Amin Gharipour

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In high-throughput applications, accurate segmentation of biomedical images can be considered as an important step for recognizing cells that have the phenotype of interest. In this paper, while conventional fuzzy clustering is not able to implement the local and global spatial information, a novel spatial fuzzy clustering cell image segmentation algorithm(More)
A. A. Besalatpour, S. Ayoubi, M. A. Hajabbasi, A. Yousefian Jazi, and A. Gharipour Department of Soil Science, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran Department of Soil Science, Isfahan University of Technology, Isfahan, Iran Department of Biomedical Engineering, Seoul National University, Jongno-gu, Seoul, Korea School of Information and(More)
This paper presents an integration framework for image segmentation. The proposed method is based on Fuzzy c-means clustering (FCM) and level set method. In this framework, firstly Chan and Vese’s level set method (CV) and Bayes classifier based on mixture of density models are utilized to find a prior membership value for each pixel. Then, a supervised(More)
BACKGROUND The aim of this study is to present an objective method based on support vector machines (SVMs) and gravitational search algorithm (GSA) which is initially utilized for recognition the pattern among risk factors and hypertension (HTN) to stratify and analysis HTN's risk factors in an Iranian urban population. METHODS This community-based and(More)
In this study a new image segmentation framework which combines the Fuzzy c means clustering and the level set method is presented. Using this framework, the well-known Chan and Vese's level set technique and classical Bayes classifier are employed to obtain a prior membership value for each pixel based on region information. Next, a novel clustering model(More)
Complex systems are complex, evolutionary, and dynamical system. One general method to predict such systems is use the previous and most recently behavior of a system to predict its future changes. The main advantage of this method is the ability to predict the behavior of systems without analytical prediction rules. In this situation, decision makers are(More)
Fluorescence microscopy image segmentation is a central task in high-throughput applications such as protein expression quantification and cell function investigation. In this paper, a multiple kernel local level set segmentation algorithm is introduced as a framework for fluorescence microscopy cell image segmentation. In this framework, a new local(More)
BACKGROUND Selenoprotein P (SeP) is involved in transporting selenium from the liver to target tissues. Because SeP confers protection against disease by reducing chronic oxidative stress, the present study aimed to assess the level of SeP in the serum of patients with metabolic syndrome (MetS) with a history of cardiovascular disease (CVD). METHODS A(More)
The accurate segmentation of biomedical images has become increasingly important for recognizing cells that have the phenotype of interest in biomedical applications. In order to improve the conventional deterministic segmentation models, this paper proposes a novel graph-cut cell image segmentation algorithm based on Bayes theorem. There are two(More)