Abstract:
An imaging system for detecting targets of interest (TOIs) in multispectral imaging data includes a memory device storing a plurality of instructions embodying the system for detecting TOIs, a processor for receiving the multispectral imaging data and executing the plurality of instructions to perform a method including determining a list of events collocated across images of the multispectral imaging data and labeling each event as one of a TOI or non-TOI.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of Provisional Application No. 61/048,714 filed on Apr. 29, 2008, the content of which is herein incorporated by reference in its entirety. 
     
    
     BACKGROUND 
       [0002]    1. Technical Field 
         [0003]    The present disclosure relates to image analysis, and more particularly to systems and methods for identification of targets in multispectral imaging data. 
         [0004]    2. Discussion of Related Art 
         [0005]    Multispectral fluorescence imaging techniques, such as fluorescence microscopy and bioluminescence, provide a mechanism for visualizing and studying molecular targets both in vitro and in vivo. These optical imaging technologies have several biomedical applications, including the diagnosis and monitoring of disease, studying the effects of drug candidates on target pathologies, and the discovery and development of biomarkers. 
         [0006]    In multispectral fluorescence imaging, multiple targets of interest (TOIs) in a specimen are each specifically labeled with a fluorophore, which is a fluorescent molecule. The specimen is illuminated with light of a specific wavelength(s), which is absorbed by the fluorophores, causing the fluorophores to emit different wavelengths of light (e.g., longer wavelengths). These different wavelengths correspond to a different color than the absorbed light. The illumination light is separated from the weaker emitted fluorescence through the use of an emission filter. Multiple filters may be used to differentiate between the emissions of the fluorophores. The filtered emitted light for each fluorophore is converted into a digital image corresponding to a labeling pattern of the fluorophore within the specimen. The images acquired typically include intensity data, where the intensity of each pixel (e.g., on a scale of black to white) represents a level of fluorescence detected at that point in the specimen. 
         [0007]    In multispectral fluorescence imaging, using W emission filters generates W corresponding images to output one image per fluorophore. Since the image acquisition is typically rapid, the W images may be registered to within a few pixels. 
         [0008]    Labeled TOIs in multispectral fluorescence imaging data emit light at narrow, specific wavelengths that exclude emission from other components in the specimen. Many specimens (including samples of biological origin) frequently contain unpredictable material with which the fluorophores may bind, causing emission that passes through the specific filter, producing spurious intensity in the image. Additionally, some specimens exhibit inherent fluorescence that can be detected at the target emission wavelengths. Such situations could result in a false prediction of the presence of the TOIs. Additionally, several factors such as noise, occlusion, photobleaching, etc., can prevent a TOI from emitting a sufficient amount of light at the detected emission wavelength. 
         [0009]    Therefore, a need exists for a system and method for distinguishing an emission indicating the presence of the TOIs from an emission that does not indicate the presence of the TOIs. 
       BRIEF SUMMARY 
       [0010]    According to an embodiment of the present disclosure, an imaging system for detecting targets of interest (TOIs) in multispectral imaging data includes a memory device storing a plurality of instructions embodying the system for detecting TOIs, a processor for receiving the multispectral imaging data and executing the plurality of instructions to perform a method including determining a list of events collocated across images of the multispectral imaging data and labeling each event as one of a TOI or non-TOI. 
         [0011]    According to an embodiment of the present disclosure, a method for detecting targets of interest (TOIs) in multispectral imaging data, the method includes determining a list of events collocated across images of the multispectral imaging data and labeling each event as one of a TOI or non-TOI. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]    The invention will now be elucidated by reference to the embodiment partially illustrated schematically in the drawings regarding an exemplary medical application scenario using a favorable hardware set-up: 
           [0013]      FIG. 1  is a diagram of a system for analyzing targets of interest in imaging data according to an embodiment of the present disclosure; 
           [0014]      FIG. 2  is a flowchart of a method for detecting targets of interest in imaging data according to an embodiment of the present disclosure 
       
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
       [0015]    According to an embodiment of the present disclosure, a system and method are described for identifying, classifying and counting targets of interest (TOIs) in multispectral fluorescence imaging data. It should be noted that the material described herein can be applied to TOI detection/discrimination in any multichannel image, not just fluorescence imaging. The system and method may be conceptualized as including two parts: an event finder  101  for producing a list of events that are collocated across images and a classifier  102  for determining whether or not each event is a TOI (see  FIG. 1 ). 
         [0016]    Referring to  FIG. 2 , according to an embodiment of the present disclosure, emission events are determined across images collected through the use of different emission filters (e.g., the same structure is labeled with different fluorophores) ( 201 ) and a classified ( 202 ) to determine whether or not these co-localized regions represent TOIs ( 203 ). 
         [0017]    Embodiments of the system and method can distinguish emissions that indicate the presence of the TOIs from emissions that do not indicate the presence of the TOIs. Embodiments of the system and method include components for finding candidate events and classifying the events into true TOIs and false TOIs. Although traditional classification methods are widespread and effective, it is often difficult to adequately train a classifier since most images contain a mix of true/false events (which can be difficult to distinguish by the human eye) for which only an overall positive/negative label is assigned, e.g., obtained independently through a different diagnostic test. 
         [0018]    According to an embodiment of the present disclosure, a method includes an event finder ( 201 ) and a classification routine ( 202 ). 
         [0019]    Referring to  FIGS. 1 and 2  and event finding ( 201 ); an event finder ( 101 ) receives input data, e.g., multispectral fluorescence imaging data including W images, and locates groups of high-intensity pixels that might represent a positive TOI. Event finding is applied separately to each of the W images, acquired with filters at different wavelengths. For purposes of notation, consider a set S of wavelengths (filters) for which images are acquired (i.e., |S|=W) that is indexed by the variable s. 
         [0020]    Referring to  FIG. 3 , to begin the event finding, a median filter with a small kernel (e.g., 3×3) is applied to a current image of the W images to remove stuck pixels, shot noise, etc. ( 301 ). Given this median filtered image, M s , belonging to frequency s, a mean filter with a kernel width exceeding twice the target TOI size (measured in pixels) is applied to produce the filtered image F s  that tracks illumination changes (e.g., caused by distance from the illumination source) and overall background level ( 302 ). A response of the mean filter is subtracted to remove the illumination changes and background levels. A transform is applied to compensate for illumination and provide a rough measure of signal-to-noise (S/N) that reflects human brightness perception ( 303 ). The transformation may be written as: 
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         [0000]    in which the subscript i is used to indicate the value corresponding to pixel i. Note that a small constant (e.g., 1e−10 is added to the denominator to prevent division by zero). 
         [0021]    The values of T s  are now thresholded to produce a binary version of T s  ( 304 ), which is denote as T B   s  by applying the threshold 
         [0000]      Θ=mean( T   8 )+βvariance( T   8 ).   (2) 
         [0022]    Morphological operators of erosion and dilation are then applied to clean each T B   8  ( 305 ). Overlapping connected components of these binary masks across frequencies (i.e., for all T B   8 ) are then considered collocated if the following two conditions are met ( 306 ): 
         [0023]    1. The overlap exceeds percent of the largest connected component. An exemplary value of v=0.25. 
         [0024]    2. None of the connected components in a single channel (wavelength) overlap with more than one connected component in another channel. 
         [0025]    The event finder ( 101 ) outputs a list of labels, which are shared across all wavelength images identifying corresponding connected components. The collection of pixels in each wavelength channel corresponding to the same label is considered to be one event. 
         [0026]    Referring now to classification ( 202 ) and  FIG. 4 , the event finder ( 201 ) located a set of events collocated across wavelength channels. Given this set of events, the classifier ( 102 ) determines if each event either indicates a TOI or represents noise. In order to make this determination, a training set of images is used. Due to the high number of events, manual classification of each event can be infeasible. However, given knowledge that certain sample specimens are “positive” (contain a TOI) or “negative” (do not contain a TOI), based on alternate diagnostic testing of the same specimen, training assumes that every event in a positive image represents a TOI while every event in a negative image does not represent a TOI. According to an embodiment of the present disclosure, given the training assumptions, a classification approach is described herein that tolerates label noise. 
         [0027]    A feature vector is computed for each event by computing a series of measurements for an event across all wavelength images and concatenating these features into a single vector ( 401 ). Examples of features include brightness, blur and entropy. Assume that every training event is represented by a feature vector t and every test event is represented by vector v. A probability, x, is assigned to each vector that represents the probability that this event is a TOI ( 402 ). These probabilities can be assigned to the events in an image as follows: 
         [0028]    1. Compute the inverse covariance matrix, C, of t. 
         [0029]    2. Find the K-nearest neighbors (e.g., K=20) of each x and t, measured by the Mahalanobis distance (using C). 
         [0030]    3. Treat the labels on t as boundary conditions (‘1’ if t i  is from a positive specimen and ‘0’ if t i  is from a negative specimen) and solve for a combinatorial harmonic function, described below, to assign probabilities to each v of being a TOI. 
         [0031]    4. Depending on the confidence tolerance, assign t i  to ‘positive’ or ‘negative’ if x i  exceeds a threshold. 
         [0032]    This procedure was applied to the training/testing of sample specimens infected with Respiratory Syncytial Virus (RSV), where one fluorophore was used to identify total cell count in a sample and another fluorophore was used to identify the presence of the virus. The results obtained were classification rates representing 80% sensitivity (i.e., true positive rate) and &gt;99% specificity (i.e., the false positive rate) of RSV-infected cells. These classification rates mirror the ability of current, manual, methods of counting of cells using a fluorescence microscope. 
         [0033]    Referring to the combinatorial harmonic function, given a set of feature vectors that have been specified as belonging to L image labels, remaining feature vectors can be labeled by a multi-label harmonic potential segmentation method. For an arbitrary L, and an image or volume of arbitrary dimensions, consider a person at every voxel starting to walk randomly across the volume until meeting a labeled feature vector, hereafter a label. The expected percentage of random walkers that first reach a label i are denoted as p i . If the walkers are biased to avoid crossing a sharp image gradient, such as an edge, to reach a neighboring voxel, the probability that a walker starting at a given pixel first strikes label i gives an indication of how strongly that feature vector belongs to label i. Once the set of {p 1 ,p 2 , . . . ,p 1 } is determined for each voxel, that voxel may be assigned to a particular label by choosing the label with the highest probability, the i corresponding to maxi(p i ). 
         [0034]    It is to be understood that the present disclosure may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. In one embodiment, the present disclosure may be implemented in software as an application program tangibly embodied on a program storage device. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. 
         [0035]    Referring to  FIG. 5 , according to an embodiment of the present disclosure, a computer system  501  for identification of targets in multispectral imaging data the present disclosure can comprise, inter alia, a central processing unit (CPU)  502 , a memory  503  and an input/output (I/O) interface  504 . The computer system  501  is generally coupled through the I/O interface  504  to a display  505  and various input devices  506  such as a mouse and keyboard. The support circuits can include circuits such as cache, power supplies, clock circuits, and a communications bus. The memory  503  can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combination thereof. The present disclosure can be implemented as a routine  507  that is stored in memory  503  and executed by the CPU  502  to process the signal from the signal source  508 , e.g., a multispectral fluorescence imaging device inputting imaging data. As such, the computer system  501  is a general purpose computer system that becomes a specific purpose computer system when executing the routine  507  of the present disclosure. 
         [0036]    The computer platform  501  also includes an operating system and micro instruction code. The various processes and functions described herein may either be part of the micro instruction code or part of the application program (or a combination thereof) which is executed via the operating system. In addition, various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device. 
         [0037]    It is to be further understood that, because some of the constituent system components and method steps depicted in the accompanying figures may be implemented in software, the actual connections between the system components (or the process steps) may differ depending upon the manner in which the present disclosure is programmed. Given the teachings of the present disclosure provided herein, one of ordinary skill in the related art will be able to contemplate these and similar implementations or configurations of the present disclosure. 
         [0038]    Having described embodiments for identification of targets in multispectral imaging data, it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in embodiments of the present disclosure that are within the scope and spirit thereof.