The effect of ethnic factor on cephalic index in 17-20 years old females of north of Iran [Efecto del factor étnico en el índice cefálico en mujeres entre 17 y 20 años de edad del norte de Irán]

Cephalic index and head shape are affected by geographical, gender, age, racial and ethnic factors. This study was carried out to determine cephalic index and head shape in 17-20 years old female in Gorgan, North of Iran. This descriptive and cross sectional study is undertaken on 410 normal 17-20 years old female (Turkman group: n=203, Fars group: n=207). The study was done by classic cephalometry in Gorgan North of Iran. Means and SD of cephalic index was 85 ± 4.5 and 82.8 ± 3.6 in native Fars and Turkman groups, respectively. Dominant and rare type of head shape in native Fars group were hyperbrachycephalic (53.6%) and dolichocephalic (15%), and in Turkman group were brachycephalic (58.1%) and dolichocephalic (0.05%), respectively. With noticing of our results and other studies in the world, we can conclude that the role of ethnic factor on head dimensions. © 2007 Sociedad Chilena de Anatomía

Morphological evaluation of head in Turkman males in Gorgan-North of Iran

Cephalometry or measurement of human head is used in identification, forensic medicine, plastic surgery, orthodontics, archeology and examine the differences between races and ethnicities. This descriptive investigation was undertaken on 198 young Turkman males to determine the cephalic index and head phenotype among them in Gorgan, North of Iran. In this study cephalic index was determined by classic cephalometric method. Mean and standard deviation of cephalic index was 80.4 ± 4. Based on the cephalic index, the head shape of 42.4% of individuals were brachycephalic, 7.6% hyperbrachycephalic, 40.9% mesocephalic and 8.1% dolicocephalic. This research showed that Turkman individuals have typical brachycephalic phenotype. In comparison to other studies, we can conclude that the ethnic factor has an effective role on head phenotype in North of Iran

An Approach for Efficient Detection of Cephalometric Landmarks

AbstractIn this paper, a method is developed for the automated identification of cephalometric landmarks in orthodontics. The process of soft tissue edge detection is divided into two steps: detecting the sub-images that contained the required landmarks using combination of the Histograms of Oriented Gradients (HOG) descriptor with the Support Vector Machine (SVM), then utilizing Thresholding and Mathematical Morphological (TMM) algorithm to trace soft tissue profile. In addition, the mandible's edge is detected by the Active contours without edges (Chan-Vese method). Finally, the landmarks of soft tissue profile and the mandible's edge are pinned based on analyzing the contour plot of these lines. The simulation results have high accuracy

Análise Crítica e Sistemática sobre Técnicas Computacionais para a Detecção Automática de Pontos Cefalométricos

A análise cefalométrica, baseada em radiografias laterais da face, é uma ciência que fazparte do ramo odontológico, que visa obter dados referentes a localização e posição depontos cefalométricos, para que a partir dos mesmos seja realizado um diagnósticopelos profissionais da área [CHUKRUBURTTY S. et al ].De acordo com Ren et al (1998), existem cerca de 70 pontos de marcação em umaimagem cefalométrica comum, e esse grande número de pontos demanda uma grandequantidade de tempo para que os profissionais realizem sua identificação e marcação.O objetivo deste trabalho é realizar um estudo crítico e sistemático de técnicas dedetecção automática de pontos cefalométricos a fim de se identificar as que apresentammelhores resultados para um maior número de pontos cefalométricos

Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research

Anatomical Structure Sketcher for Cephalograms by Bimodal Deep Learning

The lateral cephalogram is a commonly used medium to acquire patient-specific morphology for diagnose and treatment planning in clinical dentistry. The robust anatomical structure detection and accurate annotation remain challenging considering the personal skeletal variations and image blurs caused by device-specific projection magnification, together with structure overlapping in the lateral cephalograms. We propose a novel cephalogram sketcher system, where the contour extraction of anatomical structures is formulated as a cross-modal morphology transfer from regular image patches to arbitrary curves. Specifically, the image patches of structures of interest are located by a hierarchical pictorial model. The automatic contour sketcher converts the image patch to a morphable boundary curve via a bimodal deep Boltzmann machine. The deep machine learns a joint representation of patch textures and contours, and forms a path from one modality (patches) to the other (contours). Thus, the sketcher can infer the contours by alternating Gibbs sampling along the path in a manner similar to the data completion. The proposed method is robust not only to structure detection, but also tends to produce accurate structure shapes and landmarks even in blurry X-ray images. The experiments performed on clinically captured cephalograms demonstrate the effectiveness of our method.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000346352700099&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceCPCI-S(ISTP)

Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages

In this paper we perform an empirical evaluation of variants of deep learning methods to automatically localize anatomical landmarks in bioimages of fishes acquired using different imaging modalities (microscopy and radiography). We compare two methodologies namely heatmap based regression and multivariate direct regression, and evaluate them in combination with several Convolutional Neural Network (CNN) architectures. Heatmap based regression approaches employ Gaussian or Exponential heatmap generation functions combined with CNNs to output the heatmaps corresponding to landmark locations whereas direct regression approaches output directly the (x, y) coordinates corresponding to landmark locations. In our experiments, we use two microscopy datasets of Zebrafish and Medaka fish and one radiography dataset of gilthead Seabream. On our three datasets, the heatmap approach with Exponential function and U-Net architecture performs better. Datasets and open-source code for training and prediction are made available to ease future landmark detection research and bioimaging applications