System and method for quantification of Escherichia coli bacteria in water

A system and method for quantification of Escherichia Coli bacteria in water is disclosed. In an embodiment, a region of interest (ROI) is obtained from an image of the water. For example, the ROI includes a plurality of pixels in the image of water contaminated with Escherichia Coli bacteria. Further, a plurality of red pixels are identified from the ROI based on a value of the plurality of pixels in the ROI and a threshold value. Furthermore, total redness of the plurality of red pixels in the ROI is calculated based on intensity of plurality of red pixels. In addition, a redness factor indicative of a degree of redness of the ROI is computed based on the calculated total redness. Quantification of the Escherichia Coli bacteria is then estimated based on the computed redness factor.

PRIORITY CLAIM

This U.S. patent application claims priority 35 U.S.C. § 119 to: Indian provisional specification no. 3880/MUM/2014 filed on Dec. 3, 2014. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD

The embodiments herein generally relate to water contaminated withEscherichia Colibacteria, and more particularly, for quantification ofEscherichia Colibacteria in water.

BACKGROUND

Public health protection requires safe drinking water, which is free of pathogenic bacteria. Typically, water-related diseases are caused by consumption of water that is contaminated with human or animal fecal material. Pathogens such asEscherichia Coli(E. coli) are generally present in very low concentrations in environmental waters within a diversified microflora. The presence ofE. colihas long been established as the most reliable microbiological indication of water quality and presence of fecal contamination in water. Detection and quantification of bacteria is important for monitoring the sanitation of water. Culture methods are routinely used for detection and quantification of the presence ofE. coli.

Existing culture-based methods perform a selective culture step followed by biochemical or genetic confirmation of presumptiveE. colicolonies or cultures. Typical culture-based methods require advanced techniques, laboratory environment or (incubators) and trained professional with specialized skills to use the techniques. Thus, culture-based methods are tedious, cost-intensive and time consuming.

SUMMARY

The following presents a simplified summary of some embodiments of the disclosure in order to provide a basic understanding of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some embodiments of the invention in a simplified form as a prelude to the more detailed description that is presented below. In view of the foregoing, an embodiment herein provides water contaminated withEscherichia Colibacteria, and more particularly, for quantification ofEscherichia Colibacteria in water.

In one aspect, a method for quantification ofEscherichia Colibacteria in water is disclosed. In an embodiment, a region of interest (ROI) is obtained from an image of a syringe filter upon transferring water contaminated withEscherichia Colibacteria. The ROI includes a plurality of pixels in the image of the syringe filter upon transferring water contaminated withEscherichia Colibacteria. Further, a plurality of red pixels are extracted from the ROI based on a value of the plurality of pixels in the ROI and a threshold value. Furthermore, total redness of the plurality of red pixels in the ROI is calculated based on intensity of plurality of red pixels. In addition, a redness factor indicative of a degree of redness of the ROI is computed based on the calculated total redness. Quantification of theEscherichia Colibacteria is then quantified based on the computed redness factor.

In another aspect, a system for quantification ofEscherichia Colibacteria in water is disclosed. In an embodiment, the system includes one or more processors and a memory communicatively coupled to the one or more processors. The memory includes a quantification module. In this embodiment, the quantification module obtains a ROI from an image of a syringe filter upon transferring water contaminated withEscherichia Colibacteria. The ROI includes a plurality of pixels in the image of the syringe filter upon transferring water contaminated withEscherichia Colibacteria. Further, the quantification module extracts a plurality of red pixels from the ROI based on a value of the plurality of pixels in the ROI and a threshold value. Furthermore, the quantification module calculates a total redness of the plurality of red pixels in the ROI based on intensity of plurality of red pixels. In addition, the quantification module determines a redness factor indicative of a degree of redness of the ROI based on the calculated total redness. Also, the quantification module estimates quantification of theEscherichia Colibacteria based on the computed redness factor.

These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following description, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration. The summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter. Changes and modifications may be made within the scope of the embodiments herein.

DETAILED DESCRIPTION

FIG. 1is a flow diagram100illustrating a method for quantification ofEscherichia Colibacteria in water, according to an embodiment of the present disclosure. At block102, a Region of Interest (ROI) is obtained from an image of the syringe filter upon transferring water contaminated withEscherichia Colibacteria. For example, the ROI includes a plurality of pixels in the image of the syringe filter upon transferring water contaminated withEscherichia Colibacteria.

In an exemplary embodiment, water sample is collected and transferred to a syringe filter. Further,Escherichia Colibacteria in the water changes color of the syringe filter to red color. In general, the syringe filter is surrounded by a blue ring represents the contamination and provide protection to the ROI. Generally, the ROI is a region inside the blue ring. In this example, a degree of contamination is directly proportional to an intensity and spread of the red color in the syringe filter. Further, an image of the syringe filter is obtained using an image capturing device (e.g. a camera and the like). For example, the image includes three components, such as red, green and blue (RGB) components. Furthermore, red (R), green (G) and blue (B) band images are extracted from the captured image of the syringe filter by separating the RGB components, respectively. Each of the plurality of pixels includes a red component value, a green component value and/or a blue component value.

In addition, the blue ring surrounding the syringe filter is detected. For example, the blue ring includes blue colored pixels which have lower values of red and green bands and higher values of a blue band. In an example implementation to detect the blue ring, threshold ranges are defined to all three RGB bands. For example, an Otsu's method is used to compute the threshold ranges (an inbuilt matlab function (graythresh) is used to compute the threshold ranges). The threshold ranges are used to binarize the grey images. In an example, the threshold range defined for the red band is 0 to graythresh (red_band)*255. The threshold range defined for the green band is 0 to graythresh (green_band)*255. The threshold range defined for the blue band is graythresh (blue_band)*255 to 255. In some embodiments, Otsu's methodology is employed to compute clustering-based image thresholding. The above methodology makes an assumption that the image contains two classes of pixels following bi-modal histogram and then computes an optimum threshold separating the two classes so that their combined spread is minimal.

Further in this example implementation, masking is applied on each of the RGB band images with the associated threshold ranges. Upon masking, the portion which is blue in the captured image appears to be binary 1 in all the three masked images of different bands. Binary mask allows specifying transparent areas when a given image is intended to be placed over a background. In the images, the black pixels have the all-zero values and white pixels have the all-one values.

Furthermore, a logical AND operation is performed on the three masked images namely a red mask, a green mask and a blue mask to form a blue ring image and to detect the blue ring. In this scenario, position the images on the screen over the background, and masks the screen pixel's bits with the image mask at the desired coordinates namely the red mask, the green mask and the blue mask using the logical AND operation to detect the blue ring. Also, standard image processing techniques, such as bwareopen, imclose and bwconn comp are used to remove the portion of the other blue rings, small marks, noise data etc. and to smoothen the borders in the blue ring image.

In addition, ROI indices are extracted from the blue ring image using a below method:

1. Traverse a row and perform the following:a. If no white pixel (binary 1) is found, make all the row white (binary 1).b. If any white pixel is found then perform the following:(i) Stop and make the row white till this point.(ii) Start traversing the same row from the other end till the white pixel is found and make the row white till this point.

2. Repeat the step 1 for all the rows in the blue ring image

3. Finally complement the resultant image.

4. Save the indices of the white pixels (e.g., the ROI indices).

At the extracted ROI index locations, concatenate all the three color bands (RGB) to get the color image of the ROI.

At block104, a plurality of red pixels are extracted from the ROI based on a value of the plurality of pixels in the ROI and threshold values. In an example, red pixels are extracted from ROI using a below equation (1):
Rij>(GijBij)1/2(1)
Applying logarithm on both sides,

log⁡(Rij)>log⁡(Gij)+log⁡(Bij)2(2)
When the RGB component value of the pixel is 0, to overcome the log (0) error, add 1 inside the logarithm term as shown in below equation:
2*log(Rij+1)>log(Gij+1)+log(Bij+1)  (3)
2*log(Rij+1)−log(Gij+1)−log(Bij+1)>0  (4)

Almost all the pixels in the ROI satisfy the above condition. So a threshold value (β) is used to extract only the red pixels.

In an example implementation, the threshold value (β) is determined using a method by initializing β value is equal to 0 and extracting the red pixels from the ROI. Further, observe if any non-red color pixels are extracted. Further, if β value is equal to 11.09 end the process, else increase the β value to 0.1 and continue the process. Upon determining the β value, the plurality of red pixels are extracted from the ROI when a difference between two times red component value of a pixel and a sum of a green component value of the pixel and a blue component value of the pixel is greater than the threshold value using a below example equation (5).
2*log(Rij+1)−log(Gij+1)−log(Bij+1)>β  (5)
where, Rijis the red component value of the pixel at the location i,j, Gijis the green component value of the pixel at the location i,j, Bijis the blue component value of the pixel at the location i,j, β is a threshold values, ij are the variables and Log is equal logarithm with respect to the base e.

An exemplary image of200explains the red pixels extracted from the ROI with different threshold values are shown inFIG. 2. The images of the extracted red pixels from the ROI are obtained with different threshold (β) values ranges from (0.4, 0.5, 0.9, 1.8). Further, a table300ofFIG. 3illustrates the observations over several ROI images on extracting on red pixels while determining threshold value. Further, in an example the 1 ranging from 0.8 to 1.2 is considered as the suitable range of thresholds to extract all the red pixels from the ROI. In addition,FIG. 4explains a graph400between all pixels extracted from ROI with the threshold values. Further, for this experimentation a value of 1.18 is considered as the threshold value to extract the red pixels from the ROI.

In an embodiment, extracted red pixels from the ROI have different color intensities. At block106, a total redness of the plurality of red pixels in the ROI is calculated based on the intensity of plurality of red pixels. In an embodiment, redness of each of the plurality of red pixels is determined based on the intensity of plurality of red pixels. For example, the redness of each of the plurality of red pixels is a difference between the two times of a red component value of a red pixel and a sum of a green component value of the red pixel and a blue component value of the red pixel as shown in a below equation (6).
f(R,G,B)=2*log(Rij+1)−log(Gij+1)−log(Bij+1)  (6)where, Rijis the red component value of the red pixel at the location i,j, Gijis the green component value of the red pixel at the location i,j, Bijis the blue component value of the red pixel at the location i,j, i,j are the variables and Log is a logarithm with respect to the base e.
Furthermore, the total redness of the plurality of red pixels is calculated by summation of the redness of each of the plurality of red pixels.

At block108, a redness factor (RF) indicative of a degree of redness of the ROI is computed based on the calculated total redness. In an example implementation, a mean value of the total redness of the ROI is calculated based on the ratio of the total redness of the ROI to the number of plurality of the red pixels in the ROI. For example, the mean value of the total redness of the ROI is calculated using a below equation (7):

Where Σext. red RGBf(R, G, B) is the total redness of the image of the ROI.

Further in this implementation, the RF indicative of the degree of redness of the ROI is then computed based on the calculated mean value of the total redness.

In one example, a sample dataset used in determining RF along with the CFU categories is shown in a table500ofFIG. 5. At block110, computed redness factor is compared with a set of predetermined redness factors shown in a table600ofFIG. 6. In an embodiment, the redness factor is computed for all the images with knownE. coliconcentration which is measured in terms of CFU/ml (Colony Forming Unit) as shown below.

In an example, 10^6 multiplier is applied on RF factor as the redness value is very small. Further, the range of RF values for the CFU categories is shown in the table600ofFIG. 6.FIG. 7indicates the graph700between the CFU categories and redness factor multiplied by 10^6. The graph700shows that all the CFU categories can be classified based on RF value.

At block112, theEscherichia Colibacteria in the water is quantified based on the comparison at block110. Particularly, the computed redness factor is used to quantify theE. colibacteria contamination with the respective CFU category.

In an implementation, one or more of the method(s) described herein may be implemented at least in part as instructions embodied in non-transitory computer-readable storage medium and executable by one or more computing devices. In general, a processor (for example a microprocessor) receives instructions, from a non-transitory computer-readable medium, for example, a memory, and executes those instructions, thereby performing one or more method(s), including one or more of the method(s) described herein. Such instructions may be stored and/or transmitted using any of a variety of known computer-readable media.

FIG. 8illustrates a system800for quantification ofEscherichia Colibacteria in water, according to an embodiment of the present disclosure. Although the present subject matter is explained considering that the system is implemented as a server, it may be understood that the system may also be implemented as a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a tablet, a mobile phone, a robot and the like. In one implementation, the system may be implemented in a cloud-based environment. It will be understood that the system may be accessed by multiple users through one or more user devices.

As shown inFIG. 8, the system800includes one or more processor(s)802and a memory804communicatively coupled to each other. The system800also includes interface(s)806. Further, the memory804includes modules, such as a quantification module808. AlthoughFIG. 8shows example components of the system800, in other implementations, the system800may contain fewer components, additional components, different components, or differently arranged components than depicted inFIG. 8.

The processor(s)802and the memory804may be communicatively coupled by a system bus. The processor(s)802may include circuitry implementing, among others, audio and logic functions associated with the communication. The processor(s)802may include, among other things, a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the processor(s)802. The processor(s)802can be a single processing unit or a number of units, all of which include multiple computing units. The processor(s)802may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s)802is configured to fetch and execute computer-readable instructions and data stored in the memory804.

The interface(s)806may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, and a printer. The interface(s)806can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the interface(s)806may include one or more ports for connecting the system800to other sources or image capturing devices.

The memory804may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory804, may store any number of pieces of information, and data, used by the system800to implement the functions of the system800. The memory804may be configured to store information, data, applications, instructions or the like for enabling the system800to carry out various functions in accordance with various example embodiments. Additionally or alternatively, the memory804may be configured to store instructions which when executed by the processor802causes the system800to behave in a manner as described in various embodiments. The memory804includes the quantification module808and other modules. The module808includes routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The other modules may include programs or coded instructions that supplement applications and functions of the system800.

In an embodiment, the quantification module808obtains a ROI from an image of the syringe filter upon transferring water contaminated withEscherichia Colibacteria. For example, the ROI includes a plurality of pixels in the image of the syringe filter upon transferring water contaminated withEscherichia Colibacteria. Further, the quantification module808identifies a plurality of red pixels from the ROI based on a value of the plurality of pixels in the ROI and a threshold value. Furthermore, the quantification module808calculates a total redness of the plurality of red pixels in the ROI based on intensity of plurality of red pixels. In addition, the quantification module808determines a redness factor indicative of a degree of redness of the ROI based on the calculated total redness. Also, the quantification module808estimates quantification of theEscherichia Colibacteria based on the computed redness factor. This is explained in more detail with reference toFIG. 1.

It is, however to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device.

The preceding description has been presented with reference to various embodiments. Persons having ordinary skill in the art and technology to which this application pertains appreciate that alterations and changes in the described structures and methods of operation can be practiced without meaningfully departing from the principle, spirit and scope.