Automatic knee segmentation using eagle algorithm with multi stochastic objective process

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Publicado en 3C Tecnología. Edición Especial/Special Issue – Noviembre/November 2021



In our living world, Osteoarthritis (OA) is known to be foremost sicknesses which affect the knee region especially affect hoarier people. The reason for OA in a person may be due to ageing, malformed joints, rough cartilage, genetics effects or continuous repetitive stress towards the joint. Magnetic Resonance Imaging (MRI) enacts a vigorous role in the medical field for detecting issues regarding bone structure, cartilage, and meniscus region and tibia bone. Though it provides the details about the bone, it is not useful to detect clearly about the faults in the bone due to many unfavorable conditions like poor segmentation, broken pixels or some other natural issues including the shakes while clicking the image, blurring, etc. Besides, manual calculations have some unexceptional error with partial accuracy. Hence automated segmentation should be implemented for achieving perfection in accuracy and the bone segmentation. In our work, we proposed eagle algorithm as the segmentation method which provides an improved accuracy in contrast with other traditional methods. The performance is measured by the metrics such as thickness, mean and Standard Deviation (SD).


Palabras clave

Pre-Processing, Contrast Enhancement, Stochastic Multi-Objective Process, Levy Walk Random Process, Eagle Algorithm.

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