Osteosarcoma Detection from Whole Slide Images Using Multi-Feature Non-Seed-Based Region Growing Segmentation and Feature Extraction

Out of the various types of primary bone cancers, Osteosarcoma is one of the most common malignant bone tumors. Children and teenagers are mostly affected by Osteosarcoma, which weakens the strength of their bones and sometimes may even result in death. It is important to develop an intelligent classifier that detect osteosarcoma accurately so that proper treatment can be given to the patient timely. In this paper, we propose an intelligent classifier that classifies osteosarcoma whole slide images (WSIs) into Viable Tumor, Non-Viable Tumor and Non-Tumor. To extract the region of interest (ROI) from WSIs, a Multi-Feature Non-Seed-based Region Growing algorithm (MFNSRG) based on intra-region homogeneity and inter-region heterogeneity maximization is used. We use textural heterogeneity along with color heterogeneity as the matching criteria during region growing. Finally, the background is eliminated using thresholding based on the size of a region and the ROI is obtained. The performance of MFNSRG is further improved by using Marine Predators, a recently proposed metaheuristic algorithm, which is used to obtain optimal value of segmentation parameters. Here, we use handcrafted methods to extract relevant features from the segmented image which are given as input to the classifier. The results of experimentation prove the superiority of the proposed approach as compared to the existing state-of-the-art algorithms.