Versatile video interpretation, visualization, and management system

A process for detecting colon cancer by identifying clinical features in a colon, comprising:

• training a computer to perform probabilistic analysis by semi supervised learning from labeled and unlabeled examples of clinical features in video frames containing colonoscopic features;
• obtaining multiple colonoscopy video frames containing colonoscopic features;
• excluding any uninformative video frames;
• applying a probabilistic analysis selected from the group consisting of Hidden Markov Model analysis and conditional random field classifier to five dimensions of relationships between colonoscopic features in temporally or spatially neighboring portions of said video frames;
• wherein said five dimensions of relationships consist of inter-frame relationships, intra-frame relationships, frame quality, anatomical structures, and imaging modalities;
• and
• classifying and annotating any of said colonoscopic features in said video frames that satisfy said probabilistic analysis as clinical features.

A process for detecting and tracking polyps and diverticula in colonoscopic video, comprising:

• pre-processing said video to enhance contrast;
• segmenting said video to identify regions of interest;
• refining said regions of interest by similarity scores in subsequent video frames to determine a final region of interest;
• estimating a trajectory of said final region of interest between video frames in said video.

A process for video spatial synchronization of at least two colonoscopic videos, comprising:

• tagging spatially and temporally coarsely spaced video frames with spatial location information in each video;
• estimating positions of frames subsequent to said tagged video frames in each video;
• and
• registering frames in said videos having most closely matching features.

A device for detecting colon cancer by identifying clinical features in a colon, comprising:

• obtaining means for obtaining multiple colonoscopy video frames containing colonoscopic features;
• excluding means for excluding any uninformative video frames;
• applying means for applying a probabilistic analysis selected from the group consisting of Hidden Markov Model analysis and conditional random field classifier to five dimensions of relationships between colonoscopic features in temporally or spatially neighboring portions of said video frames;
• wherein said five dimensions of relationships consist of inter-frame relationships, intra-frame relationships, frame quality, anatomical structures, and imaging multimodalities;
• classifying and annotating means for classifying and annotating any of said colonoscopic features in said video frames that satisfy said probabilistic analysis as clinical features;
• filtering means for creating sections of said video containing relevant clinical features;
• wherein said probabilistic analysis has been trained by semi supervised learning from labeled and unlabeled examples of clinical features in video containing colonoscopic features;
• storage means for capturing, storing, searching and retrieving clinically relevant video frames;
• feature alert means for automatically interpreting, classifying and annotating said video frames;
• and
• field of view scoring means for scoring field of view of said video frames.