In general, medical images acquired at insufficient lighting conditions are suffering from low contrast issues which are inadequate for image analysis steps. One standard solution to improve the image contrast is to change its intensity distribution with the help of an image histogram. The histogram‐based contrast enhancement methods treat the images as regions rather than objects which would be more useful for applications like brain image enhancement. In this paper, a Fuzzy and Spline based Histogram Equalization (FSDHE) is proposed to perform contrast enhancement with medical images. The proposed FSDHE method partitions the image into connected components, and the type of components are identified with a fuzzy membership function. The dynamic histogram equalization is applied to each component individually. The equalized sub‐histograms are combined to drive the global histogram, which is inconsistent as dynamic histogram equalization treated the intensity range for each connected component differently. Hence, a spline‐based histogram smoothing is proposed here in this research work. The equalized intensity mapping is received as control points for the polynomial curve, and a smooth intensity transformation is interpolated as a spline curve. The proposed FSDHE model is analyzed with MRI‐brain image dataset of 3064 images, which consists of both benign and malignant cases. The contrast enhancement performance of the proposed FSDHE method is quantified by various measures like Absolute Mean Brightness Error (AMBE), Peak Signal to Noise Ratio (PSNR), Contrast (C), Weber Contrast (WC), Entropy, Hausdorff Distance (HD) and Texture Preservation (TP) measures. The performance of the FSDHE method is compared against with other histogram equalization methods, and the results indicate that the FSDHE method achieves better quality measures of 3.1401, 30.5499, 21.5486, 0.7779, 4.0252, 0.2777, and 0.7836 for the seven‐performance metrics.

中文翻译：

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基于模糊和样条的动态直方图均衡化，可增强脑部图像的对比度

通常，在不足的照明条件下获取的医学图像遭受对比度不足的问题，这不足以进行图像分析步骤。一种改善图像对比度的标准解决方案是借助图像直方图更改其强度分布。基于直方图的对比度增强方法将图像视为区域而不是对象，这对于诸如脑部图像增强的应用程序将更为有用。在本文中，提出了一种基于模糊和样条的直方图均衡（FSDHE）来增强与医学图像的对比度。所提出的FSDHE方法将图像划分为连接的分量，并使用模糊隶属度函数识别分量的类型。动态直方图均衡化分别应用于每个组件。组合均衡的子直方图以驱动全局直方图，这与动态直方图均衡对每个连接的分量的强度范围进行不同处理时不一致。因此，本研究工作在此提出了基于样条的直方图平滑。接收到均衡强度映射作为多项式曲线的控制点，并插入平滑强度变换作为样条曲线。利用3064张图像的MRI脑图像数据集分析了所提出的FSDHE模型，该数据集包括良性和恶性病例。所提出的FSDHE方法的对比增强性能可通过各种方法进行量化，例如绝对平均亮度误差（AMBE），峰值信噪比（PSNR），对比度（C），韦伯对比度（WC），熵，Hausdorff距离（HD）和纹理保留（TP）措施。将FSDHE方法的性能与其他直方图均衡方法进行了比较，结果表明FSDHE方法在七个性能指标上实现了更好的质量度量，分别为3.1401、30.5499、21.5486、0.7779、4.0252、0.2777和0.7836。