| Unified system and method for animal behavior characterization in home cages using video analysis -> Monitor Keywords |
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Unified system and method for animal behavior characterization in home cages using video analysisRelated Patent Categories: Animal Husbandry, Animal Controlling Or Handling (e.g., Restraining, Breaking, Training, Sorting, Conveying, Etc.)Unified system and method for animal behavior characterization in home cages using video analysis description/claimsThe Patent Description & Claims data below is from USPTO Patent Application 20070175406, Unified system and method for animal behavior characterization in home cages using video analysis. Brief Patent Description - Full Patent Description - Patent Application Claims
CROSS-REFERENCE TO RELATED APPLICATION [0001] This application is a continuation of U.S. patent application Ser. No. 10/698,044, now U.S. Pat. No. ______, which is a continuation-in-part of U.S. patent application Ser. No. 09/718,374, now U.S. Pat. No. 6,678,413. The subject matter of the related applications is hereby incorporated by reference in its entirety. BACKGROUND OF THE INVENTION [0003] Animals, for example mice or rats, are used extensively as human models in the research of drag development; genetic functions; toxicology research; understanding and treatment of diseases; and other research applications. Despite the differing lifestyles of humans and animals, for example mice, their extensive genetic and neuroanatomical homologies give rise to a wide variety of behavioral processes that are widely conserved between species. Exploration of these shared brain functions will shed light on fundamental elements of human behavioral regulation. Therefore, many behavioral test experiments have been designed on animals like mice and rats to explore their behaviors. These experiments include, but not limited to, home cage behaviors, open field locomotion experiments, object recognition experiments, a variety of maze experiments, water maze experiments, and freezing experiments for conditioned fear. [0004] Animal's home cage activity patterns are important examination item on the general health list of animals, such as mice and rats. It provides many important indications of whether the animal's health status is normal or abnormal. Home cage behaviors are best observed by videotaping several 24-hour periods in the animal housing facility, and subsequent scoring of the videotape by two independent observers. However, this observation has rarely been done until our inventions came into play, due to the instability in long term human observation, the time consumed, and the huge costs associated with the observation. [0005] As discussed, all these apparatus and experiments use, in many cases, human observation of videotapes of the experiment sessions, resulting in inaccuracy, subjectivity, labor-intensive, and thus expensive experiments. Some automating software provides rudimentary and basic parameters, relying on tracking animal as a point in space, generating experiment results that are inaccurate and can not meet the demands for advanced features. Besides, each system software module works for only a specific experiment, resulting in potential discrepancy in the results across different systems due to differences in software algorithms used. [0006] All the observations of these behavioral experiments use video to record experiment processes and rely on human observations. This introduces the opportunity to utilize the latest technologies development in computer vision, image processing, and digital video processing to automate the processes and achieve better results, high throughput screening, and lower costs. Many of these experiments are conducted with observations performed from top view, that is, observation of the experiments from above the apparatus is used to obtain needed parameters. This also provides an opportunity to unify the approaches to observe and analyze these experiments' results. SUMMARY OF THE INVENTION [0007] There are strong needs for automated systems and software that can automate the measurements of the experiments mentioned above, provide the measurements of meaningful complex behaviors and new revealing parameters that characterize animal behaviors to meet post-genomic era's demands, and obtain consistent results using novel approaches. [0008] The invention relates generally to behavior analysis of animal objects. More particularly, one aspect of the invention is directed to monitoring and characterization of behaviors under specific behavioral paradigm experiments, including home cage behavior paradigms, locomotion or open field paradigm experiment, object recognition paradigm experiments, variety of maze paradigm experiments, water maze paradigm experiments, freezing paradigm experiments for conditioned fear, for an animal, for example, a mouse or a rat, using video analysis from a top view image or side view image, or the integration of both views. [0009] A revolutionary approach is invented to automatically measure animal's home cage activity patterns. This approach consists of defining a unique set of animal's, such as mice or rats, behavior category. This category includes behaviors like rearing, walking, grooming, eating, drinking, jumping, hanging, etc. Computer systems are designed and implemented that can produce digital video files of animal's behaviors in a home cage in real time or off-line mode. Software algorithms are developed to automatically understand and analyze the animal's behaviors in those video files. This analysis is based on the premise that the entire animal body, body parts, related color information, and their dynamic motion are taken advantage of in order to provide the measurement of complex behaviors and novel parameters. [0010] In general, the present invention is directed to systems and methods for finding patterns of behaviors and/or activities of an animal using video. The invention includes a system with a video camera connected to a computer in which the computer is configured to automatically provide animal identification, animal motion tracking (for moving animal), animal shape, animal body parts, and posture classification, and behavior identification. Thus, the present invention is capable of automatically monitoring a video image to identify, track and classify the actions of various animals and their movements. The video image may be provided in real time from a camera and/or from a storage location. The invention is particularly useful for monitoring and classifying mice or rats behavior for testing drugs and genetic mutations, but may be used in a number of surveillance or other applications. [0011] In one embodiment the invention includes a system in which an analog/digital video camera and a video record/playback device (e.g., VCR) are coupled to a video digitization/compression unit. The video camera may provide a video image containing an animal to be identified. The video digitization/compression unit is coupled to a computer that is configured to automatically monitor the video image to identify, track and classify the actions of the animal and its movements over time within a sequence of video session image frames. The digitization/compression unit may convert analog video and audio into, for example, MPEG or other formats. The computer may be, for example, a personal computer, using either a Windows platform or a Unix platform, or a Macintosh computer and compatible platform. The computer is loaded and configured with custom software programs (or equipped with firmware) using, for example, MATLAB or C/C++ programming language, so as to analyze the digitized video for animal identification and segmentation, tracking, and/or behavior/activity characterization. This software may be stored in, for example, a program memory, which may include ROM, RAM, CD ROM and/or a hard drive, etc. In one variation of the invention the software (or firmware) includes a unique background subtraction method which is more simple, efficient, and accurate than those previously known. [0012] In operation, the system receives incoming video images from either the video camera in real time or pre-recorded from the video record/playback unit. If the video is in analog format, then the information is converted from analog to digital format and may be compressed by the video digitization/compression unit. The digital video images are then provided to the computer where various processes are undertaken to identify and segment a predetermined animal from the image. In a preferred embodiment the animal is a mouse or rat in motion with some movement from frame to frame in the video, and is in the foreground of the video images. In any case, the digital images may be processed to identify and segregate a desired (predetermined) animal from the various frames of incoming video. This process may be achieved using, for example, background subtraction, mixture modeling, robust estimation, and/or other processes. [0013] The shape and location of the desired animal is then tracked from one frame or scene to another frame or scene of video images. The body parts of the animal such as head, mouth, tail, ear, abdomen, lower back, upper back, forelimbs, and hind limbs, are identified by novel approaches through body contour segmentation, contour segment classification, and relaxation labeling. Next, the changes in the shapes, locations, body parts, and/or postures of the animal of interest may be identified, their features extracted, and classified into meaningful categories, for example, vertical positioned side view, horizontal positioned side view, vertical positioned front view, horizontal positioned front view, moving left to right, etc. Then, the shape, location, body parts, and posture categories may be used to characterize the animal's activity into one of a number of pre-defined behaviors. For example, if the animal is a mouse or rat, some pre-defined normal behaviors may include sleeping, eating, drinking, walking, running, etc., and pre-defined abnormal behavior may include spinning vertical, jumping in the same spot, etc. The pre-defined behaviors may be stored in a database in the data memory. The behavior may be characterized using, for example, approaches such as rule-based label analysis, token parsing procedure, and/or Hidden Markov Modeling (HMM). Further, the system may be constructed to characterize the object behavior as new behavior and particular temporal rhythm. [0014] In another embodiment of the invention, there are multiple cameras taking video images of experiment cages that contain animals. There is at least one cage, but as many as the computer computing power allows, say four (4) or sixteen (16) or even more, can be analyzed. Each cage contains at least one animal or multiple animals. The multiple cameras may be taking video from different points of views such as one taking video images from the side of the cage, or one taking video images from the top of the cage. When video images are taken of multiple cages and devices containing one or multiple animals, and are analyzed for identifying these animals' behaviors, high throughput screening is achieved. When video images taken from different points of views, for example, one from the top view and another from the side view, are combined to identify animal's behaviors, integrated analysis is achieved. [0015] In another preferred embodiment directed toward video analysis of animals such as mice or rats, the system operates as follows. As a preliminary matter, normal postures and behaviors of the animals are defined and may be entered into a Normal Paradigm Parameters, Postures and Behaviors database. In analyzing, in a first instant, incoming video images are received. The system determines if the video images are in analog or digital format and input into a computer. If the video images are in analog format they are digitized and may be compressed, using, for example, an MPEG digitizer/compression unit. Otherwise, the digital video image may be input directly to the computer. Next, a background may be generated or updated from the digital video images and foreground objects detected. Next, the foreground animal features are extracted. Also, body parts such as head, tail, ear, mouth, forelimbs, hind limbs, abdomen, and upper and lower back, are identified. Two different methods are pursuing from this point, depending on different behavior paradigms. In one method, the foreground animal shape is classified into various categories, for example, standing, sitting, etc. Next, the foreground animal posture is compared to the various predefined postures stored in the database, and then identified as a particular posture or a new (unidentified) posture. Then, various groups of postures and body parts are concatenated into a series to make up a foreground animal behavior compared against the sequence of postures, stored in for example a database in memory, that make up known normal or abnormal behaviors of the animal. The abnormal behaviors are then identified in terms of known abnormal behavior, new behavior and/or daily rhythm. In another method, behavioral processes and events are detected, and behavior parameters are calculated. These behaviors 5 parameters give indications to animal health information related to learning and memory capability, anxiety, and relations to certain diseases. [0016] In one variation of the invention, animal detection is performed through a unique method of background subtraction. First, the incoming digital video signal is split into individual images (frames) in real-time. Then, the system determines if the background image derived from prior incoming video needs to be updated due to changes in the background image or a background image needs to be developed because there was no background image was previously developed. If the background image needs to be generated, then a number of frames of video image, for example 20, will be grouped into a sample of images. Then, the system creates a standard deviation map of the sample of images. Next, the process removes a bounding box area in each frame or image where the variation within the group of images is above a predetermined threshold (i.e., where the object of interest or moving objects are located). Then, the various images within the sample less the bounding box area are averaged. Final background is obtained by averaging 5-10 samples. This completes the background generation process. However, often the background image does not remain constant for a great length of time due to various reasons. Thus, the background needs to be dynamically recalculated periodically as above or it can be recalculated by keeping track of the difference image and note any sudden changes. The newly dynamically generated background image is next subtracted from the current video image(s) to obtain foreground areas that may include the object of interest. [0017] Next, the object identification/detection process is performed. First, regions of interest (ROI) are obtained by identifying areas where the intensity difference generated from the subtraction is greater than a predetermined threshold, which constitute potential foreground object(s) being sought. Classification of these foreground regions of interest will be performed using the sizes of the ROIs, distances among these ROIs, threshold of intensity, and connectedness, to thereby identify the foreground objects. Next, the foreground object identification/detection process may be refined by adaptively learning histograms of foreground ROIs and using edge detection to more accurately identify the desired object(s). Finally, the information identifying the desired foreground object is output. The process may then continue with the tracking and/or behavior characterization step(s). [0018] Development activities have been completed to validate various scientific definitions of mouse behaviors and to create novel digital video processing algorithms for mouse tracking and behavior recognition, which are embodied in a software and hardware system according to the present invention. An automated method for analysis of mouse behavior from digitized 24 hours video has been achieved using the present invention and its digital video analysis method for object identification and segmentation, tracking, and classification. Several different methods and their algorithms, including Background Subtraction, Probabilistic approach with Expectation-Maximization, and Robust Estimation to find parameter values by best fitting a set of data measurements and results proved successful. [0019] The need for sensitive detection of novel phenotypes of genetically manipulated or drug-administered mice demands automation of analyses. Behavioral phenotypes are often best detected when mice are unconstrained by experimenter manipulation. Thus, automation of analysis of behavior in a known environment, for example a home cage, would be a powerful tool for detecting phenotypes resulting from gene manipulations or drug administrations. Automation of analysis would allow quantification of all behaviors as they vary across the daily cycle of activity. Because gene defects causing developmental disorders in humans usually result in changes in the daily rhythm of behavior, analysis of organized patterns of behavior across the day may also be effective in detecting phenotypes in transgenic and targeted mutant mice. The automated system may also be able to detect behaviors that do not normally occur and present the investigator with video clips of such behavior without the investigator having to view an entire day or long period of mouse activity to manually identify the desired behavior. [0020] The systematically developed definition of mouse behavior that is detectable by the automated analysis according to the present invention makes precise and quantitative analysis of the entire mouse behavior repertoire possible for the first time. The various computer algorithms included in the invention for automating behavior analysis based on the behavior definitions ensure accurate and efficient identification of mouse behaviors. In addition, the digital video analysis techniques of the present invention improves analysis of behavior by leading to: (1) decreased variance due to non-disturbed observation of the animal; (2) increased experiment sensitivity due to the greater number of behaviors sampled over a much longer time span than ever before possible; and (3) the potential to be applied to all common normative behavior patterns, capability to assess subtle behavioral states, and detection of changes of behavior patterns in addition to individual behaviors. [0021] The entire behavioral repertoire of individual mice in their home cage was categorized using successive iterations by manual videotape analysis. These manually defined behavior categories constituted the basis of automatic classification. Classification criteria (based on features extracted from the foreground object such as shape, position, movement) were derived and fitted into a decision tree (DT)classification algorithm. The decision tree could classify almost 7000 sample features into 8 different postures classes with accuracy over 94%. A set of HMMs have been built and used to classify the classified postures identified by the DT and yields an almost perfect mapping from input posture to output behaviors in mouse behavior sequences. [0022] The invention may identify some abnormal behavior by using video image information (for example, stored in memory) of known abnormal animals to build a video profile for that behavior. For example, video image of vertical spinning while hanging from the cage top was stored to memory and used to automatically identify such activity in mice. Further, abnormalities may also result from an increase in any particular type of normal behavior. Detection of such new abnormal behaviors may be achieved by the present invention detecting, for example, segments of behavior that do not fit the standard profile. The standard profile may be developed for a particular strain of mouse whereas detection of abnormal amounts of a normal behavior can be detected by comparison to the statistical properties of the standard profile. Continue reading about Unified system and method for animal behavior characterization in home cages using video analysis... 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