonlinear system, quadrotor dynamics can be simplified by assuming several assumptions. Quadrotor autopilot is developed using nonlinear feedback linearization technique, LQR, SMC, PD, and PID controllers. Often, these approaches are used to improve control and to reject disturbances. PD-PID controllers are also deployed in the tracking and surveillance of smoke or fire by intelligent algorithms. In this paper, the efficiency using a combined PD-PID controllers with adjustable parameters have been studied. The performance was assessed by simulation using matlab Simulink. The computational study conducted to assess the proposed approach showed that the PD-PID combination presented in this paper yields promising outcomes.

ResNet-SVM: Fusion based glioblastoma tumor segmentation and classification.

Computerized segmentation of brain tumor based on magnetic resonance imaging (MRI) data presents an important challenging act in computer vision. In image segmentation, numerous studies have explored the feasibility and advantages of employing deep neural network methods to automatically detect and segment brain tumors depicting on MRI. For training the deeper neural network, the procedure usually requires extensive computational power and it is also very time-consuming due to the complexity and the gradient diffusion difficulty. In order to address and help solve this challenge, we in this study present an automatic approach for Glioblastoma brain tumor segmentation based on deep Residual Learning Network (ResNet) to get over the gradient problem of deep Convolutional Neural Networks (CNNs). Using the extra layers added to a deep neural network, ResNet algorithm can effectively improve the accuracy and the performance, which is useful in solving complex problems with a much rapid training process. An additional method is then proposed to fully automatically classify different brain tumor categories (necrosis, edema, and enhancing regions). Results confirm that the proposed fusion method (ResNet-SVM) has an increased classification results of accuracy (AC = 89.36%), specificity (SP = 92.52%) and precision (PR = 90.12%) using 260 MRI data for the training and 112 data used for testing and validation of Glioblastoma tumor cases. Compared to the state-of-the art methods, the proposed scheme provides a higher performance by identifying Glioblastoma tumor type.

Sparse classification of discriminant nystagmus features using combined video-oculography tests and pupil tracking for common vestibular disorder recognition.

Vertigo is a common sign related to a problem with the brain or vestibular system. Detection of ocular nystagmus can be a support indicator to distinguish different vestibular disorders. In order to get reliable and accurate real time measurements from nystagmus response, video-oculography (VOG) plays an important role in the daily clinical examination. However, vestibular diseases present a large diversity in their characteristics that leads to many complications for usual analysis. In this paper, we propose a novel automated approach to achieve both selection and classification of nystagmus parameters using four tests and a pupil tracking procedure in order to give reliable evaluation and standardized indicators of frequent vestibular dysfunction that will assist clinicians in their diagnoses. Indeed, traditional tests (head impulse, caloric, kinetic and saccadic tests) are applied to obtain clinical parameters that highlight the type of vertigo (peripheral or central vertigo). Then, a pupil tracking method is used to extract temporal and frequency nystagmus features in caloric and kinetic sequences. Finally, all extracted features from the tests are reduced according to their high characterization degree by linear discriminant analysis, and classified into three vestibular disorders and normal cases using sparse representation. The proposed methodology is tested on a database containing 90 vertiginous subjects affected by vestibular Neuritis, Meniere's disease and Migraines. The presented technique highly reduces labor-intensive workloads of clinicians by producing the discriminant features for each vestibular disease which will significantly speed up the vertigo diagnosis and provides possibility for fully computerized vestibular disorder evaluation.

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA.

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis.

BACKGROUD AND OBJECTIVE: The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition. METHODS: In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects. RESULTS: The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy. CONCLUSIONS: The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.

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

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.

Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images.

This paper presents an advanced approach for foetal brain abnormalities diagnostic by integrating significant biometric features in the identification process. In foetal anomaly diagnosis, manual evaluation of foetal behaviour in ultrasound images is a subjective, slow and error-prone task, especially in the preliminary treatment phases. The effectiveness of this appearance is strictly subject to the attention and the experience of gynaecologists. In this case, automatic methods of image analysis offer the possibility of obtaining a homogeneous, objective and above all fast diagnosis of the foetal head in order to identify pregnancy behaviour. Indeed, we propose a computerised diagnostic method based on morphological characteristics and a supervised classification method to categorise subjects into two groups: normal and affected cases. The presented method is validated on a real integrated microcephaly and dolichocephaly cases. The studied database contains the same gestational age of both normal and abnormal foetuses. The results show that the use of a support vector machine (SVM) classifier is an effective way to enhance recognition and detection for rapid and accurate foetal head diagnostic.

Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images.

BACKGROUND AND OBJECTIVE: This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process. METHODS: The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm. Stromal cells are then removed from the segmented image using an adaptive criterion in order to get fast tumor nuclei recognition. Finally, unsupervised classification of cancer nuclei is obtained by the combination of four common color separation techniques for a subsequent Allred cancer scoring. RESULTS: Experimental results on various IHC tissue images of different cancer affected patients, demonstrate the effectiveness of the proposed scheme when compared to the manual scoring of pathological experts. A statistical analysis is performed on the whole image database between immuno-score of manual and automatic method, and compared with the scores that have reached using other state-of-art segmentation and classification strategies. According to the performance evaluation, we recorded more than 98% for both accuracy of detected nuclei and image cancer scoring over the truths provided by experienced pathologists which shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.993, p-value < 0.005) and the lowest computational total time of 72.3 s/image (±1.9) compared to recent studied methods. CONCLUSIONS: The proposed scheme can be easily applied for any histopathological diagnostic process that needs stained nuclear quantification and cancer grading. Moreover, the reduced processing time and manual interactions of our procedure can facilitate its implementation in a real-time device to construct a fully online evaluation system of IHC tissue images.

A new preprocessing parameter estimation based on geodesic active contour model for automatic vestibular neuritis diagnosis.

The diagnostic of the vestibular neuritis (VN) presents many difficulties to traditional assessment methods This paper deals with a fully automatic VN diagnostic system based on nystagmus parameter estimation using a pupil detection algorithm. A geodesic active contour model is implemented to find an accurate segmentation region of the pupil. Hence, the novelty of the proposed algorithm is to speed up the standard segmentation by using a specific mask located on the region of interest. This allows a drastically computing time reduction and a great performance and accuracy of the obtained results. After using this fast segmentation algorithm, the obtained estimated parameters are represented in temporal and frequency settings. A useful principal component analysis (PCA) selection procedure is then applied to obtain a reduced number of estimated parameters which are used to train a multi neural network (MNN). Experimental results on 90 eye movement videos show the effectiveness and the accuracy of the proposed estimation algorithm versus previous work.

A new automatic image analysis method for assessing estrogen receptors' status in breast tissue specimens.

Manual assessment of estrogen receptors' (ER) status from breast tissue microscopy images is a subjective, time consuming and error prone process. Automatic image analysis methods offer the possibility to obtain consistent, objective and rapid diagnoses of histopathology specimens. In breast cancer biopsies immunohistochemically (IHC) stained for ER, cancer cell nuclei present a large variety in their characteristics that bring various difficulties for traditional image analysis methods. In this paper, we propose a new automatic method to perform both segmentation and classification of breast cell nuclei in order to give quantitative assessment and uniform indicators of IHC staining that will help pathologists in their diagnostic. Firstly, a color geometric active contour model incorporating a spatial fuzzy clustering algorithm is proposed to detect the contours of all cell nuclei in the image. Secondly, overlapping and touching nuclei are separated using an improved watershed algorithm based on a concave vertex graph. Finally, to identify positive and negative stained nuclei, all the segmented nuclei are classified into five categories according to their staining intensity and morphological features using a trained multilayer neural network combined with Fisher's linear discriminant preprocessing. The proposed method is tested on a large dataset containing several breast tissue images with different levels of malignancy. The experimental results show high agreement between the results of the method and ground-truth from the pathologist panel. Furthermore, a comparative study versus existing techniques is presented in order to demonstrate the efficiency and the superiority of the proposed method.