Adaptive fractional differential approach and its application to medical image enhancement. (July 2015)

• Record Type: Journal Article
• Authors: Li, Bo; Xie, Wei
• Is Part Of: Computers & electrical engineering. Volume 45(2015)
• Page Start: 324
• Page End: 335
• Publication Date: 2015-07
• Subjects: Fractional differential -- Adaptive -- Medical image enhancement -- Gradient -- Otsu -- Order; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.02.013
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: We use the improved Otsu algorithm to segment medical images. The function of order v is constructed based on the features of image areas. The fractional differential order of each pixel is obtained by the function. Each pixel of medical image is processed by adaptive fractional differential mask. The edge of medical image is enhanced while the weak texture is preserved. Abstract: This paper presents a new medical image enhancement method that adjusts the fractional order according to the dynamic gradient feature of the entire image. The presented method can extract the edges of an image accurately and enhance them while preserving smooth areas and weak textures; these improvements can be particularly helpful to doctors' diagnoses. The primary contribution of this paper is the Adaptive Fractional Differential Algorithm (AFDA), which uses the improved Otsu algorithm to segment the edges, textures and smooth areas of images. This algorithm allows the optimal fractional order of each pixel to be obtained using an adaptive fractional differential function constructed based on the area feature of image. As a result, the image can be enhanced adaptively. Experimental results show that for medical images, AFDA shows better image enhancement than other methods by making edges clearer and textures richer.