Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images
Abstract
:1. Introduction
- Augmentation methods are used to deal with data scarcity. Additionally, stain normalization is used to deal with color inconsistencies.
- Morphology operations enhance the image by highlighting important features. The connected components analysis method is used to group components with similar characteristics and assist in separating overlapping and non-overlapping objects.
- The active contours method uses the obtained binary masks from the connected components analysis to highlight and isolate the edges/boundaries of ROIs. Further, the blob detection method is used to resolve undersegmentation from the previous step and identify BC lesions (blobs) from the previously obtained masked images.
2. Related Work
3. Methods and Techniques
3.1. Dataset Preparation and Pre-Processing
3.1.1. Data Augmentation
3.1.2. Data Stain Normalization
3.2. Image Enhancement
3.2.1. Thresholding
3.2.2. Morphology Operations
3.2.3. Distance Transform
3.3. Segmentation
3.3.1. Connected Components Analysis
3.3.2. Active Contours Segmentation
3.4. Detection
4. Results and Discussion
Limitations
5. Conclusions
6. Future Work
- Data availability and integrity. Most deep learning approaches require huge volumes of data to achieve meaningful performance results. Therefore, publicly available image datasets are necessary, especially histology image datasets, to assist deep learning.
- Regularization methods. These are needed to improve the performance of models. This can be achieved through model hyperparameter tuning, such as optimizing the learning rates, dropout, loss functions, activation functions, and early stopping methods.
- Hybrid image processing/model approaches. Combining various/several image processing methods or model architectures, it would be possible to form a hybrid method that improves the overall evaluation performance. This combination can occur at any step in the model, such as pre-processing, combining various attributes of different models to form one that will enhance the training, extraction, detection, and classification tasks. Additionally, future work could expand, explore, and diagnose other human and animal diseases through image datasets, moving beyond BC histology images.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors | Type of Image | Pre-Processing/Image Enhancement Methods | Segmentation Method | Detection Method | Accuracy |
---|---|---|---|---|---|
Reshma et al. [13] | Unsupervised images | Median filters, top- and bottom-hat filtering, grayscaling | Fourier transform | Speeded Up Robust Features (SURF) method | 85.17% |
Kiran et al. [10] | Unsupervised images | Color deconvolution, data augmentation | Binary thresholding, marker-controlled watershed algorithm | Dense Res-U-Net model | 90.03% |
Araujo et al. [18] | Supervised images | Macenko normalization, augmentation | CNN | Support vector machine (SVM) | 95.6% |
Isohail et al. [21] | Unsupervised images | Macenko normalization, mean standard deviation-based normalization | Masked R-CNN | Deep High Ensemble Mitotic Classifier (DHE-Mit-Classifier) | 77% |
Yu et al. [25] | Unsupervised images | Color deconvolution | Speeded Up Robust Features (SURF), gray-level co-occurrence matrix (GLCM), and local binary patterns (LBP) | Support vector machine (SVM) | 96.7% |
George et al. [27] | Supervised images | Macenko normalization | Laplacian of Gaussian (LoG)–blob detection algorithm | CNN (transfer learning) | 96.3% |
Sornapudi et al. [29] | Unsupervised images | Gaussian and median filters, linear transformation | Simple linear iterative clustering (SLIC) super-pixel algorithm | CNN (transfer learning) | 95.70% |
Veta et al. [33] | Unsupervised images | Color deconvolution, opening and closing morphology operations | Fast radial symmetry transform | Marker-controlled watershed | 81.5% |
Natarajan et al. [34] | Unsupervised images | Color and illumination normalization | LinkNet encoder–decoder architecture | LinkNet + Freeman chain coding (post-processing) | 97.2% |
Niaz [38] | Unsupervised images | None | Chan–Vese (CV) method, local binary fitting (LBF) method, local image fitting (LIF) method, variational level set with bias correction (VLSBC) method | Weighted length regularization by place (WLRP) method | 98.39% |
Xu et al. [40] | Unsupervised glomerulus images | Difference of Gradient (DoG), Hessian analysis | U-Net probability map | Hessian convexity map + U-Net probability map (blob detection) | 96.3% |
Majanga et al. [41] | Unsupervised dental images | Grayscaling, Gaussian blurring | Thresholding, erosion, dilation morphology, connected components analysis | Active contours, blob detection + convexity thresholding | 97.0% |
Proposed Method | Unsupervised breast histology images | Augmentation, Macenko normalization, binary thresholding, erosion, dilation, opening and closing morphology operations, distance transformation | Connected components analysis (CCA) method, active contours (AC) method | Blob detection on (CCA+AC) outputs | 98.82% |
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Majanga, V.; Mnkandla, E.; Wang, Z.; Moulla, D.K. Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images. Bioengineering 2025, 12, 364. https://doi.org/10.3390/bioengineering12040364
Majanga V, Mnkandla E, Wang Z, Moulla DK. Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images. Bioengineering. 2025; 12(4):364. https://doi.org/10.3390/bioengineering12040364
Chicago/Turabian StyleMajanga, Vincent, Ernest Mnkandla, Zenghui Wang, and Donatien Koulla Moulla. 2025. "Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images" Bioengineering 12, no. 4: 364. https://doi.org/10.3390/bioengineering12040364
APA StyleMajanga, V., Mnkandla, E., Wang, Z., & Moulla, D. K. (2025). Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images. Bioengineering, 12(4), 364. https://doi.org/10.3390/bioengineering12040364