Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI

Abstract

Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.

Multivariate Analysis in Pediatric Brain Tumor

“…Among them, 13 articles reported multivariate analysis methods applied to pediatric brain tumor classification (or segmentation) [3][4][5][6][7][8][9][10][11][12][13][14][15]. Table 1 provides a summary of these papers.…”

“…In addition, Ahmed et al [8] and Iftekharuddin et al [9] used shape, intensity and texture features extracted from MRI images for tumor segmentation and classification (sensitivity 100%, specificity 75%) [9]. For special brain tumors such as optic pathway gliomas, automatic tumor segmentation was achieved by using prior tumor location tissue characteristics and intensity information, and by classifying tumor into internal components [10].…”

“…However, some imaging features such as texture features are not straightforward nor standard. They can be 2D or 3D, based on grey-level co-occurrence matrix or grey-level run-length matrix [14], or fractal dimension or multi-fractional Brownian motion [8][9]. Study has shown that computed with 2D texture features, 3D texture features improved tumor classification performance [14].…”

Review on Brain Tumor Segmentation and Classification Techniques

“…They further study the selective fusion of these features for improved PF tumor segmentation. Their result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data [7]. have proposed Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm, in which they propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.…”

Feature Selection on Segmented Image using Automatic Subjective Optimality Model

“…Nevertheless, its disadvantages comprise over-segmentation and compassion to forged edges. Intensity based image segmentation [17] is completed to manage with the disadvantages declared above and the examination of k-means clustering [18] with Gaussian distribution is discussed in [19]. In this work, a schematic procedure for image segmentation based on different sets of features and the automatic subjective optimality model is presented for feature selection.…”