Down syndrome recognition using local binary patterns and statistical evaluation of the system

Kurt, Burçin; Vasif, Nabiyev V.

doi:10.1016/j.eswa.2011.01.076

Cited by 55 publications

(29 citation statements)

References 4 publications

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“…A research group from Turkey achieved a maximum classification accuracy of 97.34% when analyzing a set of 30 facial images (15 Down syndrome, 15 healthy subjects) using Gabor wavelet transformations and a SVM classifier (17). A second study from Turkey published data on the use of an image-processing method based on local binary patterns for feature extraction and changed Manhattan distance for classification, with an overall correct classification rate of O90% in a sample of 107 facial images collected from the internet (51 subjects with Down syndrome, 56 healthy individuals) (18). Zhao et al (19) reported that using Independent Component Analysis for landmark placement, local binary patterns for feature extraction and SVMs for classification, they achieved 96.7% accuracy in the classification of a data set consisting of 130 facial photographs (50 patients with Down syndrome, 80 healthy subjects).…”

Section: Application Of Face Classification Technology In Genetic Dismentioning

confidence: 99%

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

Kosilek

Frohner

Würtz

et al. 2015

European Journal of Endocrinology

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Cushing's syndrome (CS) and acromegaly are endocrine diseases that are currently diagnosed with a delay of several years from disease onset. Novel diagnostic approaches and increased awareness among physicians are needed. Face classification technology has recently been introduced as a promising diagnostic tool for CS and acromegaly in pilot studies. It has also been used to classify various genetic syndromes using regular facial photographs. The authors provide a basic explanation of the technology, review available literature regarding its use in a medical setting, and discuss possible future developments. The method the authors have employed in previous studies uses standardized frontal and profile facial photographs for classification. Image analysis is based on applying mathematical functions evaluating geometry and image texture to a grid of nodes semi-automatically placed on relevant facial structures, yielding a binary classification result. Ongoing research focuses on improving diagnostic algorithms of this method and bringing it closer to clinical use. Regarding future perspectives, the authors propose an online interface that facilitates submission of patient data for analysis and retrieval of results as a possible model for clinical application.

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Section: Application Of Face Classification Technology In Genetic Dismentioning

confidence: 99%

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

Kosilek

Frohner

Würtz

et al. 2015

European Journal of Endocrinology

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show abstract

“…5 [1,6]. This operator is defined as a gray scale invariant texture measure and derived from a general definition of texture in a local neighborhood [30]. Only eight neighbors of a pixel were taken into account in the basic version of the LBP, but it has been extended to include all circular neighborhoods with any number of pixels [1,20,3].…”

Section: Traditional Local Binary Pattern Operatormentioning

confidence: 99%

Two novel local binary pattern descriptors for texture analysis

Kaya

Ertuğrul

Tekin

2015

Applied Soft Computing

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“…Both methods compared the results and got 98.25% accuracy for GLCM and 96.45% for LBP [6]. Although the results of the previous study showed LBP was slightly lower than other methods, LBP was introduced to describe images well and widely used in computer vision, image processing and image retrieval of images, remote sensing and biomedical image analysis [7]. The advantage of using the LBP operator is its tolerance to changes in illumination, the lightweight computation that makes it possible to analyze images in real-time.…”

Section: Introductionmentioning

confidence: 99%

Local Binary Pattern Method and Feature Shape Extraction for Detecting Butterfly Image

Kartika¹

2018

GEOMATE

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ABSTRACT:Research in the field of information retrieval especially on image processing is proliferating. Various methods are developed to be able to detect images optimally and produce better accuracy. The process of image detection can use the dataset that exists around us. In this research, we use butterflies dataset, since the butterfly has unique colors, patterns, and diverse shapes. Therefore, we use local binary pattern method for texture feature extraction and region props for shape feature extraction. The results of each texture feature extraction and shape feature extraction will be a merging process. The results of the merging process get an accuracy of 66%. In addition, the system testing process with confusion matrix will produce 67.1% precision value, 66% recall and f-measure of 66.5%. The merging process of both methods shows the interplay of texture and shape extraction.

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Down syndrome recognition using local binary patterns and statistical evaluation of the system

Cited by 55 publications

References 4 publications

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives

Two novel local binary pattern descriptors for texture analysis

Local Binary Pattern Method and Feature Shape Extraction for Detecting Butterfly Image

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