Automated Segmentation of Breast Skin for Early Cancer Diagnosis: A Multi-otsu Region Growing Approach for Detecting Skin Thickness Variations

Medina-Ramos, Luis Antonio; Toriz-Vázquez, Alfonso; Hernández-Rodríguez, Yazmín Mariela; Bayareh-Mancilla, Rafael; Cigarroa-Mayorga, Oscar Eduardo

doi:10.1007/978-3-031-46933-6_23

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…They have utilized dynamic time warping (DTW) for shape analysis and the growing seed region (GRS) method for breast skin segmentation. In [6], skin segmentation is also addressed using multi-class partition, which allows us to achieve a more refined delineation of skin regions. The segmentation could also be dealt with the new active contour model based on the level set method and Kullback-Leibler divergence presented in [7].…”

Section: Introductionmentioning

confidence: 99%

A New Method for 2D-Adapted Wavelet Construction: An Application in Mass-Type Anomalies Localization in Mammographic Images

Valdés-Santiago,

León-Mecías,

Baguer Díaz-Romañach

et al. 2024

Applied Sciences

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This contribution presents a wavelet-based algorithm to detect patterns in images. A two-dimensional extension of the DST-II is introduced to construct adapted wavelets using the equation of the tensor product corresponding to the diagonal coefficients in the 2D discrete wavelet transform. A 1D filter was then estimated that meets finite energy conditions, vanished moments, orthogonality, and four new detection conditions. These allow, when performing the 2D transform, for the filter to detect the pattern by taking the diagonal coefficients with values of the normalized similarity measure, defined by Guido, as greater than 0.7, and α=0.1. The positions of these coefficients are used to estimate the position of the pattern in the original image. This strategy has been used successfully to detect artificial patterns and localize mass-like abnormalities in digital mammography images. In the case of the latter, high sensitivity and positive predictive value in detection were achieved but not high specificity or negative predictive value, contrary to what occurred in the 1D strategy. This means that the proposed detection algorithm presents a high number of false negatives, which can be explained by the complexity of detection in these types of images.

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Section: Introductionmentioning

confidence: 99%

A New Method for 2D-Adapted Wavelet Construction: An Application in Mass-Type Anomalies Localization in Mammographic Images

Valdés-Santiago,

León-Mecías,

Baguer Díaz-Romañach

et al. 2024

Applied Sciences

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“…Bayareh-Mancilla et al integrated the morphological shape and skin thickness in the mammogrambased asymmetry screening and demonstrated that such explainable features could significantly improve the mammogram screening performance [4]. Explainable medical image features like segmentation-based skin thickness were further supported by the efficacy of early cancer detection [5]. The rs-fMRI scans were acquired using the Siemens Tim Trio 3T MRI scanner at Southwestern University.…”

Section: Introductionmentioning

confidence: 99%

DepressionGraph: A Two-Channel Graph Neural Network for the Diagnosis of Major Depressive Disorders Using rs-fMRI

Xia,

Fan,

et al. 2023

Electronics

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Major depressive disorder (MDD) is a prevalent psychiatric condition with a complex and unknown pathological mechanism. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable non-invasive technology for MDD diagnosis. By utilizing rs-fMRI data, a dynamic brain functional connection network (FCN) can be constructed to represent the complex interacting relationships of multiple brain sub-regions. Graph neural network (GNN) models have been widely employed to extract disease-associated information. The simple averaging or summation graph readout functions of GNNs may lead to a loss of critical information. This study introduces a two-channel graph neural network (DepressionGraph) that effectively aggregates more comprehensive graph information from the two channels based on the node feature number and node number. Our proposed DepressionGraph model leverages the transformer–encoder architecture to extract the relevant information from the time-series FCN. The rs-fMRI data were obtained from a cohort of 533 subjects, and the experimental data show that DepressionGraph outperforms both traditional GNNs and simple graph readout functions for the MDD diagnosis task. The introduced DepressionGraph framework demonstrates efficacy in extracting complex patterns from rs-fMRI data and exhibits promising capabilities for the precise diagnosis of complex neurological disorders. The current study acknowledges a potential gender bias due to an imbalanced gender distribution in the dataset. Future research should prioritize the development and utilization of gender-balanced datasets to mitigate this limitation and enhance the generalizability of the findings.

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A Hybrid Segmentation Method BCOT for Skin Lesion Detection

Harish,

Vaishnavi,

Shashanka

et al. 2024

2024 International Conference on Social and Sustainable Innovations in Technology and Engineering (SASI-ITE)

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Automated Segmentation of Breast Skin for Early Cancer Diagnosis: A Multi-otsu Region Growing Approach for Detecting Skin Thickness Variations

Cited by 3 publications

References 16 publications

A New Method for 2D-Adapted Wavelet Construction: An Application in Mass-Type Anomalies Localization in Mammographic Images

A New Method for 2D-Adapted Wavelet Construction: An Application in Mass-Type Anomalies Localization in Mammographic Images

DepressionGraph: A Two-Channel Graph Neural Network for the Diagnosis of Major Depressive Disorders Using rs-fMRI

A Hybrid Segmentation Method BCOT for Skin Lesion Detection

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