An automated computer vision based preliminary study for the identification of a heavy metal (Hg) exposed fish-channa punctatus.

Fishes available in the market may be cultured either in fresh or contaminated water bodies. Heavy metals are one of those contaminants which may cause menace to fish health and thereby affect the health of living beings consuming them. The identification of heavy metal residues in fish samples is a challenging task and may require expensive and sophisticated instruments and testing. This paper investigates visual changes which may be used as benchmark for differentiating between fresh water and heavy metal exposed fishes. The proposed method is an automated non-destructive image processing method for identifying visual changes which can be used to differentiate between controlled (untreated) and heavy metals exposed (treated) fishes. The eye of the fish from digital images is considered as focal tissue that was automatically segmented using the Circular Hough Transform and adaptive intensity thresholding. Post segmentation, a potential feature is identified and transformed into mathematical parameters for classification of a fish sample as fresh or heavy metal exposed water fish. The proposed method can identify and translate the potential visual feature for ease of understanding. The accuracy of the proposed method is high, and computation time elapsed indicates the possibility of using such algorithm for real time detection in related field.