Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed-based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 -w, T2 -w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C -Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO-based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO-based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images.
Landslide is a common natural hazard that usually occurs in mountainous areas. Rapid urban development and high traffic intensity movements have been hampered to a great extent by phenomenon of landslides. In Ghat section, vertical cuttings and steep slopes are induced slope failures. An assessment of landslide hazards is therefore a prerequisite for sustainable development of the hilly region. In the present study, Macro Landslide Hazard Zonation was carried out in the Bodi -Bodimettu ghats section, Western Ghats, Theni district. The slope spreads over an area of about 10.09 sq km encompassing Puliuttu Ar. sub-watershed. The study was made with help of different types of data including Survey of India topographic map, geology map, important inherent factors like lithology, structure, slope morphometry, relative relief, land use/land cover and hydrogeological conditions using Bureau of Indian Standard (BSI 14496 (Part 2):1998) and related thematic maps. Based on the thematic layers, landslide hazard evaluation factor (LHEF) and total estimated hazard (TEHD) were calculated and the macro hazard zonation map was prepared. Landslide Hazard Zonation (LHZ) of the terrain shows that out of 17 facets, facets 1 to 5 and 8 falls under Moderate Hazard zone category and facets 6, 7 and 9 to 17 under the High Hazard zone category. The field study with further analysis for hazard concluded that about 68% of the total area falls in the high hazard zone.
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