Automated segmentation technique with self-driven post-processing for histopathological breast cancer images

• Published in: CAAI Transactions on Intelligence Technology Vol. 5; no. 4; pp. 294 - 300
• Main Authors: Kaushal, Chetna, Singla, Anshu
• Format: Journal Article
• Language: English
• Published: Beijing The Institution of Engineering and Technology 01.12.2020; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Artificial neural networks; automated segmentation technique; Automation; Breast cancer; breast tissue; cancer; cancerous cell detection; Clustering; convolutional neural nets; convolutional neural network-based pangnet; Deep learning; feature extraction; fuzzy C‐means; Fuzzy logic; Fuzzy sets; histopathological breast cancer images; Image enhancement; Image segmentation; magnification factor; medical image processing; Medical imaging; Morphology; Neural networks; Parameter sensitivity; post-processing method; region of interest extraction; Research Article; self-driven post-processing operations; spatial fuzzy C‐means; spatial neutrosophic distance regularised level set; time steps; Watersheds; weighted area coefficient parameters; window area size; time steps; automated segmentation technique; histopathological breast cancer images; region of interest extraction; self-driven post-processing operations; weighted area coefficient parameters; magnification factor; window area size; spatial neutrosophic distance regularised level set; image enhancement; feature extraction; image segmentation; fuzzy C-means; cancerous cell detection; spatial fuzzy C-means; post-processing method; breast tissue; cancer; convolutional neural nets; convolutional neural network-based PangNet; medical image processing
• ISSN: 2468-2322; 2468-2322
• DOI: 10.1049/trit.2019.0077

Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue. Recent reviews state high accuracy to segment image, but depends on user input, say window area size, time steps, level set, magnification factor and so on. To extract the region of interest effectively, the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say, area opening, fill holes and selects most appropriate enhanced image required for further analysis. The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively. The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained. The proposed technique has the following features: (i) technique is context sensitive; (ii) no prior setting of time step, weighted area coefficient parameters is required; (iii) magnification independent; (iv) post-processing operations are self-driven which enhance segmentation results adaptively. The experimental results are compared with four state-of-the-art techniques: fuzzy C-means, spatial fuzzy C-means, spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet. Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.