Cleft lip pathology diagnosis and foetal landmark extraction via 3D geometrical analysis

Moos, Sandro; Marcolin, Federica; Tornincasa, Stefano; Vezzetti, Enrico; Violante, Maria Grazia; Fracastoro, Giulia; Speranza, Domenico; Padula, Francesco

doi:10.1007/s12008-014-0244-1

Cited by 28 publications

(20 citation statements)

References 33 publications

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“…Some scholars have indicated that additional anomalies diagnosed by MRI may lead to changes in fetal cleft lip and palate prognosis . In the present study, it was found that the cleft lip and palate, combined with other malformations, such as bilateral external auditory atresia and holoprosencephaly, were correctly detected by MRI.…”

Section: Discussionsupporting

confidence: 56%

“…More recently, a method for automatic diagnosis for cleft lip with 3‐D US was developed. The 3‐D geometrical analysis can visualize all relevant anatomical landmarks, such as upper lip outline and quantification of facial asymmetry . However, this method is not suitable for evaluating of the palate, hence a fetal CP can be easily missed by US .…”

Section: Introductionmentioning

confidence: 99%

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Accurate diagnosis of fetal cleft lip/palate by typical signs of magnetic resonance imaging

et al. 2019

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Objective This study aims to evaluate the accuracy of magnetic resonance imaging (MRI) in diagnosing orofacial clefts and proposes specific signs for determining the type of cleft. Methods Pregnant women in whom fetal facial malformations are suspected by ultrasonography (US) underwent 1.5 T MRI. The accuracy of the prenatal US and MRI diagnosis was compared with the postnatal findings. Results A total of 71 fetuses were included in the final analysis, which comprised of 35 cases of isolated cleft lip, six cases of cleft lip and alveolus, 21 cases of cleft lip and palate, four cases of isolated cleft palate, and five normal fetuses. MRI was more sensitive than US (MRI 100%, US 77.5%; Fisher's exact test: MRI vs US P=0.000). The accuracy of MRI for all types was 100%. Specific signs (Inverted T, L/anti‐L, U, and “Notch” signs) were proposed, indicating the normal hard, unilateral cleft, bilateral cleft, and cleft soft palates, respectively, which can facilitate the identification of different types of cleft palates. Conclusion MRI can accurately diagnose the type of cleft based on typical signs, which can serve as a definite diagnostic modality and an effective supplement of US.

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Section: Discussionsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Accurate diagnosis of fetal cleft lip/palate by typical signs of magnetic resonance imaging

et al. 2019

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“…Another example of heterogeneous face recognition is sketch-photo matching [4], which is often used to detect suspects in forensic cases. In addition to 2D data, with the easy access to sensors used to acquire 3D data in recent years, 3D face images have become widely used for facial recognition [5] and facial analysis [6] for different purposes. In this work, we propose a face recognition scheme for 2D still images in visible and infrared spectra.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

2018

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Abstract:In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.

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“…These descriptors created by composing primary geometrical descriptors (for instance mean and Gaussian) and the coefficients of the fundamental forms, and by applying trigonometric functions (for instance sine and cosine) and logarithm to them [17]. Another approach for 3D feature extraction via geometrical analysis is presented in [18]. To the best of our knowledge, 3D face recognition methods haven't addressed 3D image similarity so far.…”

Section: Introductionmentioning

confidence: 99%

A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition

Shnain

Hussain

2017

Applied Sciences

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Featured Application: This work integrates image structural features into a new similarity measure suitable for face recognition with the new merit of recognition confidence.Abstract: Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called the Feature-Based Structural Measure (FSM), combines the best features of the well-known SSIM (structural similarity index measure) and FSIM (feature similarity index measure) approaches, striking a balance between performance for similar and dissimilar images of human faces. In addition to the statistical structural properties provided by SSIM, edge detection is incorporated in FSM as a distinctive structural feature. Its performance is tested for a wide range of PSNR (peak signal-to-noise ratio), using ORL (Olivetti Research Laboratory, now AT&T Laboratory Cambridge) and FEI (Faculty of Industrial Engineering, São Bernardo do Campo, São Paulo, Brazil) databases. The proposed measure is tested under conditions of Gaussian noise; simulation results show that the proposed FSM outperforms the well-known SSIM and FSIM approaches in its efficiency of similarity detection and recognition of human faces.

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Cleft lip pathology diagnosis and foetal landmark extraction via 3D geometrical analysis

Cited by 28 publications

References 33 publications

Accurate diagnosis of fetal cleft lip/palate by typical signs of magnetic resonance imaging

Accurate diagnosis of fetal cleft lip/palate by typical signs of magnetic resonance imaging

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition

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