Feasibility of Haralick's Texture Features for the Classification of Chromogenic In-situ Hybridization Images

Pavlov, Stoyan; Momcheva, Galina; Burlakova, Pavlina; Atanasov, Simeon; Stoyanov, Dimo; Ivanov, Martin; Tonchev, Anton B.

doi:10.1109/bia50171.2020.9244282

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Figure 7 indicates the quality of the texture preservation measured by Haralick 34 of the state-of -the-art methods on both L506 and L291 data sets. As a result, TADDM-Net achieves the closest approximation to NDCT on both data sets.…”

Section: The Results On the Aapm-mayo Clinic Data Setsmentioning

confidence: 99%

“…In order to further quantitatively analyze the texture changes, as shown in Table 1, the Mean & Std of reconstructed images (measured by HU value), Haralick, 34 and visual information fidelity (VIF) 35 are calculated.And Mean & Std of reconstructed images are used to measure the image quality on the pixel level, and the Haralick is exploited to measure the quality of image texture restoration. In Table 2, we compare the new method with other methods, including the FBP, 36 CNN-MSE, 33 RED-CNN, 12 WGAN-VGG, 13 DP-ResNet, 18 MDD-U-Net, 19 DudoNet, 17 and CNN-SA-AE.…”

Section: The Results On the Aapm-mayo Clinic Data Setsmentioning

confidence: 99%

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Texture‐aware dual domain mapping model for low‐dose CT reconstruction

et al. 2022

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Background Remarkable progress has been made for low‐dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low‐dose CT (LDCT) reconstruction. Purpose Most of the existing deep learning‐based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects. Methods The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S‐DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self‐attention (SA) residual encoder & decoder network (SRED‐Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature‐aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality. Results The proposed method was validated using both the American association of physicists in medicine (AAPM)‐Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state‐of‐the‐art performance on objective indicators and visual metrics in terms of denoising and texture restoration. Conclusions Compared with single‐domain or existing dual‐domain processing strategies, the proposed texture‐aware dual domain mapping network (TADDM‐Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability.

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Section: The Results On the Aapm-mayo Clinic Data Setsmentioning

confidence: 99%

Section: The Results On the Aapm-mayo Clinic Data Setsmentioning

confidence: 99%

Texture‐aware dual domain mapping model for low‐dose CT reconstruction

et al. 2022

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Comprehensive Review on Computational In-Situ Hybridization (Ish) Digital Pathology Using Image Analysis Techniques: Principles and Applications

Rehman,

W Ahmad,

Ahmad Fauzi

et al. 2024

Preprint

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Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques

Rehman,

Ahmad Fauzi,

Wan Ahmad

et al. 2024

Diagnostics

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Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20–25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques.

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Feasibility of Haralick's Texture Features for the Classification of Chromogenic In-situ Hybridization Images

Cited by 3 publications

References 27 publications

Texture‐aware dual domain mapping model for low‐dose CT reconstruction

Texture‐aware dual domain mapping model for low‐dose CT reconstruction

Comprehensive Review on Computational In-Situ Hybridization (Ish) Digital Pathology Using Image Analysis Techniques: Principles and Applications

Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques

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