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

• Main Authors: Chetna Kaushal, Anshu Singla
• Format: Article
• Language: English
• Published: Wiley 2020-11-01
• Series: CAAI Transactions on Intelligence Technology
• Subjects: cancer; medical image processing; feature extraction; image segmentation; image enhancement; convolutional neural nets; automated segmentation technique; self-driven post-processing operations; cancerous cell detection; post-processing method; weighted area coefficient parameters; spatial neutrosophic distance regularised level set; histopathological breast cancer images; breast tissue; window area size; time steps; magnification factor; region of interest extraction; fuzzy c-means; spatial fuzzy c-means; convolutional neural network-based pangnet
• Online Access: https://digital-library.theiet.org/content/journals/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.