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Atherosclerosis is a chronic inflammatory disease primarily caused by lipid and bad cholesterol deposits in the arterial wall. These accumulations lead to the formation of monocyte cells, whose multiplication in the arterial wall and transformation to macrophages initiate the cholesterol plaque-formation process. The massive accumulation of cholesterol in these macrophages promote the plaques progression. Because of its ranking as the major cause of cardiovascular deaths, an early detection of such a disease is imposed. In this context, a novel algorithm is advanced, whereby, atherosclerotic lesions imaged by fluorescence microscopy can be effectively detected. The newly designed method involves an image preprocessing step, a segmentation step, along with a merging step combining the entirety of obtained segments. The reached results are further refined for the purpose of reducing over and under segmentation as well as eliminating the misclassified and unconnected pixels. Once achieved, the final segmentation associated quality is evaluated. Actually, the attained experimental results prove the efficiency of our proposed method in terms of precision, recall, and f-score.

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