Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis

OBJECTIVE: To study the application of image processing for segmentation of blastocysts images and extraction of potential variables for prediction of embryo fitness. DESIGN: Retrospective study. SETTING: Single reproductive medical center. IVI-RMA (Valencia, Spain) between 2017 and 2019. PATIENTS: An initial dataset including 353 images from EmbryoScope and 474 images from Geri incubators was acquired, of which 320 images from EmbryoScope and 309 images from Geri incubators were used in this study. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Successful segmentation of images into trophectoderm (TE), blastocoel, and inner cell mass (ICM) using the proposed processing steps. RESULTS: A total of 33 variables were automatically generated by digital image processing, each representing a different aspect of the embryo and describing a different characteristic of the expanding blastocyst (EX), ICM, or TE. These variables can be categorized into texture, gray level average, gray level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. CONCLUSIONS: The proposed image processing protocol that can successfully segment human blastocyst images from two distinct sources and extract 33 variables with potential utility in embryo selection for ART.

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Section: Methodsmentioning

confidence: 99%

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

Chéles

Ferreira

Jesus

et al. 2022

Preprint

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“…In [3], Le et al proposed an adapted linear discriminant analysis with variable selection for the classification in high-dimension and applied the method to medical data. Some other works that tried to adapt FDA to different fields include the works with application in health care [3]- [7], and facial recognition [8]- [12]. In addition, the nature of the data may also require adaption, which leads to even more modification of FDA.…”

Section: Denotes Equal Contributionmentioning

confidence: 99%

Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification

Nguyen¹,

Le²,

Tu³

et al. 2022

Preprint

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Fisher Discriminant Analysis (FDA) is one of the essential tools for feature extraction and classification. In addition, it motivates the development of many improved techniques based on the FDA to adapt to different problems or data types. However, none of these approaches make use of the fact that the assumption of equal covariance matrices in FDA is usually not satisfied in practical situations. Therefore, we propose a novel classification rule for the FDA that accounts for this fact, mitigating the effect of unequal covariance matrices in the FDA. Furthermore, since we only modify the classification rule, the same can be applied to many FDA variants, improving these algorithms further. Theoretical analysis reveals that the new classification rule allows the implicit use of the class covariance matrices while increasing the number of parameters to be estimated by a small amount compared to going from FDA to Quadratic Discriminant Analysis. We illustrate our idea via experiments, which shows the superior performance of the modified algorithms based on our new classification rule compared to the original ones.

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“…The textures were examined by the local binary pattern (LBP) algorithm and by constructing a gray-level cooccurrence matrix (GLCM). The LBP algorithm is a widely applied approach to recognize and analyze texture in images [39]. In this study, the LBP technique was applied to determine the textures of the ICM, TE, and EX.…”

Section: Binary Image Constructionmentioning

confidence: 99%

“…Then, all values of the neighboring pixels are changed to a binary value (0 if lower than the value of the central pixel or 1 if larger). After this transformation, the encoding step involves conversion of the binary number obtained to a decimal value [39]. In the first step, all neighboring pixels in each defined area (e.g., pattern) are compared to the value of the central pixel.…”

Section: Binary Image Constructionmentioning

confidence: 99%

“…Then, all values of the neighboring pixels are changed to a binary value (0 if lower than the value of the central pixel or 1 if larger). After this transformation, the encoding step involves conversion of the binary number obtained to a decimal value [39]. The GLCM algorithm was also applied to examine the textures of the ICM, TE, and the blastocoel plus ICM images obtained by segmentation.…”

Section: Binary Image Constructionmentioning

confidence: 99%

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An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

et al. 2022

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Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).

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Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis

Cited by 11 publications

References 5 publications

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction

Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification

An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

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