Development of medical image segmentation techniques has become one of the most important challenges in many applications that employ computers-based medical image analysis techniques. However, most of current-existing medical image segmentation techniques still have poor efficiency and complexity in calculations. A new technique for X-ray bone image segmentation has been presented in this paper. The proposed technique is designed to generate a highly-efficient quality of bone extraction from X-ray bone image at low calculation burdens. The proposed approach begins with obtaining the inverse of the original X-ray bone image then applying bounded-product operator and fuzzy controller to control the contrast of the inverse image. At the same time, an adaptive thresholding of the gradient magnitude of the original X-ray bone image is achieved to detect the edges of the bone regions. Accordingly, the bone edges are integrated with the contrasted image to give the bone segmented image. To ensure the efficient quality of the proposed algorithm, more than sixty-one of X-ray bone images were tested using much vision and statistical investigations. Then, evaluations employed several measures such as Dice similarity coefficient index, Confusion Matrix, Accuracy, Precision, Sensitivity, Specificity, and processing speed. Furthermore, results obtained using the proposed were compared to those of conventional image segmentation techniques (such as Watershed segmentation, Otsu-thresholding, K-means, and fuzzy C-means). Comparison results demonstrated the superiority of the proposed technique over other conventional techniques in both of quality and processing speed. All obtained results were obtained using MATLAB R20014a over Windows XP with processing speed 2 GHz. The high efficiency and processing speed of the proposed technique makes it such a promising solution to be implemented in many real applications.

中文翻译：

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基于模糊逻辑和边缘检测技术的X射线骨骼图像自动分割高效技术

在采用基于计算机的医学图像分析技术的许多应用中，医学图像分割技术的发展已成为最重要的挑战之一。然而，现有的大多数医学图像分割技术在计算效率和复杂度方面仍然很差。本文提出了一种用于 X 射线骨骼图像分割的新技术。所提出的技术旨在以低计算负担从 X 射线骨骼图像中生成高效的骨骼提取质量。所提出的方法首先获得原始 X 射线骨骼图像的逆，然后应用有界乘积算子和模糊控制器来控制逆图像的对比度。同时，对原始 X 射线骨骼图像的梯度幅度进行自适应阈值化，以检测骨骼区域的边缘。因此，骨骼边缘与对比图像结合以给出骨骼分割图像。为了确保所提出算法的有效质量，使用大量视觉和统计调查测试了 61 多张 X 射线骨骼图像。然后，评估采用了若干度量，例如 Dice 相似系数指数、混淆矩阵、准确度、精确度、灵敏度、特异性和处理速度。此外，使用所提出的结果获得的结果与传统的图像分割技术（如分水岭分割、大津阈值、K 均值和模糊 C 均值）进行了比较。比较结果表明，所提出的技术在质量和处理速度方面均优于其他传统技术。所有获得的结果都是在 Windows XP 上使用 MATLAB R20014a 获得的，处理速度为 2 GHz。所提出的技术的高效率和处理速度使其成为在许多实际应用中实现的有希望的解决方案。