Wavelet transform (WT) is a potential tool for the detection of microcalcifications, an early sign of breast cancer. This article describes the implementation and evaluates the performance of two novel WT-based schemes for the automatic detection of clustered microcalcifications in digitized mammograms. Employing a one-dimensional WT technique that utilizes the pseudo-periodicity property of image sequences, the proposed algorithms achieve high detection efficiency and low processing memory requirements. The detection is achieved from the parent-child relationship between the zero-crossings [Marr-Hildreth (M-H) detector] /local extrema (Canny detector) of the WT coefficients at different levels of decomposition. The detected pixels are weighted before the inverse transform is computed, and they are segmented by simple global gray level thresholding. Both detectors produce 95% detection sensitivity, even though there are more false positives for the M-H detector. The M-H detector preserves the shape information and provides better detection sensitivity for mammograms containing widely distributed calcifications.

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Videos are used often for communicating ideas, concepts, experience, and situations, because of the significant advances made in video communication technology. The social media platforms enhanced the video usage expeditiously. At, present, recognition of a video is done, using the metadata like video title, video descriptions, and video thumbnails. There are situations like video searcher requires only a video clip on a specific topic from a long video. This paper proposes a novel methodology for the analysis of video content and using video storytelling and indexing techniques for the retrieval of the intended video clip from a long duration video. Video storytelling technique is used for video content analysis and to produce a description of the video. The video description thus created is used for preparation of an index using wormhole algorithm, guarantying the search of a keyword of definite length L, within the minimum worst-case time. This video index can be used by video searching algorithm to retrieve the relevant part of the video by virtue of the frequency of the word in the keyword search of the video index. Instead of downloading and transferring a whole video, the user can download or transfer the specifically necessary video clip. The network constraints associated with the transfer of videos are considerably addressed.

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