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Srikanth, B.
- Analysis and Detection of Multi Tumor from MRI of Brain using Advance Adaptive Feature Fuzzy C-means (AAFFCM) Algorithm
Authors
1 Department of CSE, Acharya Nagarjuna University, Guntur - 522 510, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 43 (2016), Pagination:Abstract
Objectives: The objective of the study focused on the detection of the multi-tumor must involves evaluation of the computer-aided diagnosis systems which use image processing as the main tool for detection, therefore, the performance parameters that agree with the inter observers must be used. Methods: Segmentation is a significant feature of medical image dispensation, where Clustering move toward is extensively used in biomedical application mainly for brain multi tumor detection in irregular Magnetic Resonance Images (MRI). The present approach derives an innovative method for brain tumor analysis and detection based on the support vector machine (SVM) and fuzzy c-means algorithms.. No such study is available to detect the multi-tumor. The present approach is to solve that problem and used to detect multi-tumors. Findings: The proposed AAFFCM approach is a hybrid approach which is a combination of fuzzy c-means and SVM algorithms for detecting multi-tumors in brain. A color base segmentation technique so as to uses the k-means clustering system is to path the multi tumor objects in the Magnetic Resonance (MR) brain images. Improvement: In the proposed approach, the MRI is improved by improvement techniques such as difference development, and Mean stretch. The skull striping operation is performed by using Morphology and double-thresh holding technique. By using Matrix, the specific information is removed from the brain image which is called Grey Level Advance Length Matrix (GLALM). After removing the specific information from the brain, SVM algorithm is used to categorize the brain MRI images, which give precise and more effectual importance for categorization of brain MRI.Keywords
AAF2CM, Fuzzy C Means, Grey Level Advance length Matrix, Magnetic Resonance Imaging, Segmentation, SVM.Full Text
- Oxo-Parviflorin, a New Phenanthrapyrone from Vanda parviflora
Authors
1 Department of BS and H, Vignan’s Nirula Institute of Technology and Science, Guntur, IN
2 Department of Chemistry, K.B.N College, Vijayawada, IN
Source
Asian Journal of Research in Chemistry, Vol 4, No 8 (2011), Pagination: 1221-1224Abstract
From the whole plant of Vanda parviflora, a new phenanthrapyrone derivative was isolated. Its structure was elucidated as 2,6-dihydroxy-8-methoxy-9,10-dihydro (4,5-bcd) pyrone on the basis of spectroscopic data. This is the first report of phenanthrapyrone from Vanda parviflora.Keywords
Vanda parviflora, Orchidaceae, Oxo-Parviflorin, Parviflorin, Phenanthrene, 9,10-Dihydro-phenanthra (4,5 bcd) Pyrone.
- Physiological and Molecular Characterization of Three Rice Genotypes with Differential Nitrogen Use Efficiency
Authors
1 ICAR- Indian Institute of Rice Research, Rajendranagar, Hyderabad (Telangana), IN
2 Jawaharlal Nehru Technological University, Hyderabad (Telangana), IN
3 ICAR- Indian Institute of Rice Research, Rajendranagar, Hyderabad (Telangana)
Source
Asian Journal of Bio Science, Vol 11, No 1 (2016), Pagination: 162-171Abstract
Identification and adaptation of rice genotypes with high nitrogen use efficiency (NUE) is a potential approach in optimizing N
requirements, lowering the cost of cultivation and reducing the environmental pollution through the reduction of nitrous oxide
emission in rice. Three rice genotypes differing in their NUE were grown in hydroponics and field conditions and characterized for
physiological, yield parameters and NUE indicators along with expression analysis under low and recommended nitrogen conditions.
Significant variations were observed in 18 physiological, yield parameters and 8 NUE indicators among genotypes confirming the
genotypic variability for the traits under low nitrogen conditions. Reduction of yield related parameters except thousand grain
weight were noted. Correlation analysis of various yield components explains the significance of total biomass for grain yield,
straw yield, N content in grain, straw and nitrogen absorption efficiency. Differential expression of OsNIA2 gene in NUE efficient
genotypes viz., in shoots of GQ25 and ischolar_mains of IR55178 in the present study suggests its possible role in N metabolism and is
encouraging for exploring the possibilities of improving nitrogen use efficiency in rice.