Abstract
This paper proposes an improved region-based active contour model for segmenting magnetic resonance imaging (MRI) images of brain tuberculosis by combining a global energy fitting term and a local energy fitting term. First, a global energy fitting term is utilized to extract global image information, which guides the evolving curve globally and approximates the image intensity inside and outside the contour. Second, a local energy fitting term is proposed to describe the intensity inhomogeneity based on the local intensity variance and the adaptive image difference. Third, an improved Fuzzy C-Means (FCM) clustering method is applied to pre-segment the MRI images to automatically track the approximate location of brain tuberculosis and provide the initial contour for the hybrid model segmentation. By combining the global and local weighting functions, a hybrid region-based model is defined. Experiments demonstrate that the proposed model provides initialization of the contours automatically and offers superior segmentation performance for brain tuberculosis MRI medical images with intensity inhomogeneity.
Highlights
Intracranial tuberculosis (TB) is a serious type of central nervous system tuberculosis caused by the hematogenous spread of mycobacterium tuberculosis and accounts for 1.5%-3.2% of all tuberculosis-related deaths[1],[2], ranking 11th in its disability rate and 13th in its fatality rate
Diagnosis of central nervous system (CNS) TB is crucial to receiving appropriate treatment, which can reduce the risk of morbidities and mortality
EVALUATION METRICS To evaluate the performance of the proposed model, the segmentation results for the brain tuberculosis magnetic resonance imaging (MRI) images were objectively evaluated using three metrics to measure the accuracy of the segmentation results: the Dice Similarity Coefficient (DSC), the Jaccard Similarity Coefficient (JSC) and the Conformity Similarity Coefficient (CSC)
Summary
Intracranial tuberculosis (TB) is a serious type of central nervous system tuberculosis caused by the hematogenous spread of mycobacterium tuberculosis and accounts for 1.5%-3.2% of all tuberculosis-related deaths[1],[2], ranking 11th in its disability rate and 13th in its fatality rate. The involvement of the central nervous system (CNS) is the reason that this is one of the most serious forms of this infection and is responsible for its high mortality and morbidity[3],[4]. Diagnosis of CNS TB is crucial to receiving appropriate treatment, which can reduce the risk of morbidities and mortality. Routine diagnostic techniques are usually implemented on the stage of early diagnosis. They usually require cultures and immunological tests of tissue and biofluids, which are time-consuming and may delay definitive management[6]
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