Tumor segmentation in brain MRI using a fuzzy approach with class center priors
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RESEARCH
Open Access
Tumor segmentation in brain MRI using a fuzzy approach with class center priors Moumen T El-Melegy1* and Hashim M Mokhtar2
Abstract This paper proposes a new fuzzy approach for the automatic segmentation of normal and pathological brain magnetic resonance imaging (MRI) volumetric datasets. The proposed approach reformulates the popular fuzzy c-means (FCM) algorithm to take into account any available information about the class center. The uncertainty in this information is also modeled. This information serves to regularize the clusters produced by the FCM algorithm thus boosting its performance under noisy and unexpected data acquisition conditions. In addition, it also speeds up the convergence process of the algorithm. Experiments using simulated and real, both normal and pathological, MRI volumes of the human brain show that the proposed approach has considerable better segmentation accuracy, robustness against noise, and faster response compared with several well-known fuzzy and non-fuzzy techniques reported in the literature. Keywords: Prior information; MRI segmentation; Fuzzy c-means; Fuzzy algorithms; Brain MRI; Tumor segmentation
1 Introduction Magnetic resonance imaging (MRI) of the brain is often used to monitor tumor response to treatment process. The segmentation of the brain tumor from the magnetic resonance images is important in medical diagnosis because it provides information associated to anatomical structures as well as potential abnormal tissues necessary to treatment planning and patient follow-up. It can also be helpful for general modeling of pathological brains and the construction of pathological brain atlases [1]. One example is to analyze and estimate quantitatively the growth process of brain tumors, and to assess the response to treatment and in guiding appropriate therapy in serial studies [2,3]. In spite of numerous efforts and promising results in the medical imaging community, accurate and reproducible segmentation and characterization of abnormalities are still a challenging and difficult task because of the variety of the possible shapes, locations and image intensities of various types of tumors. This task involves various disciplines including medicine, MRI physic, radiologist's perception, and image analysis based on intensity and shape.
* Correspondence: [email protected] 1 Electrical Engineering Department, Assiut University, Assiut 71516, Egypt Full list of author information is available at the end of the article
Brain tumor segmentation process consists of separating the different tumor tissues, such as solid tumor, edema, and necrosis from the normal brain tissues, such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Although manual segmentation by qualified professionals remains superior in quality to automatic methods, it has two drawbacks. The first drawback is that producing manual segmentations or semi-automatic segmentations is extremely time-consuming, with higher accuracies on more finely detailed volumes demandi
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