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Tuberculosis (TB) is a worldwide health crisis and a primary infectious disease that causes death. An attempt has been made to detect the presence of bacilli in sputum smears. The smear images recorded under standard image acquisition protocol are segmented by metaheuristic-based methods. Morphological operators are embedded in ant colony optimization (ACO) and particle swarm optimization (PSO) segmentation to retain concavity of rod-shaped bacilli. Results demonstrate that hybrid ACO segmentation is able to retain the shape of bacilli in TB images. Segmented images are validated with ground truth using overlap, distance, and probability-based measures. Significant shape-based features such as area, perimeter, compactness, shape factor, and tortuosity are extracted from the segmented images. It is observed that hybrid method preserves more edges, detects the presence of bacilli, and facilitates direct segmentation with reduced number of redundant searches to generate edges. Thus, this hybrid ACO-morphology segmentation technique aids in the diagnostic relevance of TB images.

Metaheuristic-Based Optimization Methods for the Segmentation of Tuberculosis Sputum Smear Images