COMPUTER-IMPLEMENTED METHOD FOR EVALUATING AN IMAGE DATA SET OF AN IMAGED REGION, EVALUATION DEVICE, IMAGING DEVICE, COMPUTER PROGRAM AND ELECTRONICALLY READABLE STORAGE MEDIUM

Abstract

A computer-implemented method for evaluating an image data set of an imaged region comprises: determining, from the image data set, at least two processed data sets having different image data content; applying a first sub-algorithm, of an evaluation algorithm, to a first of at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets; applying a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets; determining quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, wherein the third sub-algorithm uses both the first intermediate result and the second intermediate result as input data.

Claims

1. A computer-implemented method for evaluating an image data set of an imaged region, wherein, from the image data set, different processed data sets having different image data content are determinable by image processing, and quantitative evaluation result data describing at least one of at least one dynamic feature or at least one static feature of the imaged region is determined by applying an evaluation algorithm, the method comprising: determining, from the image data set, at least two processed data sets having different image data content; applying a first sub-algorithm, of the evaluation algorithm, to a first of the at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets; applying a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets; and determining the quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, the third sub-algorithm using both the first intermediate result and the second intermediate result as input data.

2. The computer-implemented method according to claim 1, wherein the image data set is a multi-energy computed tomography data set, and at least one of the at least two processed data sets is determined at least one of based on a material decomposition or as a monoenergetic image.

3. The computer-implemented method according to claim 2, further comprising: acquiring the image data set using at least one of a source-based or a detector-based multi-energy computed tomography.

4. The computer-implemented method according to claim 1, wherein the image data set is an angiography data set and at least one of the at least two processed data sets is selected from a virtual non-contrast image, an iodine concentration image, a functional image, a monoenergetic image, a virtual non-calcium image, or a virtual non-iodine image.

5. The computer-implemented method according to claim 1, wherein the first intermediate result describes a segmentation result regarding multiple segmented features, and the third sub-algorithm assigns data of the second intermediate result to segmented features to yield quantitative segmented feature-specific evaluation results.

6. The computer-implemented method according to claim 5, wherein the segmented features are anatomical features including at least one of (i) vessels or vessel segments of a vessel tree or (ii) organs or organ segments.

7. The computer-implemented method according to claim 1, wherein the first intermediate result includes a segmented vessel tree, the third sub-algorithm performs at least one fluid flow simulation in the segmented vessel tree to determine at least one fluid flow parameter as an evaluation result, and the at least one fluid flow simulation is at least partly parametrized using the second intermediate result.

8. The computer-implemented method according to claim 7, wherein the segmented vessel tree is a blood vessel tree, the fluid is blood and the at least one fluid flow includes a fractional flow reserve.

9. The computer-implemented method according to claim 1, wherein at least a part of the first intermediate result and at least a part of the second intermediate result are used as quantitative input data to at least one disease value estimation of the third sub-algorithm.

10. The computer-implemented method according to claim 1, wherein the third sub-algorithm determines at least one two-dimensional output image visualizing the quantitative evaluation result data.

11. The computer-implemented method according to claim 10, wherein at least one of an orientation, a viewpoint, a shown imaged region portion of the at least one two-dimensional output image based on the second of the at least two processed data sets, or the second intermediate result is chosen based on the first intermediate result.

12. An evaluation device for evaluating an image data set of an imaged region, wherein, from the image data set, different processed data sets having different image data content are determinable by image processing, the evaluation device comprising: a first interface to receive the image data set; an image processor to determine, from the image data set, at least two processed data sets having different image data content; an evaluation unit to determine quantitative evaluation result data describing at least one of at least one dynamic feature or at least one static feature of the imaged region by applying an evaluation algorithm; a second interface to provide the quantitative evaluation result data; and wherein the evaluation unit includes a first sub-unit to apply a first sub-algorithm, of the evaluation algorithm, to a first of the at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets, a second sub-unit to apply a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets, and a third sub-unit to determine the quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, the third sub-algorithm using both the first intermediate result and the second intermediate result as input data.

13. An imaging device with a control device comprising: the evaluation device according to claim 12.

14. A non-transitory computer-readable storage medium storing a computer program that, when executed by at least one processor at an evaluation device, causes the evaluation device to perform the method of claim 1.

15. An evaluation device to evaluate an image data set of an imaged region, the evaluation device comprising: a memory storing computer-executable instructions; and at least one processor configured to execute the computer-executable instructions to cause the evaluation device to determine, from the image data set, at least two processed data sets having different image data content, apply a first sub-algorithm, of an evaluation algorithm, to a first of the at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets, apply a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets, and determine quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, the third sub-algorithm using both the first intermediate result and the second intermediate result as input data, and the quantitative evaluation result data describing at least one of at least one dynamic feature or at least one static feature of the imaged region.

16. The computer-implemented method according to claim 3, wherein the acquiring acquires the image data set using a counting x-ray detector.

17. The computer-implemented method according to claim 4, wherein the functional image is a perfusion image.

18. The computer-implemented method according to claim 5, wherein the first intermediate result includes a segmented vessel tree, the third sub-algorithm performs at least one fluid flow simulation in the segmented vessel tree to determine at least one fluid flow parameter as an evaluation result, and the at least one fluid flow simulation is at least partly parametrized using the second intermediate result.

19. The computer-implemented method according to claim 18, wherein the segmented vessel tree is a blood vessel tree, the fluid is blood and the at least one fluid flow includes a fractional flow reserve.

20. The computer-implemented method according to claim 5, wherein at least a part of the first intermediate result and at least a part of the second intermediate result are used as quantitative input data to at least one disease value estimation of the third sub-algorithm.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Other objects and features of the present invention will become apparent from the following detailed description considered in conjunction with the accompanying drawings. The drawings, however, are only principle sketches designed solely for the purpose of illustration and do not limit the present invention. The drawings show:

[0050] FIG. 1 a flowchart of a general embodiment of a method according to one or more example embodiments of the present invention,

[0051] FIG. 2 the functional structure of an evaluation device according to one or more example embodiments of the present invention, and

[0052] FIG. 3 a schematic view of an imaging device according to one or more example embodiments of the present invention.

DETAILED DESCRIPTION

[0053] In the following, embodiments of the present invention are described with respect to a multi-energy computed tomography angiography data set as image data set to be evaluated. However, also other image data sets, from which processed data sets having different image content can be derived, can be used. The image data set of these embodiments has been acquired in detector-based multi energy computed tomography by using a counting x-ray detector. That is, all image data have been acquired in one single acquisition, such that derived processed data sets are all registered to each other.

[0054] FIG. 1 shows a general flowchart of a method according to one or more example embodiments of the present invention. After the image data set one has been provided, in particular received via a first interface, in step S1, multiple processed data sets all having different image data content are derived from the image data set by image processing. For example, by using material decomposition, as principally known in the state of the art, virtual non-contrast images, iodine concentration images, functional images, virtual non-calcium images, virtual non-iodine images and the like can be determined. Additionally, or alternatively, monoenergetic images relating to certain energy intervals, for example defined energy slots or bins of the counting x-ray detector, may be determined. Each processed data set 2, 3 determined in this manner forms input data to at least one first or at least one second sub-algorithm of an evaluation algorithm configured to determine quantitative evaluation result data, for example calcium scores, FFR, perfused blood volume information and the like. In this embodiment, a first processed data set 2 is determined as input to a first sub-algorithm and a second processed data set 3 is determined as input data for a second sub-algorithm. However, this example is not limiting, and, as indicated by the dots 4, a larger number of first and/or second processed data sets 2, 3 and/or first and/or second sub-algorithms may be employed, in particular also in a stacked manner.

[0055] In a step S2, the first sub-algorithm is applied to the first processed data set 2, while in a step S3, the second sub-algorithm of the evaluation algorithm is applied to the second processed data set 3. In this manner, first and second intermediate results 4, 5 are determined. These intermediate results 4, 5 now both serve as input data for a third sub-algorithm of the evaluation algorithm, which is executed in step S4 to yield the quantitative evaluation result data 6. Additionally, the output data of the third sub-algorithm and hence the evaluation algorithm formed by the first sub-algorithm, the second sub-algorithm and the third sub-algorithm, and indicated as reference number 7 may also comprise at least one two-dimensional output image 8, in particular series of output images 8, visualizing or explaining the quantitative evaluation result data 6.

[0056] In this manner, from the same image data set 1, multiple processed data sets 2, 3, each showing different image data content, are derived, intermediate results 4, 5 are determined therefrom and together used as input data for a third sub-algorithm in step S4 to provide additional information and improved quality of the evaluation result data 6 and the output images 9. Processed data sets 2, 3 are already registered, such that no additional registration process is required. The different processed data sets can be understood as different representations of the image data set, each including different pieces of information which, put together, allow to provide an improved and reliable evaluation result.

[0057] In a first concrete embodiment, the first processed data set 2 may be a contrasted low-energy representation, from which, by the first sub-algorithm, as the first intermediate result 4, segmentation and labelling information regarding a vessel tree and/or organs in the imaged region of a patient is determined. In coronary angiography, the first intermediate result 4 thus describes the position and orientation of the heart and its valves as well as the lumen, course and labelling of the coronary arteries. As a second processed data set 3, a non-contrasted virtual non-iodine image is determined. Here, the second sub-algorithm provides, as second intermediate result 5, identification and quantification of calcium in the imaged region. The third sub-algorithm uses both intermediate results 4, 5 as input and determines correctly quantified calcium values correctly assigned to coronary vessels and/or heart valves. The output images 8 may be determined in a valve- and/or heart-oriented view, which view orientation and the corresponding positions may be determined from the first intermediate result. For example, a stack of two-dimensional MPR images may be determined along the long axis or the short axis of the heart.

[0058] In another embodiment or additionally, the or an additional second processed data set 3 may be a functional image, for example a late enhancement functional representation showing iodine, that is contrast agent, concentration in tissue, in particular the myocardium. From this functional representation, the second sub-algorithm may determine a perfused blood volume information as a second intermediate result 5, such that, after combination by the third sub-algorithm, for example perfused blood volume information may be shown overlaid over the myocardium in output images 8 in a heart-oriented view, while blood volume perfusion values may be quantitatively determined for different sections of the myocardium.

[0059] Furthermore, using a low energy monoenergetic image as the first processed data set 2 and a high-energy monoenergetic image as the second processed data set 3, anatomical landmarks and calcium information may be derived as first intermediate result 4 and the position and orientation of a stent or stent graft as intermediate result 5. Put together by the third sub-algorithm, a quantitative and visualized description of stent or stent graft occlusion by calcium can be derived in the manner of looking into the stent or stent graft.

[0060] In an especially advantageous concrete embodiment, the first processed data set 2 may be an iodine image, from which a segmented vessel tree is determined as first intermediate result 4. The second processed data set 2 is a low-energy monoenergetic image, from which further anatomical features may be segmented such that a set of parameters relevant for the simulation of fluid flow in the vessel tree, for example the myocardium mass, may be determined. The third sub-algorithm then performs a simulation of a blood flow in the vessel tree, which is parameterized using the second intermediate result 5. In this manner, at least one fluid flow parameter may be determined as evaluation result data, preferably at least the FFR (fractional flow reserve). In such embodiments, more than two processed data sets 2, 3 may be employed, for example by additionally using a virtual non-iodine image to detect calcifications and also take these into account in the simulation.

[0061] As a final concrete example, the first and second intermediate results 4, 5 may also be combined to determine at least one disease value using a respective disease value estimation in the third sub-algorithm. The disease value information may, in particular, comprise a trained function, such that, for example, a machine learning-based disease burden estimation is possible. In other examples, plaque quantification and classification may be performed.

[0062] But also generally, any of the first, second and third sub-algorithms may comprise trained functions, as already discussed above.

[0063] FIG. 2 shows the functional structure of an evaluation device 9 according to one or more example embodiments of the present invention. The evaluation device 9 comprises a first interface 10 for receiving the image data set 1. In an image processing unit 11, the processed data sets 2, 3 may be determined according to step S1. An evaluation unit 12 is provided for applying the evaluation algorithm 7 and comprises three subunits 13, 14 and 15, wherein the first subunit 13 executes the first sub-algorithm to determine the intermediate result 4, as described with regard to step S2, and the second subunit 14 executes the second sub-algorithm to determine the second intermediate result 5, as described with regard to step S3. Of course, as discussed above, multiple first and/or second subunits 13, 14 may also be provided and/or stacked structure of subunits 13, 14 and 15 may be present if the principles described herein are multiply used.

[0064] In the third subunit 15, the third sub-algorithm is performed to yield the quantitative evaluation result data 6 and optionally the at least one output image 8 described with respect to step S4. The evaluation result data 6 and the output images 8 may be provided via a second interface 16 of the evaluation device 9.

[0065] The evaluation device 9 may further comprise a storage device or memory 17 for storing data temporally or permanently for later retrieval, for example image data sets 1, processed data sets 2, 3, intermediate results 4, 5, evaluation result data 6 and output images 9.

[0066] FIG. 3 schematically shows an imaging device 19 according to the current invention, in this case a computed tomography device. The imaging device 19 comprises a gantry 19 having a patient opening 20, into which a patient 21 may be introduced using a patient table 22. An acquisition assembly comprises an x-ray source 23 and an x-ray detector 24, in this case a counting x-ray detector 24, and may be rotated around the opening 20 and hence the patient to acquire projection images using different projection angles, from which an image data set 1 can be reconstructed.

[0067] The operation of the imaging device 18 is controlled by a control device 25, which, in this case, also comprises an evaluation device 9 according to one or more example embodiments of the present invention.

[0068] It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, components, regions, layers, and/or sections, these elements, components, regions, layers, and/or sections, should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and/or,” includes any and all combinations of one or more of the associated listed items. The phrase “at least one of” has the same meaning as “and/or”.

[0069] Spatially relative terms, such as “beneath,” “below,” “lower,” “under,” “above,” “upper,” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as “below,” “beneath,” or “under,” other elements or features would then be oriented “above” the other elements or features. Thus, the example terms “below” and “under” may encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly. In addition, when an element is referred to as being “between” two elements, the element may be the only element between the two elements, or one or more other intervening elements may be present.

[0070] Spatial and functional relationships between elements (for example, between modules) are described using various terms, including “on,” “connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitly described as being “direct,” when a relationship between first and second elements is described in the disclosure, that relationship encompasses a direct relationship where no other intervening elements are present between the first and second elements, and also an indirect relationship where one or more intervening elements are present (either spatially or functionally) between the first and second elements. In contrast, when an element is referred to as being “directly” on, connected, engaged, interfaced, or coupled to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., “between,” versus “directly between,” “adjacent,” versus “directly adjacent,” etc.).

[0071] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a,” “an,” and “the,” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “and/or” and “at least one of” include any and all combinations of one or more of the associated listed items. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list. Also, the term “example” is intended to refer to an example or illustration.

[0072] It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

[0073] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0074] It is noted that some example embodiments may be described with reference to acts and symbolic representations of operations (e.g., in the form of flow charts, flow diagrams, data flow diagrams, structure diagrams, block diagrams, etc.) that may be implemented in conjunction with units and/or devices discussed above. Although discussed in a particularly manner, a function or operation specified in a specific block may be performed differently from the flow specified in a flowchart, flow diagram, etc. For example, functions or operations illustrated as being performed serially in two consecutive blocks may actually be performed simultaneously, or in some cases be performed in reverse order. Although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of operations may be re-arranged. The processes may be terminated when their operations are completed, but may also have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0075] Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments. The present invention may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.

[0076] In addition, or alternative, to that discussed above, units and/or devices according to one or more example embodiments may be implemented using hardware, software, and/or a combination thereof. For example, hardware devices may be implemented using processing circuity such as, but not limited to, a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a System-on-Chip (SoC), a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. Portions of the example embodiments and corresponding detailed description may be presented in terms of software, or algorithms and symbolic representations of operation on data bits within a computer memory. These descriptions and representations are the ones by which those of ordinary skill in the art effectively convey the substance of their work to others of ordinary skill in the art. An algorithm, as the term is used here, and as it is used generally, is conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of optical, electrical, or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0077] It should be borne in mind that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, or as is apparent from the discussion, terms such as “processing” or “computing” or “calculating” or “determining” of “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device/hardware, that manipulates and transforms data represented as physical, electronic quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[0078] In this application, including the definitions below, the term ‘module’ or the term ‘controller’ may be replaced with the term ‘circuit.’ The term ‘module’ may refer to, be part of, or include processor hardware (shared, dedicated, or group) that executes code and memory hardware (shared, dedicated, or group) that stores code executed by the processor hardware.

[0079] The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules that are connected via interface circuits. For example, multiple modules may allow load balancing. In a further example, a server (also known as remote, or cloud) module may accomplish some functionality on behalf of a client module.

[0080] Software may include a computer program, program code, instructions, or some combination thereof, for independently or collectively instructing or configuring a hardware device to operate as desired. The computer program and/or program code may include program or computer-readable instructions, software components, software modules, data files, data structures, and/or the like, capable of being implemented by one or more hardware devices, such as one or more of the hardware devices mentioned above. Examples of program code include both machine code produced by a compiler and higher level program code that is executed using an interpreter.

[0081] For example, when a hardware device is a computer processing device (e.g., a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a microprocessor, etc.), the computer processing device may be configured to carry out program code by performing arithmetical, logical, and input/output operations, according to the program code. Once the program code is loaded into a computer processing device, the computer processing device may be programmed to perform the program code, thereby transforming the computer processing device into a special purpose computer processing device. In a more specific example, when the program code is loaded into a processor, the processor becomes programmed to perform the program code and operations corresponding thereto, thereby transforming the processor into a special purpose processor.

[0082] Software and/or data may be embodied permanently or temporarily in any type of machine, component, physical or virtual equipment, or computer storage medium or device, capable of providing instructions or data to, or being interpreted by, a hardware device. The software also may be distributed over network coupled computer systems so that the software is stored and executed in a distributed fashion. In particular, for example, software and data may be stored by one or more computer readable recording mediums, including the tangible or non-transitory computer-readable storage media discussed herein.

[0083] Even further, any of the disclosed methods may be embodied in the form of a program or software. The program or software may be stored on a non-transitory computer readable medium and is adapted to perform any one of the aforementioned methods when run on a computer device (a device including a processor). Thus, the non-transitory, tangible computer readable medium, is adapted to store information and is adapted to interact with a data processing facility or computer device to execute the program of any of the above mentioned embodiments and/or to perform the method of any of the above mentioned embodiments.

[0084] Example embodiments may be described with reference to acts and symbolic representations of operations (e.g., in the form of flow charts, flow diagrams, data flow diagrams, structure diagrams, block diagrams, etc.) that may be implemented in conjunction with units and/or devices discussed in more detail below. Although discussed in a particularly manner, a function or operation specified in a specific block may be performed differently from the flow specified in a flowchart, flow diagram, etc. For example, functions or operations illustrated as being performed serially in two consecutive blocks may actually be performed simultaneously, or in some cases be performed in reverse order.

[0085] According to one or more example embodiments, computer processing devices may be described as including various functional units that perform various operations and/or functions to increase the clarity of the description. However, computer processing devices are not intended to be limited to these functional units. For example, in one or more example embodiments, the various operations and/or functions of the functional units may be performed by other ones of the functional units. Further, the computer processing devices may perform the operations and/or functions of the various functional units without sub-dividing the operations and/or functions of the computer processing units into these various functional units.

[0086] Units and/or devices according to one or more example embodiments may also include one or more storage devices. The one or more storage devices may be tangible or non-transitory computer-readable storage media, such as random access memory (RAM), read only memory (ROM), a permanent mass storage device (such as a disk drive), solid state (e.g., NAND flash) device, and/or any other like data storage mechanism capable of storing and recording data. The one or more storage devices may be configured to store computer programs, program code, instructions, or some combination thereof, for one or more operating systems and/or for implementing the example embodiments described herein. The computer programs, program code, instructions, or some combination thereof, may also be loaded from a separate computer readable storage medium into the one or more storage devices and/or one or more computer processing devices using a drive mechanism. Such separate computer readable storage medium may include a Universal Serial Bus (USB) flash drive, a memory stick, a Blu-ray/DVD/CD-ROM drive, a memory card, and/or other like computer readable storage media. The computer programs, program code, instructions, or some combination thereof, may be loaded into the one or more storage devices and/or the one or more computer processing devices from a remote data storage device via a network interface, rather than via a local computer readable storage medium. Additionally, the computer programs, program code, instructions, or some combination thereof, may be loaded into the one or more storage devices and/or the one or more processors from a remote computing system that is configured to transfer and/or distribute the computer programs, program code, instructions, or some combination thereof, over a network. The remote computing system may transfer and/or distribute the computer programs, program code, instructions, or some combination thereof, via a wired interface, an air interface, and/or any other like medium.

[0087] The one or more hardware devices, the one or more storage devices, and/or the computer programs, program code, instructions, or some combination thereof, may be specially designed and constructed for the purposes of the example embodiments, or they may be known devices that are altered and/or modified for the purposes of example embodiments.

[0088] A hardware device, such as a computer processing device, may run an operating system (OS) and one or more software applications that run on the OS. The computer processing device also may access, store, manipulate, process, and create data in response to execution of the software. For simplicity, one or more example embodiments may be exemplified as a computer processing device or processor; however, one skilled in the art will appreciate that a hardware device may include multiple processing elements or processors and multiple types of processing elements or processors. For example, a hardware device may include multiple processors or a processor and a controller. In addition, other processing configurations are possible, such as parallel processors.

[0089] The computer programs include processor-executable instructions that are stored on at least one non-transitory computer-readable medium (memory). The computer programs may also include or rely on stored data. The computer programs may encompass a basic input/output system (BIOS) that interacts with hardware of the special purpose computer, device drivers that interact with particular devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, etc. As such, the one or more processors may be configured to execute the processor executable instructions.

[0090] The computer programs may include: (i) descriptive text to be parsed, such as HTML (hypertext markup language) or XML (extensible markup language), (ii) assembly code, (iii) object code generated from source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. As examples only, source code may be written using syntax from languages including C, C++, C#, Objective-C, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5, Ada, ASP (active server pages), PHP, Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, and Python®.

[0091] Further, at least one example embodiment relates to the non-transitory computer-readable storage medium including electronically readable control information (processor executable instructions) stored thereon, configured in such that when the storage medium is used in a controller of a device, at least one embodiment of the method may be carried out.

[0092] The computer readable medium or storage medium may be a built-in medium installed inside a computer device main body or a removable medium arranged so that it can be separated from the computer device main body. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium is therefore considered tangible and non-transitory. Non-limiting examples of the non-transitory computer-readable medium include, but are not limited to, rewriteable non-volatile memory devices (including, for example flash memory devices, erasable programmable read-only memory devices, or a mask read-only memory devices); volatile memory devices (including, for example static random access memory devices or a dynamic random access memory devices); magnetic storage media (including, for example an analog or digital magnetic tape or a hard disk drive); and optical storage media (including, for example a CD, a DVD, or a Blu-ray Disc). Examples of the media with a built-in rewriteable non-volatile memory, include but are not limited to memory cards; and media with a built-in ROM, including but not limited to ROM cassettes; etc. Furthermore, various information regarding stored images, for example, property information, may be stored in any other form, or it may be provided in other ways.

[0093] The term code, as used above, may include software, firmware, and/or microcode, and may refer to programs, routines, functions, classes, data structures, and/or objects. Shared processor hardware encompasses a single microprocessor that executes some or all code from multiple modules. Group processor hardware encompasses a microprocessor that, in combination with additional microprocessors, executes some or all code from one or more modules. References to multiple microprocessors encompass multiple microprocessors on discrete dies, multiple microprocessors on a single die, multiple cores of a single microprocessor, multiple threads of a single microprocessor, or a combination of the above.

[0094] Shared memory hardware encompasses a single memory device that stores some or all code from multiple modules. Group memory hardware encompasses a memory device that, in combination with other memory devices, stores some or all code from one or more modules.

[0095] The term memory hardware is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium is therefore considered tangible and non-transitory. Non-limiting examples of the non-transitory computer-readable medium include, but are not limited to, rewriteable non-volatile memory devices (including, for example flash memory devices, erasable programmable read-only memory devices, or a mask read-only memory devices); volatile memory devices (including, for example static random access memory devices or a dynamic random access memory devices); magnetic storage media (including, for example an analog or digital magnetic tape or a hard disk drive); and optical storage media (including, for example a CD, a DVD, or a Blu-ray Disc). Examples of the media with a built-in rewriteable non-volatile memory, include but are not limited to memory cards; and media with a built-in ROM, including but not limited to ROM cassettes; etc. Furthermore, various information regarding stored images, for example, property information, may be stored in any other form, or it may be provided in other ways.

[0096] The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general purpose computer to execute one or more particular functions embodied in computer programs. The functional blocks and flowchart elements described above serve as software specifications, which can be translated into the computer programs by the routine work of a skilled technician or programmer.

[0097] Although described with reference to specific examples and drawings, modifications, additions and substitutions of example embodiments may be variously made according to the description by those of ordinary skill in the art. For example, the described techniques may be performed in an order different with that of the methods described, and/or components such as the described system, architecture, devices, circuit, and the like, may be connected or combined to be different from the above-described methods, or results may be appropriately achieved by other components or equivalents.

[0098] Although the present invention has been described in detail with reference to the preferred embodiment, the present invention is not limited by the disclosed examples from which the skilled person is able to derive other variations without departing from the scope of the present invention.