Summarization from Medical Documents: A Survey <s> Multimedia summarization <s> We describe a new approach to the detection and classification of scene breaks in video sequences. Our method can detect and classify a variety of scene breaks, including cuts, fades, dissolves and wipes, even in sequences involving significant motion. We detect the appearance of intensity edges that are distant from edges in the previous frame. A global motion computation is used to handle camera or object motion. The algorithms we propose withstand compression artifacts such as those introduced by JPEG and MPEG, even at very high compression rates. Experimental evidence demonstrates that our method can detect and classify scene breaks that are difficult to detect with previous approaches. An initial implementation runs at approximately 2 frames per second on a Sun workstation. <s> BIB001 </s> Summarization from Medical Documents: A Survey <s> Multimedia summarization <s> This work aims at developing innovative algorithms and tools for summarizing echocardiogram videos. Specifically, we summarize the digital echocardiogram videos by temporally segmenting them into the constituent views and representing each view by the most informative frame. For the segmentation we take advantage of the well-defined spatio-temporal structure of the echocardiogram videos. Two different criteria are used: presence/absence of color and the shape of the region of interest (ROI) in each frame of the video. The change in the ROI is due to different modes of echocardiograms present in one study. The representative frame is defined to be the frame corresponding to the end-diastole of the heart cycle. To locate the end-diastole we track the ECG of each frame to find the exact time the time-marker on the ECG crosses the peak of the R-wave. The corresponding frame is chosen to be the key-frame. The entire echocardiogram video can be summarized into either a static summary, which is a storyboard type of summary and a dynamic summary, which is a concatenation of the selected segments of the echocardiogram video. To the best of our knowledge, this is the first automated system for summarizing the echocardiogram videos based on visual content. <s> BIB002 </s> Summarization from Medical Documents: A Survey <s> Multimedia summarization <s> A BSTRACT : Medical information is available from a variety of new online resources. Given the number and diversity of sources, methods must be found that will enable users to quickly assimilate and determine the content of a document. Summarization is one such tool that can help users to quickly determine the main points of a document. Previous methods to automatically summarize text documents typically do not attempt to infer or define the content of a document. Rather these systems rely on secondary features or clues that may point to content. This paper describes text summarization techniques that enable users to focus on the key content of a document. The techniques presented here analyze groups of similar documents in order to form a content model. The content model is used to select sentences forming the summary. The technique does not require additional knowledge sources; thus the method should be applicable to any set of text documents. <s> BIB003
The work presented in BIB003 is part of the PERSIVAL project mentioned above. In their study they are concerned with echocardiograms (ECGs). ECGs are usually videotaped for archival purposes and recently they have started to be transcribed into a digital format, which helps clinicians, and facilitates the task of summarizing them. Summarizing an ECG, and video in general as seen in the work of  , involves extracting the most interesting video frames, which are called key frames, which enable the user to easily navigate through the ECGs and view their essential parts. For BIB002 summarizing an ECG involves two things: parsing the ECG and selecting the key frames. The aim of the parser is to temporally segment the sequences of the video into smaller units, which are called shots. A shot is a sequence of frames in which the camera is uninterrupted. In the context of ECG videos, a shot corresponds to a single position and angle of the ultrasound transducer. The method they use for the parsing is a special case of the algorithm presented in BIB001 . The next step is the key-frame selection, which extracts the most informative (important) frames in the sequence of the video. After mentioning several methods for extracting key-frames, they conclude that for the context of ECGs key-frames are "the local extrema of the cardiac periodic expansive-contractive motion", since "the time at which the cardiac motion changes from expansive to contractive corresponds to the enddiastole and the time at which the motion changes from contractive to expansive corresponds to endsystole". Having performed the above two tasks, they create two summaries which they call static and dynamic. • Static summary. This summary, in essence, is constituted from the selection of the extracted keyframes, and it is useful for browsing the content of the echo video. • Dynamic summary. This summary, also called clinical summary among the clinicians, is a concatenation of the small extracted sequences of the video. They chose to extract one (or more, based on the needs of the clinicians) cycle of the heart motion, known also as R-R cycle. By joining those segments of videos they create the dynamic summary. [60], follow a similar approach to BIB002 in order to parse the video stream into physical units. Then video group detection, scene detection and clustering strategies are used to mine the video content structure. Various visual and audio feature processing techniques are utilized to detect some semantic cues, such as slides, face and speaker changes, etc. within the video, and these detection results are joined together to mine three types of events from the detected video scenes (presentations by doctors or experts on video topics, clinical operations to present details of diseases, and dialogs between doctors and patients). Based on mined video content structure and event information, a scalable video skimming and summarization tool, ClassMiner, has been constructed to visualize the video overview and help users access video content. Their system utilizes a four layer video skimming, where levels 4 through 1 consist of representative shots of clustered scenes, all scenes, all groups, and all shots of the video, respectively.