Topological approaches to skin disease image analysis

Chung, Yu-Min; Hu, Chuan-Shen; Lawson, Austin; Smyth, Clifford

doi:10.1109/bigdata.2018.8622175

Cited by 22 publications

(37 citation statements)

References 13 publications

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“…Due to their stability, it could be interesting to apply them in a biological context where noise is usually abundant. A firs step in this direction have been done, for example, in [10] and [1].…”

Section: Discussionmentioning

confidence: 99%

“…We now define a new function which pairs a persistence barcode A ∈ B F with a piecewise constant function in R. This new function summarizes information about the number of intervals of a given persistence barcode and their homogeneity and, as we will prove at the end of this subsection, is stable with respect to the bottleneck distance. It is worth saying that the usefulness of this function was proven in [10] where it was used to classify different skin lesions. Notice that the ES-function is bounded and has compact support in R. Therefore its 1-norm 3 is always finite.…”

Section: Entropy Summary Function (Es-function)mentioning

confidence: 99%

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A new entropy based summary function for topological data analysis

Atienza

González-Dı́az

Soriano-Trigueros

2018

Electronic Notes in Discrete Mathematics

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Section: Discussionmentioning

confidence: 99%

Section: Entropy Summary Function (Es-function)mentioning

confidence: 99%

A new entropy based summary function for topological data analysis

Atienza

González-Dı́az

Soriano-Trigueros

2018

Electronic Notes in Discrete Mathematics

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“…In dermatology, several studies applied TDA to the malignancy classification of melanomas using skin images taken by dermatoscopes or stereomicroscopes [20][21][22] . Another study proposed a method of applying TDA to classify seven skin diseases including melanomas and basal cell carcinomas 23 . These studies have shown that skin image analysis using TDA is useful for the qualitative assessment of skin diseases.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, TDA has also been applied in medicine to the quantification of tumor shapes [15][16][17] , finding patterns in genetic data of cancer patients 18 , and characterizing brain artery networks 19 . In dermatology, TDA has been applied to segmenting and classifying skin lesions [20][21][22][23] and quantifying the connectivity of epidermal cells 24 . TDA detects the number of topological features, such as connected components, holes, and cavities, and demonstrates their robustness and magnitude.…”

Section: Introductionmentioning

confidence: 99%

Assessment of skin barrier function using skin images with topological data analysis

et al. 2020

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Recent developments of molecular biology have revealed diverse mechanisms of skin diseases, and precision medicine considering these mechanisms requires the frequent objective evaluation of skin phenotypes. Transepidermal water loss (TEWL) is commonly used for evaluating skin barrier function; however, direct measurement of TEWL is time-consuming and is not convenient for daily clinical practice. Here, we propose a new skin barrier assessment method using skin images with topological data analysis (TDA). TDA enabled efficient identification of structural features from a skin image taken by a microscope. These features reflected the regularity of the skin texture. We found a significant correlation between the topological features and TEWL. Moreover, using the features as input, we trained machine-learning models to predict TEWL and obtained good accuracy (R2 = 0.524). Our results suggest that assessment of skin barrier function by topological image analysis is promising.

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“…From the theoretical perspective, E(L) is a stable measurement (Theorem 3.12 in Atienza et al, 2020). E(M) was first appeared in Chung et al (2018) and the discussion about the stability of E(M) can be found in Chung and Lawson (2019).…”

Section: Persistence Statisticsmentioning

confidence: 99%

A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification

Chung

et al. 2021

Front. Physiol.

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Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects—the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.

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Topological approaches to skin disease image analysis

Cited by 22 publications

References 13 publications

A new entropy based summary function for topological data analysis

A new entropy based summary function for topological data analysis

Assessment of skin barrier function using skin images with topological data analysis

A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification

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