College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Computer science & Sliding mode control. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Feature Extraction and Texture Classification in MRI

Abstract: Automated MRI (Magnetic resonance Imaging) brain tumor segmentation is a difficult task due to the variance and complexity of tumors. In this paper, a statistical structure analysis based tumor segmentation scheme is presented, which focuses on the structural analysis on both tumorous and normal tissues. The basic concept is that local textures in the images can reveal the typical 'regularities' of biological structures. Thus, textural features have been extracted using co-occurrence matrix approach. By the analysis of level of correlation we can reduce the number of features to the only significant component .An artificial neural network and fuzzy c-means are used for classification. This approach is designed to investigate the differences of texture features among macroscopic lesion white matter (LWM), normal appearing white matter (NAWM) in magnetic resonance images (MRI) from patients with tumor and normal white matter (NWM).

Enhancing Distributed Data Storage Security for Cloud Computing Using TPA and AES Algorithm

Abstract: Cloud computing model is very exciting model especially for business peoples. Many business peoples are getting attracted towards cloud computing model because of the features easy to manage, device independent, location independent. But this cloud models comes with many security issues. A business person keeps crucial information on cloud, so security of data is crucial issue as probability of hacking and unauthorised access is there. Also availability is a major concern on cloud. This paper, discusses the file distribution and SHA-1 technique. When file is distributed then data is also segregated into many servers. So here the need of data security arises. Every block of file contains its own hash code, using hash code which will enhance user authentication process, only authorized person can access the data. Here, the data is encrypted using advanced encryption standard, so data is successfully and securely stored on cloud. Third party auditor is used for public auditing. This paper discusses the handling of some security issues like Fast error localization, data integrity, data security. The proposed design allows users to audit the data with lightweight communication and computation cost. Analysis shows that proposed system is highly efficient against malicious data modification attack and server colluding attack. Performance and extensive security analysis shows that proposed systems are provably secure and highly efficient.

Fusion of local and global features for classification of abnormality in mammograms

Abstract: Mammography is the most widely used tool for the early detection of breast cancer. Computer-based algorithms can be developed to improve diagnostic information in mammograms and assist the radiologist to improve diagnostic accuracy. In this paper, we propose a novel computer aided technique to classify abnormalities in mammograms using fusion of local and global features. The objective of this work is to test the effectiveness of combined use of local and global features in detecting abnormalities in mammograms. Local features used in the system are Chebyshev moments and Haralick’s gray level co-occurrence matrix based texture features. Global features used are Laws texture energy measures, Gabor based texture energy measures and fractal dimension. All types of abnormalities namely clusters of microcalcifications, circumscribed masses, spiculated masses, architectural distortions and ill-defined masses are considered. A support vector machine classifier is designed to classify the samples into abnormal and normal classes. It is observed that combined use of local and global features has improved classification accuracy from 88.75% to 93.17%.