1.2 digital image processingap

8/7/2019 1.2 Digital image processingap

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DIGITAL IMAGE PROCESSING

Submitted by

SANDESH.GB.TECH(IV)-CSEPONDICHERRY ENGG. COLLEGEPONDICHERRYEMAIL: [email protected]
mailto:[email protected]:[email protected]


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1. INTRODUCTION

Conventional examination of

the hand radiographs is well

established as a diagnostic as well as

an outcome measuree in Rheumatoid

Arthritis (RA). It is readily available

and has been correlated with measures

of disease activity and function. X-ray

changes are, however, historical rather

than predictive, and there is significant

observer variation in quantifying

erosive changes. The earliest

radiographic changes seen in the hand

are soft-tissue swelling symmetrically

around the joints involved, juxta-

articular osteoporosis and erosion of

the bare areas of bone (i.e. areas

lacking articular cartilage).These

changes help to confirm the presence

of an inflammatory process.

The presence of early soft-

tissue swelling is easily recognized on

plain radiographs but not readily

quantified. Although the presence ofearly osteoporosis is recognized in the

affected hand, a mild osteoporosis may

be extremely subtle to the eyes. The

recognition of the changes in soft-

tissue and bone density is subjective

and is known to vary from assessor to

assessor. Therefore, attention has been

focused on the more objective erosion

and joint narrowing assessment. Use of

magnetic resonance (MR) technique

has been shown to sensitively detect

early local edema and inflammation

prior to a positive finding on plain film

radiographs. However, MR is an

expensive examination and may not be

used as a routine technique.

Presently, radiographs of the

hands and wrists are employed to

assess disease progression. The

parameters used to determine

progression are the changes in erosions

and joint-space narrowing observed on

the radiographs. There are some

problems with both of these

parameters. First, both erosion and

joint narrowing are not the earliest

changes in RA and further they may be

substantially irreversible. Second,

these two changes may occur

independently of each other. Third,

there is a tremendous variability in

erosive disease: some patients never

develop erosions; some go into

spontaneous remission of their erosive

disease; and for some, the progression

is relentless. Fourth, joint-space or

cartilage loss may be caused by either

the disease itself or by mechanical

stress. Present scoring methods require

that any degree of joint-space loss be

recorded as a progressive change due

to RA.

Quantitative techniques currently

available may provide a new approachin monitoring disease progression in

patients with RA. Adoption of these

techniques may have implications for

the management of patients with RA

and for possible detection of the

disease at an early stage.

1.1. Hand bone densitometry

Considerable advances have been

made over the past two decades in

developing radiological techniques for

assessing bone density. However; all

of these techniques have been utilized

on aging-related osteoporosis, a

pathological change involving general

bone mineral reduction. Owing to the

wide availability of DXA, recently

published research describes the use of

Bone Mineral Density (BMD)measurements in the hands of patients


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with chronic RA. Most published

observations on RA have examined

BMD changes, focusing on only the

general bone loss around the joints.

Quantification of the difference of

bone loss between the juxta-articular

bone and the shaft of tubular bones in

the hands could be a sensitive index for

quantitative analysis of RA patients.

Hand BMD measurements offer an

observer independent and reproducible

means of quantifying the cumulative

effects of local and systemic

inflammation. The technique could be

of use in the assessment of patients

with early RA, in whom conventional

measures of disease are not helpful

until disease is (irreversibly) more

advanced.

of the second metacarpal to determine

BMD. A third technique developed in

Europe measures the diaphysis and

proximal metaphysics of the second

middle phalanx. Based on published

short-term precision errors, computer-

assisted Radiographic Absorptiometry

appears to be suitable for the

measurements of the BMD of

phalanges and metacarpals, and is used

in several hundred centers worldwide.

In this work we present

preliminary results of an ongoing

research work aimed at developing an

automated radiographic absorptiometry

system for the assessment and

monitoring of both BMD and soft

tissue swelling in early stage RA. This

paper focuses on the reproducibility

1.2. Hand radiographic and accuracy of the methodology being

absorptiometry

In conventional Radiographic

Absorptiometry, radiographs of the

hand are acquired with referencewedges placed on the films. The films

are and subsequently analyzed using an

optical densitometer. The resulting

density values computed by the

densitometer are calibrated relative to

that of the reference wedge and are

expressed in arbitrary units.

Recent improvements in hardware and

software available for digital image

processing have led to the quantitative

developed. The paper is organized as

follows: the next section provides an

overview of the image acquisition

procedure. In section 3 the imageanalysis algorithms used in this work

are presented. In section 4 we present

results obtained by analyzing the data

collected in a small reproducibility

study involving 10 normal subjects.

2. IMAGE ACQUISITION

One key factor influencing the

outcome of any radiographic

absorptiometry technique is the

standardization of the image

assessment of radiological acquisition technique. Variability in

abnormalities in diagnostic radiology. acquisition parameters can

Such improvements have also enabled

introduction of several radiographic

absorptiometry techniques. One such

technique uses centralized analysis of

hand radiographs and averages the

BMD of the second to fourth middle

significantly affect the measured

values. In order to carry out this work,

a standard image acquisition protocol

was defined. This protocol has been

successfully used in earlier large scale

multinational phase 3 clinical trials for

phalanges. Another technique Rheuatoid Arthrtis related drugs.developed in Japan uses the diaphysis


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Radiographs of the left and right hands

are taken one

at a time. Templates were developed to

guide the positioning of the hand with

respect to the center of the x-ray beam.

The X-ray beam was centered between

the 2nd and 3rd metacarpo-phalangeal

joints and angled at 90 to the film

surface. This results in a tangential

image of the joints. Improper beam

centering generally results in

overlapping joint margins. The X-ray

exposure parameters were maintained

constant for all subjects. All normal

subjects were imaged at the same

clinic at UCSF. In addition to

providing a template for hand

positioning, two sets of calibration

wedges were also provided to the

clinic. Each set of wedges consisted of

one Acrylic wedge, for soft tissue and

one Aluminum wedge for bone tissue.

These wedges were custom designed

for the purposes of this research work.

Figure1.Template used to position the left hand according to the standardized

protocol.

3.Enhancement Image and Restoration

The image at the left of Figure 1 has been corrupted by noise during the digitization

process. The 'clean' image at the right of Figure 1 was obtained by applying a median

filter to the image.


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Figure 1. Application of the median filter

An image with poor contrast, such as

the one at the left of Figure 2, can be

improved by adjusting the image

histogram to produce the image shown

at the right of Figure 2.

Figure 2. Adjusting the image

histogram to improve image contrast

3.IMAGEANALYSIS

One of the major difficulties in

analyzing hand radiograph images is

the high level of noise present in the

images. Additionally the trabecular

texture of the hand in the vicinity of

the joints increases the noise in edge

maps of this regions. Use of non-

standard acquisition protocol can add

additional challenges at it can result in

further degradation of image quality.

This last challenge is minimized in this

work, as a standard image acquisition

protocol is followed. Given a particular

application varying degrees of

accuracy in anatomy segmentation can

be considered acceptable. For instance

in detecting joint-space narrowing

there is a need for accurate and reliable

determination of the joint-space of any

finger and the bone edges in this

region. However, accurate delineation

of the bone edges in the vicinity of the

joint is not as relevant. Depending

upon the application there can be

additional constraints on performanceissues as well. In an application for

which off-line processing of the data is

acceptable, more sophisticated

algorithms can be employed. This

particular application requires that the

overall process be fast, accurate and

reproducible enough for on-line

processing. Accurate estimation of the

bone edges in the middle shaft and in

the joint vicinity of high relevance in


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this work. This is primarily because the

disease progression follows different

patterns in the joint area as compared

to the middle phalange area. Also, the

manifestations of the disease

symptoms in its early stage have

different effects on soft-tissue and

bone as well, which require reliable

segmentation of these two types of

tissue at different time points. The

algorithm for hand segmentation can

be outlined into the following main

stages:

Hand outline delineation

Joint identification

Bone outline delineation

Segmentation of soft tissue and bone

The first stage of the algorithm

has been well studied in the literature

and is not described here. The second

stage can be more challenging,

especially when dealing with hands of

patients in advanced stage of disease

progression. As this system will beapplied to a patient population that is

in their early disease stage it is

expected that the joints will be well

defined. The system provided to the

radiologists allows them to adjust the

location of the automatically identified

joints. Results presented in this work

were obtained by having the

radiologists place control points to

identify the joints, rather than having

them automatically computed by the

system.

3.1. Control point placement

A simple user interface was

provided to enable placement of

control points on the joints. This was

primarily done to investigate the

sensitivity of the system to the initial

control point positioning, which in an

automated system would invariably be

the same for the same image. The user

placed 16 control points on each

image. These joints are show in Figure

4. In addition to placing the control

points for the joints, the control points

for the two wedges are also placed by

the radiologists. For each wedge, six

control points are placed with four at

the corners and two in the middle.

Once all the control points are placed,

the remaining steps of the generalized

algorithm stated above are carried out

autonomously. The middle phalange or

cortex control points are computed

automatically and are located at the

middle of straight line connecting the

two joints, one above and one below

the middle phalange. The diameters of

the circular regions of interest placed

around each joint are computed

proportionally to the length of the

fingers.

Letjibe the control points placed at the

joints and mibe the control pointsplaced at the cortex of the phalanges.

The distance dibetween jointjiand ji+4

is given by:

(1)

In the equation above jix and jiy

are the x and y coordinates of thecontrol point jirespectively. Also i and

i+4 are indexes for two joints on the

same finger, as only the joints on the

fingers are used in this work and not

the thumb. Using the distance

computations above the radius rij for

the Region of Interest(ROI) placed at

the joint jiand rim for the ROI placed

around the cortex mi

is given by:


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(2)

(3)

The resulting ROIs are larger around

the joints and are smaller around in the

middle phalange, yet they all cover

bone and soft-tissue evenly.

3.2. Bone ridge delineation

Initially the entire hand image

is equalized using the gray scale

distribution of the region of interest

around 1st MCP. This procedure

enhances the bone areas and soft-tissue

areas of the hand and suppresses any

other details. This provided an image

with the best contrast between bone

and soft tissue regions.

Once the image is equalized a

standard Sobel gradient detection

algorithm is applied. The resulting

image has well defined ridges of thebone along with other edges that

represent the bone trabecular structure.

For each middle phalange

control point mi, the bone ridges are

identified by traversing the finger axis

formed by connecting jiand ji+4, in a

direction perpendicular to that of the

axis, as shown in Figure 2. The finger

anatomy is very well defined in this

region. A pixel is defined to be on the

ridge, if at that each pixel the intensity

ranges of the edge map and of the

original image, is maximal compared

to all other pixels between this one and

the one of the finger axis.

Figure 2. Computation of a point on

the bone edge.

Let rkbe the points on the ridge

corresponding to a set of points mkthat

are in a neighborhood Nof the point

mi and on the finger axis. Then the

average of the distance between mkand

mkis used to define the bounds within

which a boundary tracing algorithm

can be used to track the rest of thebone ridge for this section of the

finger. The system follows the shaft of

the finger from the middle phalange to

the joint above and scans perpendicular

to this direction and identifies the ridge

points. The same procedure is repeated

for the part that connects the middle

phalange to the joint below. At the end

of these procedures an initial estimateof the fingers bone boundary is

detected. The next step is to scan the

boundary points and discard any

outliers. Owing to the smoothness of

the bone contours, a 7th order Bezier

Spline is fit to the contour data. Once

this is completed, the resulting ridge

contours are presented to the

radiologist. At this point the radiologist

is also provided with a tool to fix


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any severe errors that are observed in

the bone outlines. These errors are

generally found close to the joint,

where the edge distribution is quite

noisy.

This simplistic approach

provides a very fast and accurate

mechanism to generate the bone

boundary outlines. Perhaps a more

sophisticated algorithm can be

employed, at the expense of longer

execution time, to further improve the

boundary in the vicinity of the joint.

However the results obtained on the

normal population suggest that this

method is sufficient to provide a high

degree of reproducibility.

Once the contours are accepted,

the system automatically segments the

bone region and the soft-tissue regions

using the outlined contours and

provides an estimate of the average

density of each of these regions in

units of equivalent density computedfrom the calibration wedges.

3.3. Wedge profile computation

The two reference wedges

imaged along with the hand provide an

equivalent density value for each ROI

on the hand. The Acrylic wedge is

Figure 3. Various stages of the bone

delineation step. From left to right:

original image, two joint and one

middle phalange control points,

histogram equalized image, Sobelgradient edge map, edges computed

used to compute the soft-tissue density

while the Aluminium wedge is used to

compute bone density. In order to

obtain reliable estimates of the

equivalent densities an average profile

is computed for each wedge. Let Lkbe

a vector of pixel intensities across a

line inside the wedge, parallel to the

long edge of the wedge. The average

profile for the wedge is given by

(4)

Where w = Acrylic or Aluminium

Once the average profile is computed a

linear regression model is computed to

represent the average wedge profile as

a function of the height of the wedge.

Using this regression model, for any

given gray value an equivalent height

on the wedge can be computed, and

this can be in itself associated to the

equivalent density of the wedge forthat gray value.

along with 7th order Bezier curve fit,

segmented bone and soft tissue

regions.

4. EXPERIMENTAL RESULTS

A small study, involving 10

normal subjects, was conducted atUCSF to determine the reproducibility


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and accuracy of the technique being

developed. The average age of the

patients was 47.1 years. The youngest

and oldest subjects were 32 and 58

years old respectively. Left and right

hands of each subject were imaged

twice. For one of the subjects during

the first acquisition one set of wedges

was employed, and during the second

acquisition a different set of wedges

was required. This was done to observe

any variability introduced by changing

the wedges. Two distinct users, one

radiologist and one technician

analyzed the set of 40 images. While

the radiologist was a trained expert, the

technician was trained only on the use

of the system. In Figure 6a and 6b the

final bone ridge outlines and

segmentation results for a left hand are

shown.

As the results for left and right

hands were similar, only results for theleft hand are presented. The coefficient

of variance (CV) wascomputed as

follows:

(5)

The system used in this work

was developed using the NIH-Image/J

software. A set of plugin modules

were written for the Image/J package,

and some customization of that source

code was done to accommodate

multiple region-of-interests. The

equalization and edge detection

routines described in section 3 are all

part of the standard ImageJ package.

Intra-reader variability was

accessed by comparing the results of

each reader for reading each ROI and

also per patient. Figure 4a and 4b show

the CV for each ROI and each patient

respectively for the expert reader.

Inter-reader variability was accessed

by comparing the results of one reader

against that of the other. Figure 5a and

5b, show the inter-reader CVs for each

ROI and for each patient respectively.

In each of these charts, CV-Softav and

CV_Boneav represent the CVs

computed on average gray-scale

measurements on soft tissue and bone

regions in the segmented ROIs.

Meanwhile, the CV_Soft_d andCV_Bone_d represent the corrected

values of CV_Softav and CV_Boneav

using the Acrylic and Alumynium

wedge profiles respectively.

As an overall estimate for any

given patient, the average CV for an

individual

reader is 3.0% and the average CV for interreader accuracy is about 4.0%.

Left hand

CVs per ROI

for expert

reader


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Figure 4a. Left hand CVs per ROI for all patients. ROI 1-8 are joints and ROI 9-16

are middle phalanges.

Left hand CVs per patient for expert reader

Figure 4b. Left hand CVs per patient for expert reader. Each set of bars in both of the

charts above is grouped from left to right as CV_Softav, CV_Bone_av, CV_Soft_d,

CV_bone_d.

Inter reader CV for left hand per ROI


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Figure 5a. Left hand inter-reader CVs per ROI for all patients. ROI 1-8 are joints and

ROI 9-16 are middle phalanges.

Inte r reader CV fo r left hand per patient

Figure 5b. Left hand inter-reader CVs per patient. Each set of bars in both of the

charts above is grouped from left to right as CV_Softav, CV_Bone_av, CV_Soft_d,

Figure 6a. A sample left hand with all the bone ridges outlined by the system and the

two wedges outlined by the radiologist.

CV_bone_d.


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Figure 6b. Segmentation result of the hand in Figure 6a.

6. MULTIPURPOSE PACKAGE

FOR THE DOCTORS

(Our Contribution in Digital

Image Processing)

This is a project we are

such as acquisition, storage, retrieval,

translation, compression, etc.

Image Acquisition: image is

acquired and brought into the

system (digital camera, CAT

working at. This product will be useful scan) usually requires

for the doctors working in various

specializations. We have organized our

project in the following ways:-

1. Goal of project: Medical imaging isa field which researches and develops

tools and technology to acquire,

manipulate and archive digital images

which are used by the medical

profession to provide better care to the

patients. The goal of our project is to

build a package useful for doctors

working in various specializations.

This product is aimed to enhance the

image size by taking the image in

black and white mode. Thus aimed to

identify and enhance the existence of

any flaws in the bones, pimples on face

and even the spots and marks on face

etc.

2. Introduction to important terms:

Processing of digital images include

operations involving digital images

preprocessing, e.g., scaling,

sampling

Image Enhancement: image is

made clear to the user byenhancing some of the features

of the image this is a subjective

operation (it looks good)

Image Restoration: image is

improved- this operation is

objective (e.g., noise removal)

Color Processing: image colors

and color transformation e.g.,

from display color space (redgreen and blue) into hardcopy

printing space (cyan, magenta

and yellow).

Compression: image archive

size is reduced (storage,

transmission) error free, and

error prune compression

Segmentation: image is

partitioned into features (e.g.,

boundary of objects)


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Representation: extract features

are stored outside the image

Recognition: image objects are

being identified (e.g., liver,

kidneys, spine)3. Project definition

We are planning our project to

be very useful for the doctors

working in various specializations,

in spite of their illiteracy to the

computer. Our package will allow

the doctors to convert the grey

scale image into the binary image

and identify the flaws. A very user

friendly GUI support will be there.

First the image will be

converted into the pixel numbering

and then the numbering will be

used for the binary image

generation. For this we will be

using the following methods-

1. Binary image by median: the

median value of the pixel

values is identified and thevalues that are greater than the

median are given high intensity

and that having less than that

will be given the low intensity.

Thus we will obtain the binary

image which will be used for

enhancing techniques.

2. Binary image by Threshold:

The threshold is identified by

random number picking or the

average of the pixel values is

identified and that is used for

the further processing.

3. Grey level image with 32

different levels: The range for

the levels from 0 to 9 and A to

V are given and the pixels are

given values according to the

regions into which they fall

into. And an image is generated

which is having a less

reflection compared to the

original high resolution grey

levels image.

4. Code for enhancing the image

size: The code we have generated

will take the values of pixels from

a file of pixels and uses them to

generate the binary image. Here we

have followed the threshold

method and the threshold value is

identified accordingly.

The code useful in c language is

//This is the module for converting

the grey level image to a binary

image by threshold method.

#include

#include

#include

main(){

int i,j,th,gd,gm,bin;

int image[64][64];

long int sum=0;

char ch;

float avg;

FILE *fp;

gd=DETECT;

initgraph(&gd,&gm,);

fp=fopen(d:\lin.txt,r);

if(fp==NULL)

printf(File Not Open)

for(i=0;i


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avg=(ing\t)(sum/(64*64.0));//Threshol

d generation

for(j=0;javg)

bin=0;

else

bin=15;

putpixel(i+20,j+20,bin);

}

getch();

closegraph();

fclose(fp);

}

5. DISCUSSION AND

CONCLUSION

In this paper a radiographic

absorptiometry based methodology

was presented to accurately and

reliably estimate the density of soft-

tissue and bone. The results presented

here focused on the reproducibility ofsuch measurements in a normal subject

in-vivo study. One aspect to note is

that one of the readers in this work was

a technician. This suggests that in a

routine clinical setting, there would be

no need to involve a radiologist to

carry out such a procedure. A trained

technician would be

able to provide estimates as reliable as

an expert reader would. The results

presented here are preliminary and

focused only the reproducibility

aspects of the technique. It should be

noted however, that despite having

manual inputs in the control point

placement stage, the measurements

were highly reproducible. While there

is already work done to automate the

placement of the joint control points,

further work includes automating the

wedge profile control point placement.

By taking these steps it is expected that

the reproducibility of the system will

be further enhanced. It is also not clear

if there is a need for soft-tissue density

correction to be applied to the bone

density estimates. This is because the

density values of the bone include

some fraction of soft-tissue densities as

well. Most existing correction

techniques require the use of a model

for the fingers, and that in itself can

lead to introducing more errors in the

density estimate. The sensitivity of the

technique in quantifying changes

undergone by a subject during the

early stages of the disease is also not

yet well defined. This technique is

being applied towards monitoring early

stage rheumatoid arthritis patients in an

ongoing clinical trial. Results obtained

from the clinical trial data should

provide a better understanding of such

sensitivity parameters.

REFERENCES

[1]. Fries J.F., Block D.A., Sharp J.T.

et al. Assesment of radiologic

progression in rheumatoid arthritis. A

randomized, controlled trial. Arthritis

Rheum 1986;29;1-9

[2]. Sharp J.T., Wolfe F., Mitchell

D.M., Bloch D.A., The progression of

erosion and joint space narrowing

scores in rheumatoid arthritis during

the first twenty-five years of disease.

Arthritis-Rheum. 1991; 34; 660-8

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