final report - sreenivas mamidi - 2009a3ps101p

8/13/2019 Final Report - Sreenivas Mamidi - 2009A3PS101P

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A REPORT

ON

Development and implementation of image processing algorithm for

quantification of microscopic images of Bhasmas

BY

Sreenivas Mamidi 2009A3PS101P B.E (Hons.)E.E.E

AT

CSIR-CEERI, Chennai

A PRACTICE SCHOOLII STATION OF

BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI

PILANI

(JUNE, 2013)


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A REPORT

ON

Development and implementation of image processing algorithm for

quantification of microscopic images of Bhasmas

BY

Sreenivas Mamidi 2009A3PS101P B.E (Hons.)E.E.E

Prepared in partial fulfillment of the

Practice School-II Course(BITS C412)

AT

CSIR-CEERI, Chennai

A PRACTICE SCHOOLII STATION OF

BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI

PILANI

(JUNE, 2013)


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BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE

PILANI (RAJASTHAN)

Practice School Division

Station: CSIR-CEERI Centre:Chennai

Duration:51/4months

DateOfStart: 07-01-2013

Date Of Submission: 14-06-2013

Title of the Project: Development and implementation of image processing algorithm for

quantification of microscopic images of Bhasmas.

ID No./Name/

Discipline of the student Sreenivas Mamidi / 2009A3PS101P

Name and Designation

Of the expert : DR. A.Gopal, Scientist & Mr. A.S.S.Sarma, Scientist

Name Of the PS Faculty: Mr. V.R.RAJAN

Keywords: Image Processing, Matlab, Starch Grain, Fibers, Canny filter, Dilation, Erosion

Project Areas: Identification and quantization using Image Processing.

Abstract:

The project is about identifying the various components in the images of the sample powders. For this weuse the concept of image segmentation The project started with getting acquainted to the matlab image

processing toolbox. Using the functions, we detect the cells we need and then segregate them from the

original image. The various cell we need to segment are starch grains, fibers, stone cells etc. We need to

also know some of the biological features for distinguishing between the various characteristics. I have

succeeded in extracting and quantifying some of the features. The codes for segmenting them are shown

in the end of the report. The snapshots of the output are also shown.

Signature of Student Signature Of PS Faculty

Date Date


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BIRLA INSTITUE OF TECHNOLOGY & SCIENCE

PILANI (RAJASTHAN)

PRACTICE SCHOOL DIVISION

Response Option Sheet

Station: CSIR-CEERI Centre:CHENNAI

ID No. & Name: SREENIVAS MAMIDI /2009A3PS101P

Title Of the Project: Development and implementation of image processing algorithm for

quantification of microscopic images of Bhasmas.

Usefulness of the project to the on-campus courses of study in various disciplines. Project should be

scrutinized keeping in view the following response options. Write Course No. And Course Name against

the option under which the project comes.

Refer Bulletin for Course No, and Course Name.

Signature Of Student Signature Of Faculty

Code No. Response Options Course No. & Name

1. A new course can be designed out of this project NO

2. The project can help modification of the course content

Of some of the existing courses.

NO

3. The project can be used directly in some of the existing

Compulsory Discipline courses (CDC) /Disciplines

Compulsory Discipline courses (DCOC) / Emerging

Area (EA) etc. Courses

NO

4. The project can be used in preparatory courses like

Analysis and Application Oriented Courses (AAOC)/

Engineering Science (ES)/ Technical Art (TA) and

Core Courses.

NO

5. This project cannot come under any of the above-

mentioned options as it relates to the professional work

of the host organization.

YES


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ACKNOWLEDGEMENTS

I express my sincere gratitude to Dr. B. N. Jain, Vice Chancellor of Birla Institute of Technology

and Science, Pilani, for the provision of the required infrastructure. I am grateful to Dr. Niranjan Swain

Dean of Practice School Division, BITS-Pilani, for providing me with this wonderful opportunity of doing

an internship at CSIR-CEERI, Chennai.

I am deeply indebted to Mr. V.R.Rajan, Faculty in charge of our Practice School II at Chenna

station, BITS-Pilani, for his constant support, encouragement and guidance in writing this report.I am also

indebted to my Practice School Instructor, Ms. R. Bharathi, Lecturer, Birla Institute of Technology and

Science, Pilani for her constant support and encouragement. I wish to express my gratitude to

Dr.A.S.V.Sharma, scientist-in-charge, Central Electronics Engineering Research Institute, Chennai for giving

me an opportunity to work in CEERI.

I wish to express my appreciation to the enormous help given by my mentors, Dr.A.Gopal, & Mr

A.S.S Sarma and also to my friends who spared a great amount of their valuable time in going through the

manuscript and providing suggestions and error-free proofreading.

I am very much thankful to all the consultants for their constant support in the explanation of concepts

and clearing of doubts during the course of project work.


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TABLE OF CONTENTS

Acknowledgments 05

1. Background and Profile of Organization 072. Aim/Scope of the PS-II Project 083. Introduction

3.1 History of Medicinal Plants 08

3.2 Components of Powdered Leaf 09

3.3 Image Processing 12

3.4 MATLAB Image Processing toolbox 16

4. Methodology

4.1 Canny edge detector 17

4.2 Morphology Fundamentals: Dilation and Erosion 19

4.2.1 Dilation 24

4.2.2 Erosion 26

5. Results and Discussion

5.1 Starch Grain 31

5.2 Fibers 36

6. Conclusion 41

7. References 42


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Background and Profile of Organization

History

CSIR-Central Electronics Engineering Research Institute, popularly known as CSIR-CEERI, is a

constituent establishment of the Council of Scientific and Industrial Research (CSIR), New Delhi. The

foundation stone of the institute was laid on 21st September , 1953 by the then Prime Minister Pt. Jawahar

Lal Nehru. The actual R&D work started towards the end of 1958. The Institute (CSIR-CEERI) has since

blossomed into a center of excellence for development of technology and for advanced research in

electronics. Over the years the institute has developed a number of products and processes and has

established facilities to meet the emerging needs of electronics industry.

Aim & Purpose

To carry out R&D in electronic devices and systems To assist industry in technology absorption, up gradation and diversification To provide R&D services to industry and users in design, fabrication and testing To provide technical services for specific needs towards product development, precision and

quality

Areas of Research

Advanced Sensor Technologies

Process Control Instrumentation & Automation Machine Vision Technologies

Achievements

Space qualification of hybrid microcircuits. Design of serial data controller chip. Development of high voltage deflection transistor for TV applications. Development of space-qualified metalized alumina substrates and Ku band down converter. Development of hybrid PIN/FET for long haul optical communication system. Development of laboratory model of 2x35 kVA DC drive for mining loco. Development of laboratory prototype of Electric Vehicle.


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Aim/Scope of the PS-II Project

The project is about identifying the various components like starch grain, stone cells, fibres

etc. from the magnified images of the powdered plants using image segmentation. Image

segmentation is done by using image processing toolbox available in MATLAB.

Introduction

History of Medicinal Plants

In India, herbal medicine dates back several thousand years to the Rig-Veda. Many of the

herbal remedies used by the Greeks and Romans were an effective treatment that has become

incorporated into modern medicine. Plants have been part of our lives since the beginning of time

We get numerous products from plants, most of them not only good and beneficial but also crucia

to our existence. The use of plants to heal or combat illness is probably as old as humankind. Ou

of these simple beginnings came the pharmaceutical industry. Yet today, the view of plants is very

different from how it all started.

A long time ago, plants were viewed and appreciated with the utmost reverence. Most

people today greatly ignore the existence of plants and seriously downplay their importance. Mos

cannot fathom growing their own plants such as for food, let alone appreciating them, or using

them for personal healing.

The pharmaceutical industry refuses to give them credit for pretty much anything, as it istoday almost 100% synthetic in nature, while the governing agencies continuously make it a battle

to certify anything herbal or plant based. Yet these same corporations have no problem promoting

synthetic and chemical medicines that are continuously proving dangerous in various ways. In

order for something to be profitable today it needs a patent, and nature in its unmodified form

cannot be patented. Thus today we extracted what we wanted out of plants, synthesized it and

patented the final products as pharmaceuticals, or modified entire plants, such as for the production

of food to make them profitable for some and not others.

However, the tides are changing. A quiet, yet significant revolution is under way. More

and more people are remembering their roots. More and more people are returning to natureto


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Mother Earth. More and more people want the benefits that plants can offer us, whether for

medicine or healing, in their natural state.

The earliest foundations of Ayurveda were built on a synthesis of traditional herb practices

combined with new therapies.

Plants useful in Medicine:

Aloe Vera: Skin ailments and wounds Bitter gourd: for controlling blood pressure Bitter leaf: Intestinal ailments Dandelion: Liver, Kidney and Spleen problems Eucalyptus: cough, cold and painkillers Garlic: antibiotic, cardiovascular diseases Ginger: Nausea Guava: gastrointestinal ailments Lavender: antiseptic Neem: Malaria, Rheumatism and skin infections Papaya: for treating wounds Tulasi and Turmeric: Digestion, Antibiotics

Components of Powdered Leaf

Crystals


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Starch Grains

Stone Cells

Trichomes


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Image Processing

The field of digital image processing refers to processing digital images by means of a digita

computer. Digital image is composed of a finite number of elements, each of which has a particular

location and of a region containing the text, preprocessing that image, extracting (segmenting) the

individual characters, value. These elements are called picture elements, image elements, pels, and pixels

Pixel is the term used most widely to denote the elements of a digital image. Converting an image into

digital format can be done with a digital camera, a scanner, or by converting a moving image on a video

tape into digital format.

The processes of acquiring an image describing the characters in a form suitable for computer

processing, and recognizing those individual characters are in the scope of what we call digital image

processing. Digital image processing encompasses processes whose inputs and outputs are images and, in

addition, encompasses processes that extract attributes from images, up to and including the recognition

of individual objects. Digital image Processing can be divided into three types of processes:

Low level Processes Medium level processes High level Processes

Low level Processes:These involve primitive processes such as image processing to reduce the noise.

Contrast enhancement and image sharpening. Low level Processes are characterized by the fact that both

inputs and outputs are images.

Medium level processes:These involve task such as segmentation (portioning of image into regions or

objects) and classification of these objects. Mid-level processes are characterized by the fact that inputs

generally are images, but outputs are the attributes of these images.

High level Processes:These involve making sense of an ensemble of recognized objects, as in imageanalyzes and at the far end performing the functions relates to human vision.


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Fundamental Steps of Image Processing

All the methodologies that can be applied to images for different purposes and possibly with

objectives are as follows:

Image acquisition Image enhancement Image restoration colour Image processing Wavelets and multi solution processing Compression Morphological processing Segmentation Representation & description Object recognition

Image acquisition: This stage involves preprocessing, such as scaling. This can be as simple as being

given a digital image.

Image enhancement:The idea is to bring out detail that is obscured, or simply to highlight certain features

of interest in an image. It is abused on human subjective preferences regarding what constitutes a good

enhancement result.

Image restoration:This deals with improving the appearance of an image. However, unlike enhancementwhich is subjective, image restoration is objective, in the sense that restoration techniques tend to be based

on mathematical or probabilistic models of image degradation.

Color Image processing:Color is used as the basis for extracting features of interest in an image. It has

been gaining in importance because of the significant increase in the use of digital images over the internet

Wavelets:These are the foundation for representing images in various degrees of resolution.

Compression: It deals with techniques for reducing the storage required to save an image, or the bandwidth

required to transmit it. Although storage technology has improved significantly over the past decade, the

same cannot be said for transmission capacity.

Morphological processing: It deals with tools for extracting image components that are useful in the

representation and description of shape.


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Compression:It deals with techniques for reducing the storage required to save an image, or the bandwidth

required to transmit it. Although storage technology has improved significantly over the past decade, the

same cannot be said for transmission capacity.

Morphological processing: It deals with tools for extracting image components that are useful in the

representation and description of shape.

Segmentation: The procedures partition an image into its constituent parts or objects. In general,

autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged

segmentation procedure brings the process a long way toward successful solution of imaging problems

that require objects to be identified individually. On the other hand, weak or erratic segmentation

algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the

more likely recognition is to succeed.

Representation and description:This almost always follow the output of a segmentation stage, which

usually is raw pixel data, constituting either the boundary of a region(i.e. The set of pixels separating one

image region from another) or all the points in the region itself. Description, also called feature selection

deals with extracting attributes that result in some quantitative information of interest or are basic for

differentiating one class of objects from another.

Recognition:This is process that assigns a label (e.g., leaf1) to an object based on its descriptors.

The image processing toolbox is a collection of functions that extend the capability of the MATLAB

numeric computing environment. The toolbox supports a wide range of image processing operations

including

Spatial image transformation Morphological operations Neighbourhood and operations Linear filtering and filter design Transforms Image analysis and enhancement Image registration Deblurring Region of interest operations


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Image Formats

The following are some of the image formats:

BMP - Bitmap Image File JPEG - Joint Picture Expert Group PNG - Portable Network Graphics TIFFTagged Image File Format

Most images you find on the Internet are JPEG-images which are the name for one of the most widely

used compression standards for images. In computing, JPEG is a commonly used method of lossy

compression for digital photography (image). The degree of compression can be adjusted, allowing a

selectable tradeoff between storage size and image quality. JPEG typically achieves 10:1 compression

with little perceptible loss in image quality. If you have stored an image you can usually see from the

suffix what format it is stored in. For example, an image named myimage.jpg is stored in the JPEG format

We can load an image of this format into MATLAB.

Advantages of Image Processing

One of the biggest advantages of digital imaging is the ability of the operator to post-process the

image. Post-processing of the image allows the operator to manipulate the pixel shades to correct image

density and contrast, as well as perform other processing functions that could result in improved diagnosis

The processing of images is faster and more cost-effective. One needs less time for processing, as well as

less film and other photographing equipment. Copying a digital image is easy, and the quality of the image

stays good unless it is compressed. Digital imaging allows the electronic transmission of images to third-

party providers, referring dentists, consultants, and insurance carriers via a modem.

Digital imaging is also environmentally friendly since it does not require chemical processing. It

is well known that used film processing chemicals contaminate the water supply system with harmful

metals such as the silver found in used fixer solution. With the advent of electronic record systems, images

can be stored in the computer memory and easily retrieved on the same computer screen and can be saved

indefinitely or be printed on paper or film if necessary. All digital imaging systems can be networked intopractice management software programs facilitating integration of data. With networks, the images can


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interest (ROIs). With toolbox algorithms you can restore degraded images, detect and measure features,

analyze shapes and textures, and adjust color balance.

Key Features

Image enhancement, filtering, and deblurring Image analysis, including segmentation, morphology, feature extraction, and measurement Spatial transformations and intensity-based image registration methods Image transforms, including FFT, DCT, Radon, and fan-beam projection Workflows for processing, displaying, and navigating arbitrarily large images Interactive tools, including ROI selections, histograms, and distance measurements DICOM file import and export

Methodology

Canny edge detector

Canny's aim was to discover the optimal edge detection algorithm. In this situation, an "optimal" edge

detector means:

good detectionthe algorithm should mark as many real edges in the image as possible. good localizationedges marked should be as close as possible to the edge in the real image.

minimal responsea given edge in the image should only be marked once, and where possibleimage noise should not create false edges.

To satisfy these requirements Canny used the calculus of variationsa technique which finds the function

which optimizes a given functional. The optimal function in Canny's detector is described by the sum of

four exponential terms, but it can be approximated by the first derivative of a Gaussian.

Noise reduction

The image after a 5x5 Gaussian mask has been passed across each pixel.

Because the Canny edge detector is susceptible to noise present in raw unprocessed image data, it uses a

filter based on a Gaussian (bell curve), where the raw image is convolved with a Gaussian filter. The result

is a slightly blurred version of the original which is not affected by a single noisy pixel to any significan


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degree. Here is an example of a 5x5 Gaussian filter, used to create the image to the right, with \sigma =

1.4. (The asterisk denotes a convolution operation.)

Finding the intensity gradient of the image

A binary edge map, derived from the Sobel operator, with a threshold of 80. The edges are colouredto indicate the edge direction: yellow for 0 degrees, green for 45 degrees, blue for 90 degrees and red for

135 degrees.

An edge in an image may point in a variety of directions, so the Canny algorithm uses four filters

to detect horizontal, vertical and diagonal edges in the blurred image. The edge detection operator

(Roberts, Prewitt, Sobel for example) returns a value for the first derivative in the horizontal direction

(Gx) and the vertical direction (Gy). From this the edge gradient and direction can be determined:

The edge direction angle is rounded to one of four angles representing vertical, horizontal and the two

diagonals (0, 45, 90 and 135 degrees for example).


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Tracing edges through the image and hysteresis thresholding

Large intensity gradients are more likely to correspond to edges than small intensity gradients. It

is in most cases impossible to specify a threshold at which a given intensity gradient switches from

corresponding to an edge into not doing so. Therefore Canny uses thresholding with hysteresis.

Thresholding with hysteresis requires two thresholdshigh and low. Making the assumption that

important edges should be along continuous curves in the image allows us to follow a faint section of a

given line and to discard a few noisy pixels that do not constitute a line but have produced large gradients

Therefore we begin by applying a high threshold. This marks out the edges we can be fairly sure are

genuine. Starting from these, using the directional information derived earlier, edges can be traced through

the image. While tracing an edge, we apply the lower threshold, allowing us to trace faint sections of edges

as long as we find a starting point.

Morphology Fundamentals: Dilation and Erosion

To see how morphological processing can solve an image processing problem, view the ImageProcessing Toolbox watershed segmentation example.

Understanding Dilation and Erosion

Morphologyis a broad set of image processing operations that process images based on shapes.Morphological operations apply a structuring element to an input image, creating an output image of thesame size. In a morphological operation, the value of each pixel in the output image is based on acomparison of the corresponding pixel in the input image with its neighbors. By choosing the size andshape of the neighborhood, you can construct a morphological operation that is sensitive to specific

shapes in the input image.

The most basic morphological operations are dilation and erosion. Dilation adds pixels to theboundaries of objects in an image, while erosion removes pixels on object boundaries. The number ofpixels added or removed from the objects in an image depends on the size and shape of thestructuringelementused to process the image.

In the morphological dilation and erosion operations, the state of any given pixel in the outputimage is determined by applying a rule to the corresponding pixel and its neighbors in the input image.The rule used to process the pixels defines the operation as a dilation or an erosion. This table lists therules for both dilation and erosion.


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Rules for Dilation and Erosion

Operation Rule

Dilation The value of the output pixel is the maximumvalue of all the pixels in the input pixel'sneighborhood. In a binary image, if any of the pixels is set to the value 1, the output pixel

is set to 1.

Erosion The value of the output pixel is the minimumvalue of all the pixels in the input pixel'sneighborhood. In a binary image, if any of the pixels is set to 0, the output pixel is set to 0.

The following figure illustrates the dilation of a binary image. Note how the structuring element definesthe neighborhood of the pixel of interest, which is circled. (SeeUnderstanding Structuring Elementsformore information.) The dilation function applies the appropriate rule to the pixels in the neighborhoodand assigns a value to the corresponding pixel in the output image. In the figure, the morphologicaldilation function sets the value of the output pixel to 1 because one of the elements in the neighborhooddefined by the structuring element is on.

Morphological Dilation of a Binary Image

The following figure illustrates this processing for a grayscale image. The figure shows the processingof a particular pixel in the input image. Note how the function applies the rule to the input pixel'sneighborhood and uses the highest value of all the pixels in the neighborhood as the value of thecorresponding pixel in the output image.

Morphological Dilation of a Grayscale Image
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Processing Pixels at Image Borders (Padding Behavior)

Morphological functions position the origin of the structuring element, its center element, over the pixelof interest in the input image. For pixels at the edge of an image, parts of the neighborhood defined bythe structuring element can extend past the border of the image.

To process border pixels, the morphological functions assign a value to these undefined pixels, as if thefunctions had padded the image with additional rows and columns. The value of these padding pixelsvaries for dilation and erosion operations. The following table describes the padding rules for dilationand erosion for both binary and grayscale images.

Rules for Padding Images

Operation Rule

Dilation Pixels beyond the image border are assigned the minimum value afforded by the datatype.

For binary images, these pixels are assumed to be set to 0. For grayscale images, theminimum value for uint8 images is 0.

Erosion Pixels beyond the image border are assigned the maximumvalue afforded by the datatype.

For binary images, these pixels are assumed to be set to 1. For grayscale images, themaximum value for uint8 images is 255.

Note By using the minimum value for dilation operations and the maximum value for erosionoperations, the toolbox avoids border effects, where regions near the borders of the output

image do not appear to be homogeneous with the rest of the image. For example, if erosionpadded with a minimum value, eroding an image would result in a black border around theedge of the output image.

Understanding Structuring Elements

An essential part of the dilation and erosion operations is the structuring element used to probe the inputimage. A structuring element is a matrix consisting of only 0's and 1's that can have any arbitrary shapeand size. The pixels with values of 1 define the neighborhood.

Two-dimensional, orflat, structuring elements are typically much smaller than the image being

processed. The center pixel of the structuring element, called the origin, identifies the pixel of interest --the pixel being processed. The pixels in the structuring element containing 1's define the neighborhoodof the structuring element. These pixels are also considered in dilation or erosion processing.


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Three-dimensional, or nonflat, structuring elements use 0's and 1's to define the extent of the structuringelement in thex- andy-planes and add height values to define the third dimension.

The Origin of a Structuring Element

The morphological functions use this code to get the coordinates of the origin of structuring elements of

any size and dimension.

origin = floor((size(nhood)+1)/2)

(In this code nhood is the neighborhood defining the structuring element. Because structuring elementsare MATLABobjects, you cannot use the size of the STREL object itself in this calculation. You mustuse the STREL getnhood method to retrieve the neighborhood of the structuring element from the

STREL object. For information about other STREL object methods, see thestrelfunction referencepage.)

For example, the following illustrates a diamond-shaped structuring element.

Origin of a Diamond-Shaped Structuring Element

Creating a Structuring Element

The toolbox dilation and erosion functions accept structuring element objects, called STRELs. You usethe strel function to create STRELs of any arbitrary size and shape. The strel function also includesbuilt-in support for many common shapes, such as lines, diamonds, disks, periodic lines, and balls.

Note You typically choose a structuring element the same size and shape as the objects youwant to process in the input image. For example, to find lines in an image, create a linearstructuring element.

For example, this code creates a flat, diamond-shaped structuring element.

se = strel('diamond',3)

se =
http://www.mathworks.in/help/images/ref/strel.htmlhttp://www.mathworks.in/help/images/ref/strel.htmlhttp://www.mathworks.in/help/images/ref/strel.htmlhttp://www.mathworks.in/help/images/ref/strel.html


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Flat STREL object containing 25 neighbors.

Decomposition: 3 STREL objects containing a total of 13 neighbors

Neighborhood:

0 0 0 1 0 0 0

0 0 1 1 1 0 0

0 1 1 1 1 1 0

1 1 1 1 1 1 1

0 1 1 1 1 1 0

0 0 1 1 1 0 0

0 0 0 1 0 0 0

Structuring Element Decomposition

To enhance performance, the strel function might break structuring elements into smaller pieces, atechnique known asstructuring element decomposition.

For example, dilation by an 11-by-11 square structuring element can be accomplished by dilating firstwith a 1-by-11 structuring element, and then with an 11-by-1 structuring element. This results in a

theoretical speed improvement of a factor of 5.5, although in practice the actual speed improvement issomewhat less.

Structuring element decompositions used for the 'disk' and 'ball' shapes are approximations; all otherdecompositions are exact. Decomposition is not used with an arbitrary structuring element unless it is aflat structuring element whose neighborhood matrix is all 1's.

To view the sequence of structuring elements used in a decomposition, use the STREL getsequencemethod. The getsequence function returns an array of the structuring elements that form thedecomposition. For example, here are the structuring elements created in the decomposition of adiamond-shaped structuring element.

sel = strel('diamond',4)

sel =


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Flat STREL object containing 41 neighbors.

Decomposition: 3 STREL objects containing a total of 13 neighbors

Neighborhood:

0 0 0 0 1 0 0 0 0

0 0 0 1 1 1 0 0 0

0 0 1 1 1 1 1 0 0

0 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1

0 1 1 1 1 1 1 1 0

0 0 1 1 1 1 1 0 0

0 0 0 1 1 1 0 0 0

0 0 0 0 1 0 0 0 0

Dilating an Image

To dilate an image, use the imdilate function. The imdilate function accepts two primary arguments:

The input image to be processed (grayscale, binary, or packed binary image) A structuring element object, returned by the strel function, or a binary matrix defining the

neighborhood of a structuring element

imdilate also accepts two optional arguments: SHAPE and PACKOPT. The SHAPE argument affects

the size of the output image. The PACKOPT argument identifies the input image as packed binary.(Packing is a method of compressing binary images that can speed up the processing of the image. Seethebwpackreference page for information.)

This example dilates a simple binary image containing one rectangular object.

BW = zeros(9,10);
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BW2 = imdilate(BW,SE)

Eroding an Image

To erode an image, use the imerode function. The imerode function accepts two primary arguments:

The input image to be processed (grayscale, binary, or packed binary image) A structuring element object, returned by the strel function, or a binary matrix defining the

neighborhood of a structuring element

imerode also accepts three optional arguments: SHAPE, PACKOPT, and M.

The SHAPE argument affects the size of the output image. The PACKOPT argument identifies the inputimage as packed binary. If the image is packed binary, M identifies the number of rows in the originalimage. (Packing is a method of compressing binary images that can speed up the processing of theimage. See thebwpackreference page for more information.)

The following example erodes the binary image circbw.tif:

1. Read the image into the MATLAB workspace.BW1 = imread('circbw.tif');

2. Create a structuring element. The following code creates a diagonal structuring element object.(For more information about using the strel function, seeUnderstanding Structuring Elements.)

3. SE = strel('arbitrary',eye(5));4. SE=5.6. Flat STREL object containing 5 neighbors.7.8. Neighborhood:9. 1 0 0 0 010. 0 1 0 0 011. 0 0 1 0 012. 0 0 0 1 0
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0 0 0 0 1

13.Call the imerode function, passing the image BW and the structuring element SE as arguments.BW2 = imerode(BW1,SE);

Notice the diagonal streaks on the right side of the output image. These are due to the shape of thestructuring element.

imshow(BW1)

figure, imshow(BW2)

Combining Dilation and Erosion

Dilation and erosion are often used in combination to implement image processing operations.For example, the definition of a morphological opening of an image is an erosion followed by a dilation,using the same structuring element for both operations. The related operation, morphological closingofan image, is the reverse: it consists of dilation followed by an erosion with the same structuring element.

The following section uses imdilate and imerode to illustrate how to implement a morphologicalopening. Note, however, that the toolbox already includes the imopen function, which performs thisprocessing. The toolbox includes functions that perform many common morphological operations. See

Dilation- and Erosion-Based Functionsfor a complete list.
http://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-14379http://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-14379http://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-14379


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Morphological Opening

You can use morphological opening to remove small objects from an image while preserving theshape and size of larger objects in the image. For example, you can use the imopen function to removeall the circuit lines from the original circuit image, circbw.tif, creating an output image that containsonly the rectangular shapes of the microchips.

To morphologically open the image, perform these steps:

1. Read the image into the MATLAB workspace.BW1 = imread('circbw.tif');

2. Create a structuring element.SE = strel('rectangle',[40 30]);

The structuring element should be large enough to remove the lines when you erode the image, but notlarge enough to remove the rectangles. It should consist of all 1's, so it removes everything but largecontiguous patches of foreground pixels.

3. Erode the image with the structuring element.4. BW2 = imerode(BW1,SE);

imshow(BW2)

This removes all the lines, but also shrinks the rectangles.


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5. To restore the rectangles to their original sizes, dilate the eroded image using the samestructuring element, SE.

6. BW3 = imdilate(BW2,SE);imshow(BW3)

Dilation- and Erosion-Based Functions

This section describes two common image processing operations that are based on dilation and erosion:

Skeletonization Perimeter determination

This table lists other functions in the toolbox that perform common morphological operations that arebased on dilation and erosion. For more information about these functions, see their reference pages.

Dilation- and Erosion-Based Functions

Function Morphological Definition

bwhitmiss Logical AND of an image, eroded with one structuring element, and the image'scomplement, eroded with a second structuring element.

imbothat Subtracts the original image from a morphologically closed version of the image. Can beused to find intensity troughs in an image.

imclose Dilates an image and then erodes the dilated image using the same structuring element forboth operations.

imopen Erodes an image and then dilates the eroded image using the same structuring element forboth operations.

imtophat Subtracts a morphologically opened image from the original image. Can be used to

enhance contrast in an image.
http://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-22448http://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-22448http://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-34991http://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-34991http://www.mathworks.in/help/images/ref/bwhitmiss.htmlhttp://www.mathworks.in/help/images/ref/imbothat.htmlhttp://www.mathworks.in/help/images/ref/imbothat.htmlhttp://www.mathworks.in/help/images/ref/imclose.htmlhttp://www.mathworks.in/help/images/ref/imopen.htmlhttp://www.mathworks.in/help/images/ref/imopen.htmlhttp://www.mathworks.in/help/images/ref/imtophat.htmlhttp://www.mathworks.in/help/images/ref/imtophat.htmlhttp://www.mathworks.in/help/images/ref/imtophat.htmlhttp://www.mathworks.in/help/images/ref/imopen.htmlhttp://www.mathworks.in/help/images/ref/imclose.htmlhttp://www.mathworks.in/help/images/ref/imbothat.htmlhttp://www.mathworks.in/help/images/ref/bwhitmiss.htmlhttp://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-34991http://www.mathworks.in/help/images/morphology-fundamentals-dilation-and-erosion.html#f18-22448


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Skeletonization

To reduce all objects in an image to lines, without changing the essential structure of the image,use the bwmorph function. This process is known asskeletonization.

BW1 = imread('circbw.tif');

BW2 = bwmorph(BW1,'skel',Inf);

imshow(BW1)

figure, imshow(BW2)

Perimeter Determination

The bwperim function determines the perimeter pixels of the objects in a binary image. A pixel isconsidered a perimeter pixel if it satisfies both of these criteria:

The pixel is on. One (or more) of the pixels in its neighborhood is off.

For example, this code finds the perimeter pixels in a binary image of a circuit board.

BW1 = imread('circbw.tif');

BW2 = bwperim(BW1);

imshow(BW1)


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Results and Discussion

Starch Grain

MATLAB Code

%Identifying Starch Grain from the microscopic images provided and finding its

%parameters like perimeter, max & min axis length etc.

clc; %clears the command screen

clear all; %clears all the variables

%Reading the Microscopic image of the Starch Grain

rbg = imread('sg1', 'tif'); %reads the given image to the

defined variable

figure, imshow(rbg, 'InitialMagnification', 10) %displays the variable as an

image at specified magnification

title('Input Image of Starch Grain') %displays the string as the

title of the image

%Converting the RGB image To Grayscale of same size

grayscale = rgb2gray(rbg); %convets the colour image to

a grayscale image of same size

figure, imshow(grayscale, 'InitialMagnification', 10) %displays the variable as an


title('Grayscale Image') %displays the string as the

title of the image

%Defining a flat morphological structuring element "seD"


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seD = strel('diamond',1); %creates a morphological

structuring element

%Smoothening the object using the pre-defined flat morphological structuring element

smooth = imerode(grayscale, seD); %erodes the image using the

defined structuring element

figure, imshow(smooth, 'InitialMagnification', 10) %displays the variable as an


title('Smoothened Image') %displays the string as the

title of the image

%Defining a nonflat morphological structuring element "seB"

seB = strel('ball',45,1,4); %creates a morphological

structuring element

% Dilating the image using the pre-defined nonflat morphological structuring element

dilated = imdilate(smooth,seB); %dilate the image using the

defined structuring element

figure, imshow(dilated, 'InitialMagnification', 10) %displays the variable as an


title('Dilated Image') %displays the string as the

title of the image

%Finding edges in grayscale image using canny filter

filtered=edge(dilated,'canny',sqrt(3)-1, sqrt(3)); %finds edges in a grayscale

image


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figure, imshow(filtered, 'InitialMagnification', 25) %displays the variable as an


title('Output Image') %displays the string as the

title of the image

%Measuring various parameters of the image

regionprops(filtered, 'Perimeter') %measures properties of image

regions

regionprops(filtered, 'MajorAxisLength') %measures properties of image

regions

regionprops(filtered, 'MinorAxisLength') %measures properties of image

regions

%Coded using Matlab R2012b

Output


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Measurements

Fibres

MATLAB Code 1

%Identifying fibres from the microscopic images provided

clc;

clear all;

%Reading the Microscopic image of the Fibre

f1 = imread('f3', 'tif');

figure, imshow(f1, 'InitialMagnification', 10)

title('Input Image of Fibres')


f2 = rgb2gray(f1);figure, imshow(f2, 'InitialMagnification', 10)

title('Grayscale Image')


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%Running aloop for all the possible angles

fora=0:1:180

SE = strel('line', 50, a); %Defining a morphological structuring element

f3 = imdilate(f2, SE); % Dialating the image using the pre-defined

nonflat morphological structuring element

end

%Preparing Output Image

f4=f3-f2;


title('Output Image')


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MATLAB Code 2%Identifying fibres from the microscopic images provided

clc;

clear all;

%Reading the Microscopic image of the Fibre

f1 = imread('f3', 'tif');


title('Input Image of Fibres')



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f2 = rgb2gray(f1);


title('Grayscale Image')

a = input('Input angle Value:'); %reads a angle value from the user

SE = strel('line', 100, a); %Defining a morphological structuring

element

f3 = imdilate(f2, SE); % Dialating the image using the pre-

defined nonflat morphological structuring element


title('Output Image')


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Conclusion

In this project I have successfully identified two components i.e. starch grains and fibres. These codes can

be used if the magnification of the images is very high. The code written for starch grain is highly efficient

and physical parameters can also be calculated. On the other hand codes written for fibres have noise in

them. If the project is completed by identifying the other components then a database of values can be

created and can be compared to the sample powder to identify the leaf. This can be done by seeing the

presence and absence the components, comparing the physical values of the components and density of

presence in the sample image.


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final report - sreenivas mamidi - 2009a3ps101p

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