Cloud architecture is used for maintaining the personal health record and provide symptoms based treatment to the patients. The details of a patient need to be stored in a secured manner. It is to create a cloud storage server for long term storage over the internet. The storage server will act as a database server. Uploaded data stored in the cloud server through proxy re-encryption method. A secured threshold proxy re-encryption server and integrates it with a decentralized erasure code such that a secure distributed storage system. To generate proxy re-encryption key for one-time data access. A proxy server will be created virtually for one time data access. We can achieve the symptoms based treatment by secure Personal Health Record in cloud storage when applying the proposed encryption algorithm.

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Breast cancer is the leading cause of death in women. Early detection and early treatment can significantly reduce the breast cancer mortality. Texture features are widely used in classification problems, i.e., mainly for diagnostic purposes where the region of interest is delineated manually. It has not yet been considered for sonoelastographic segmentation. This paper proposes a method of segmenting the sonoelastographic breast images with optimum number of features from 32 features extracted from three different extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Edge-Based Features. The image undergoes preprocessing by Sticks filter that improves the contrast and enhances the edges and emphasizes the tumor boundary. The features are extracted and then ranked according to the Sequential Forward Floating Selection (SFFS). The optimum number of ranked features is used for segmentation using k-means clustering. The segmented images are subjected to morphological processing that marks the tumor boundary. The overall accuracy is studied to investigate the effect of automated segmentation where the subset of first 10 ranked features provides an accuracy of 79%. The combined metric of overlap, over- and under-segmentation is 90%. The proposed work can also be considered for diagnostic purposes, along with the sonographic breast images.