Mahua Bhattacharya

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In present work we have introduced nonlinear affine registration method to incorporate the anatomic body deformation. The present technique has been developed for registration of section of human brain using CT and MR modalities. Present study related to image registration of different modality imaging is based on 2-D/2-D affine registration technique.(More)
We present a non-linear 2-D/2-D affine registration technique for MR and CT modality images of section of human brain. Automatic registration is achieved by maximization of a similarity metric, which is the correlation function of two images. The proposed method has been implemented by choosing a realistic, practical transformation and optimization(More)
The problem of feature selection consists of finding a significant feature subset of input training as well as test patterns that enable to describe all information required to classify a particular pattern. In present paper we focus in this particular problem which plays a key role in machine learning problems. In fact, before building a model for feature(More)
Medical image fusion has been used to derive the useful complimentary information from multimodal images. The prior step of fusion is registration or proper alignment of test images for accurate extraction of detail information. For this purpose, the images to be fused are geometrically aligned using mutual information (MI) as similarity measuring metric(More)
The proposed chapter describes the need of data security and content protection in the modern health care system. A digital watermarking technique is used as a strong and secure tool to achieve ultimate security. In this chapter the authors discuss some existing watermarking techniques and also describe some new types of data hiding techniques using(More)
Present work is an in depth study to detect flames in video by processing the data captured by an ordinary camera. Previous vision based methods were based on color difference, motion detection of flame pixel and flame edge detection. This paper focuses on optimizing the flame detection by identifying gray cycle pixels nearby the flame, which is generated(More)