The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.
A development of automatic location identification and tracking system for visually impaired/ challenged person is a very challenging task in an indoor environment. In this paper, the comprehensive study of different feature detection and matching techniques namely, Minimum Eigenvalue (MinEigen) algorithm, Harris–Stephens (Harris) algorithm, Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Binary Robust Invariant Scalable Keypoints (BRISK) and Maximally Stable Extremal Regions (MSER) is presented. These algorithms are employed to detect and match the features of an image and retrieve the best matched image. Based on our experiments, we compare those algorithms on parameters such as sum of square difference (SDD), precision, recall, number of detected, matched features and processing time. Empirically, we have found that SURF algorithm produce minimum SSD score to achieve best matching. The MSER and MinEign algorithm extracts high and low number of features respectively. In respect of processing time, BRISK takes maximum and FAST method takes minimum time when compared to the other algorithms.
In recent years, mortality rate with high-grade tumor has been increased significantly especially with glioblastoma (GBM) brain tumor while compared to other malignant brain tumor. Here, the amount of dead cells accommodated with the tumor tissue in GBM brain tumor play a vital task and necessitate an earlier diagnosis of malignancy with the GBM tumor. It inspires to implement new automatic diagnosis system which detects the dead cells and tumor tissue with the GBM brain tumor, such that the survival rate of the diseased can easily be prognosis by the Radiologist and Neurosurgeon. The main objective of this article is to detect the amount of dead cells with respect to tumor tissue associated with the GBM brain tumor which desires the impact factor of the brain tumor. In this framework, initially, the new contrast enhancement modality is incorporated to enhance the gray information over the dead cells and the tumor tissue with the T1-weighted MRI GBM brain tumor. In this enhancement, the edges of the tumor cells and its dead cells are magnified efficiently. As the noises and outliers with MR image is unpredictable and it experiences the variable amount of noises over the local window, the contextual information over each pixel of the image is adaptively modified with respect to the amount of noise over local window using adaptive contextual clustering. The performance evaluation of the framework is investigated which exhibits the overwhelming result compared to conventional detection techniques.
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