科研之友
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摘要
In 2011, Food and Drug Administration (FDA or USFDA) certified the automated cell morphology (ACM) systems for medical use in USA. The brightness, contrast and color appearance are all factors that play a major role in the diagnosis of many blood diseases. Accordingly, enhancement of pathological microscopic image (PMI) is a crucial step to increase the efficiency of computer assisted software. Some of the previous PMI enhancement methods neglected the illumination information and others used a reference image for template matching. These methods worked under strictly controlled conditions. In this paper, a robust technique is proposed for pathological images enhancement based on neutrosophic similarity score scaling. The color image is separated into three channels, and then each channel is represented in the neutrosophic domain into three subsets T, I and F. Neutrosophic similarity score (NSS) under multi-criteria are computed and used to scale the input image. The main contribution of this paper is that red, green and blue coefficients derived from the neutrosophic calculations lead directly to an adaptive pathology image enhancement and take into consideration many color image quality (IQ) parameters like illumination, contrast and color balance where it does not focus on a single IQ parameter like previous methods. In the experiments, several microscopic image quality measurements are utilized to evaluate the proposed method's performance versus the previous enhancement techniques. The experimental results demonstrate that our proposed system is promising with low complexity, adaptive with different resolution and lighting conditions. This provides the basis for automatic medical diagnosis and further processing of medical images.
- 出版日期2018
