B. Srikanth ¹, E. Sreenivasa Reddy ¹

Affiliations
1 Department of CSE, Acharya Nagarjuna University, Guntur - 522 510, Andhra Pradesh, IN

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.

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Y. Jhansi ¹, E. Sreenivasa Reddy ²

Affiliations
1 GITAM University, Visakhapatnam - 530045, Andhra Pradesh, IN
2 ANU College of Engineering, Guntur - 522510, Andhra Pradesh, IN

Abstract

Objectives: Image retrieval is system software for browsing, examining and retrieving images from a large database of images. Images and sketches do not share numerous common modalities. Hence, Sketch to Image Retrieval is a tedious task in image processing. Sketch image retrieval focuses on the hand-drawn query and retrieves the similar images from a large database which is useful for further processing.Methods/Statistical Analysis:In the, most of the traditional/conventional image processing techniques considered edges and outlines for image retrieval. In this paper, a new methodology is developed by fusion of Edge Histogram Descriptors, Histogram of oriented gradients, Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF). In the proposed model first feature Extraction is carried out and Euclidian distance is calculated amongst the query sketch and innovative image. Formerly the feature vectors are provided to the score level fusion stage, and then they obtained results are optimized. For optimization, Fish Swarm Optimization (FSO) is employed in the proposed method. Findings: The performance of the proposed method is evaluated through Benchmark sketch image database. Also, the attained results are compared with the existing evolutionary algorithm Genetic Algorithm (GA). Application/ Improvement: The experimental results showed that the projected method with FSO yields better results than GA.

Keywords

EHD, Fish Swarm Optimization, Genetic Algorithm, HOG, Score Level Fusion,SIFT, SURF.

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