Automated breast nuclei feature extraction for segmentation in histopathology images using Deep-CNN-based gaussian mixture model and color optimization technique.
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| Title: | Automated breast nuclei feature extraction for segmentation in histopathology images using Deep-CNN-based gaussian mixture model and color optimization technique. |
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| Authors: | Murmu, Anita1 (AUTHOR) anitamurmu.cs@gmail.com, Kumar, Piyush1 (AUTHOR) piyush.cs@nitp.ac.in |
| Source: | Multimedia Tools & Applications. Sep2025, Vol. 84 Issue 31, p37933-37959. 27p. |
| Subjects: | Cancer diagnosis, Cell segmentation, Transfer of training, Convolutional neural networks, Gaussian mixture models, Image analysis, Color in design |
| Abstract: | Hematoxylin and eosin (H&E) staining are the key sources for identifying breast cancer patterns with different colors and shapes of nuclei cells for segmenting histopathology nucleus images. In nucleus cells, the color categorization of hematoxylin is stained blue, whereas cytoplasmic cells of eosin are stained pink. This color dissimilarity in the nucleus cells indicates different structures of the cellular and cancer-specific cells, which need to detect on time is a challenge for accurate diagnosis. To overcome this issue, a Deep Convolution Neural Network (Deep-CNN) based Unet framework with Transfer Learning (TL) is proposed for segmenting histopathology breast cancer images. Specifically, the Deep-CNN framework has been used to extract features automatically from input images, and select the features by using the TL technique for minimizing the target tasks to train it faster. Furthermore, a deep convolutional-based Gaussian Mixture Model (GMM) is used to normalize the shapes and merge the nucleus cell pixels for color optimization. The proposed method has been tested on Kaggle's 2018 Data Science Blow histopathology breast image dataset. The experimental results of the proposed scheme have been evaluated by performance evaluation matrices, namely accuracy, Jaccard coefficient, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and F1-score. The proposed approach outperforms compared with different state-of-the-art models on the same dataset. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188021230 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automated breast nuclei feature extraction for segmentation in histopathology images using Deep-CNN-based gaussian mixture model and color optimization technique. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Murmu%2C+Anita%22">Murmu, Anita</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> anitamurmu.cs@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Piyush%22">Kumar, Piyush</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> piyush.cs@nitp.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Sep2025, Vol. 84 Issue 31, p37933-37959. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cancer+diagnosis%22">Cancer diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+segmentation%22">Cell segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Transfer+of+training%22">Transfer of training</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+mixture+models%22">Gaussian mixture models</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Color+in+design%22">Color in design</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Hematoxylin and eosin (H&E) staining are the key sources for identifying breast cancer patterns with different colors and shapes of nuclei cells for segmenting histopathology nucleus images. In nucleus cells, the color categorization of hematoxylin is stained blue, whereas cytoplasmic cells of eosin are stained pink. This color dissimilarity in the nucleus cells indicates different structures of the cellular and cancer-specific cells, which need to detect on time is a challenge for accurate diagnosis. To overcome this issue, a Deep Convolution Neural Network (Deep-CNN) based Unet framework with Transfer Learning (TL) is proposed for segmenting histopathology breast cancer images. Specifically, the Deep-CNN framework has been used to extract features automatically from input images, and select the features by using the TL technique for minimizing the target tasks to train it faster. Furthermore, a deep convolutional-based Gaussian Mixture Model (GMM) is used to normalize the shapes and merge the nucleus cell pixels for color optimization. The proposed method has been tested on Kaggle's 2018 Data Science Blow histopathology breast image dataset. The experimental results of the proposed scheme have been evaluated by performance evaluation matrices, namely accuracy, Jaccard coefficient, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and F1-score. The proposed approach outperforms compared with different state-of-the-art models on the same dataset. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-025-20676-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 37933 Subjects: – SubjectFull: Cancer diagnosis Type: general – SubjectFull: Cell segmentation Type: general – SubjectFull: Transfer of training Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Gaussian mixture models Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Color in design Type: general Titles: – TitleFull: Automated breast nuclei feature extraction for segmentation in histopathology images using Deep-CNN-based gaussian mixture model and color optimization technique. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Murmu, Anita – PersonEntity: Name: NameFull: Kumar, Piyush IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 31 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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