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.
A lot of studies confirmed the seriousness of breast cancer as the most tumors lethal to women worldwide. Early detection and diagnosis of breast cancer are of great importance to increase treatment options and patients' survival rate. Ultrasound is one of the most frequently used methods to detect and diagnosis breast tumor due to its harmlessness and inexpensiveness. However, problems were found in the tumor diagnosis and classification as benign and malign on ultrasound image for its vagueness, such as speckle noise and low contrast. In this paper, we propose a novel breast tumor classification algorithm that combines texture and morphologic features based on neutrosophic similarity score. Then, a supervised feature selection technique is employed to reduce feature space. Finally, a support vector machine (SVM) classifier is employed to prove the discrimination power of the proposed features set. The proposed system is validated by 112 cases (58 malign, 54 benign). The experimental results show that such features set is promising and 99.1% classification accuracy is achieved.
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