Problem statement:In the past few years, immense improvement was obtained in the field of Content-Based Image Retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from images by human experts. Approach: In this study, we present a hybrid approach called Support vector machine combined with relevance feedback for the retrieval of liver diseases from Ultrasound (US) images is introduced. SVM and RF are supervised active learning technique used to improve the effectiveness of the retrieval system. Three kinds of liver diseases are identified including cyst, alcoholic cirrhosis and carcinoma. The diagnosis scheme includes four steps: image registration, feature extraction, feature selection and image retrieval. First the ultrasound images are registered in the database based on the modality. Then the features, derived from first order statistics, gray level co-occurrence matrix and fractal geometry, are obtained from the Pathology Bearing Regions (PBRs) among the normal and abnormal ultrasound images. The Correlation Based Feature Selection (CFS) algorithm selects the certain features for the specific diseases and also reduces dimensionality space for classification. Finally, we implement our hybrid approach for retrieval of specific diseases from the database. Results: This hybrid approach can get the query from user and has retrieved both positive and negative samples from the database, by getting feedback in each round from the radiologist is help to improve the retrieval of correct images. Conclusion:The hybrid approach (SVM+RF) comprises several benefits when compared to existing CBIR for medical system by neural network algorithms. Fractal geometry in feature extraction plays crucial role in ultrasound liver image retrieval. CFS also reduce the dimensionality issue during storage. Image registration plays an important role in the retrieval. It reduces the redundancy of retrieval images and increases the response rate. Getting relevance feedback from physician helps to improve the accuracy of retrieval images from the database.

Due to the rapid development of computer technology, Human Computer Interaction (HCI) techniques have become an indispensable component in our daily life. HCI focuses on the understanding between humans and the computer. Also the protection of password involves different methods and it is being a great challenge to the computer industry. Hand Written Character is one form of human interaction can be used effectively in HCI for static authentication with better password protection. In this paper, the Digital Pen consists of triaxial micro-electromechanical (MEMS) Accelerometer, to capture the activities of the human and to capture motion trajection information from accelerations for recognizing gestures for handwriting. The pen operates in two modes. It can operate as mouse by recognizing the gestures and other mode it can operate as Handwritten Character Recognition for identifying the digits and characters also in this mode it provides static authentication for the systems by recognizing the characters. An Integrated Development Interface is developed using dotnet to recognize the characters.

Spatial analysis of images sensed and captured from a satellite provides less adequate information about a remote location. Hence spectral analysis becomes essential. Hyperspectral image is one of the remotely sensed images, superior to multispectral images in providing spectral information. Target detection is one of the significant requirements in many areas such as military, agriculture etc. Sub pixel target detection, which further divides each pixel of the image into partitions, is possible only with spectral analysis of hyperspectral image. This paper focuses on developing an algorithm for segmenting hyperspectral image using sub pixel target detection followed by Fuzzy C-Means(FCM) clustering technique. Principal Component Analysis (PCA) is the basic step adopted to reduce the high dimensional data of image to low dimensional data. Mixture tuned matched filtering technique is used for sub pixel target detection because it is a combination of linear spectral unmixing and matched filtering and has advantages of both the techniques.