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Nature of medical images makes segmentation a difficult problem. Often the human anatomy in medical images blend with its surrounding due to low contrast thus making changes in the edge gray level too low and difficult to detect. Medical images are also noisy and fuzzy. The main challenge of this paper is to detect the outer cortical (OC) and inner cortical (IC) of the bone for the measurement of the inner diameter (ID) and the outer diameter (OD) of the cortical. The fact that bone radiographs are often grainy and inconsistent in gray level, prompt us to compare the performance of three fuzzy edge detection algorithms: Gaussian Rule based Fuzzy (GRBF), Robust Fuzzy Complement (RFC) and Classical Fuzzy Heuristics (CFH). To conduct a systematic evaluation of this fuzzy edge detector, we proposed to combine visual observation and number of edge pixels detected. The experimental data used as test samples consist of a hundred and thirty non dominant hand radiograph of normal Kelantanese female between the ages of twenty to seventy. Only metacarpal 2, 3 and 4 are considered. The results prove to be quite promising because most edges of both IC and OC are well detected.