Diagnosis of tuberculosis using combined blur and affine moment invariants

ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com

International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 4, Issue 12, December 2017

Diagnosis of Tuberculosis using Combined Blur and Affine Moment Invariants K. Durga Prasad1, Dr. M.B. Ramana Murthy2, Dr. A. Venkataramana3 Associate Professor, E.C.E Dept., J.P.N. College of Engg , Mahabubnagar, Telangana,India1 mail ID: dpkolluru@gmail.com Professor , E.C.E Dept. , IIIT Basara, Telangana , India 2 Lecturer in ECE, Q.Q. Government Polytechnic, Hyderabad, India 3 Abstract: Tuberculosis is a major infectious disease in many regions of the World. Detection of Tuberculosis is the most efficient solution for prevention of this disease. Chest radiography is the most common method adopted for screening this disease and the success of this method depends on the experience and interpretation of the skilled radiologist. This paper presents Computer Aided Diagnosis (CAD) system for detection of Tuberculosis using Combine Blur and Affine Moment Invariants (CBAMI). The CAD method not only provides second opinion to radiologist, but also accelerates the process of active case finding. The CBAMI are selected as features in this paper because they are invariant to both affine distortions as well as blur. Simulation results are carried out by considering The Montgomery County X ray dataset and the results are compared with Geometric Moment Invariants. It is observed from the results that the proposed method provided better accuracy.

Keywords: Tuberculosis, Computer Aided Diagnosis , Combine Blur and Affine Moment Invariants, Geometric Moment Invariants

I. INTRODUCTION Tuberculosis (TB) is one of the public health problem which causes major health threat worldwide. The TB is an infectious disease caused by bacillus mycobacterium tuberculosis. It affects the lung (pulmonary TB) portions. It is a communication disease [1] which spreads through coughing and sneezing of the infected person. If the person is living with HIV, the probability to attach TB disease is very high. The most efficient solution for prevention of this disease is to detect its presence as early as possible in the affected person. There are different methods available for screening of TB namely (i) The Sputum smear microscopy (ii) Tuberculin skin test (iii) Rapid molecular test and (iv) Chest radiography test. Sputum smear microscopy is a screening method in which sputum samples are examined under a microscope to see if bacteria are present or not. Its sensitivity is reduced in patients with HIV co-infection [2].

In Tuberculin skin test, a small amount of tuberculin is injected into the skin and the size of swelling is observed.

This method is not reliable as it causes misclassification [1].

Latest rapid molecular tests are fast and accurate. But, their availabilities and cost is a big primary concerned. The WHO

recommended chest radiography (CXR) for screening proposes for early detection of TB. Early detection leads to success of anti TB therapy. It also helps to control over transmission of infection as well as development of drug resistant TB. CXR diagnostic method improves TB detection [3,4] can be used for screening large population and can identify active TB cases with a reasonable amount of time.

This method is also low cost effective manner. Further, in areas having limited resources, the confirmatory diagnostic test like sputum smear test are not available , CBX can be used as most powerful diagnostic tool for TB detection.

Especially in developing countries, because of lack of skilled radiologists and other resources, it becomes difficult for effective diagnosis. Hence, Computer Aided Diagnosis (CAD) tool can gain lot of significance because they not only reduce diagnostic errors but also increases the efficiency of mass screening in poor resource setting. CAD method provides second opinion to the radiologists for their finding. It provides better diagnosis of cancer and other diseases including TB. In the literature, for object recognition applications, Histogram of Oriented Gradients (HOG) are used as

All Rights Reserved Š 2017 IJARTET

20

ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com

International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 4, Issue 12, December 2017

features. Sivaranjani [6] proposed a method for analysis of II. OVERVIEW ON MOMENT INVARIANTS TB with HOG as features and SVM as classifier. Ankitha et In this section, we present details about Geometric, al [7] proposed a method to extract the lung field by an moments, Geometric Moment Invariants and Combined interactive lung field segmentation based on active contour Blur and Affine Moment Invariants. models. After segmentation process, they analyzed the pixel data within region of interest using first order statistics to 2.1 Geometric Moments and Their Invariants extract textural properties in classifying image as TB and non TB. A method using HOG Features in Computer-Aided The Geometric moments of order (p, q) for an image Diagnosis of Tuberculosis without Segmentation was f ( x, y ) are defined [10] as proposed in [8]. Pallavi et al. [9] used first order statistical properties such as mean and entropy for diagnosis of TB.   (1) M pq  x p y q f ( x, y ) dx dy Hu [10] introduced geometric moments based on monomials   . He derived a set of seven moment invariants which are invariant with respect to change in image translation, where p,q  0,1, 2, .........,  rotation and scale. Jan Flusser et al [11] derived blur invariants which are invariant with respect to blurring on the To keep the dynamic range of M pq consistent for different acquired image. Blur invariants are required because real imaging systems as well as imaging conditions are size images, the N x M image plane is first mapped onto a imperfect. Blur may occur in the captured image during the image acquisition process because of the factors such as lane square defined by x [1,  1], y [1,  1] . Then M pq aberration, diffraction, wrong focus and atmospheric can be written as turbulence. They proposed an approach which describes 1 1 images by features that are invariant with respect to blur and recognize images in the feature space. In order to obtain M pq  (2) x p y q f ( x, y ) dx dy features which are invariant to blur as well as affine 1 1 distortion, The same authors [12] derived combined blur and affine moment invariants. Combined invariants are capable The discrete form representation of the above expression is of recognizing objects in the degraded scene without any given by restoration.





N 1 M 1

p q Most of the methods discussed above for diagnosis of M pq    xi y j f ( xi , y j ) x y

Tuberculosis is based on histogram of oriented features, calculation of mean, variance, third moment and entropy features as well Geometric moment invariants. All these methods do not take care into account of blur as well as affine distortion present in the Tb X-ray image. In order to solve this problem and to obtain better accuracy, we propose an efficient method for detection of Tuberculosis in this Paper. The proposed method computes Combined Blur and Affine Moment Invariants (CBAMI) on X-ray image and uses them as feature for detection of tuberculosis.

This paper is organized into five sections. Details about the Geometric moment invariants as well as CBAMI are presented in section II. Proposed method is presented in section III. Simulation results are presented in section IV. The last section presents the conclusions about the work.

(3)

i 0 j 0

where

( xi , y j ) is the centre of (i, j) pixel and

x  xi  xi 1 , y  y j  y j 1 are the sampling intervals in the ‘x’ and ‘y’ directions respectively.

N x M

corresponds to the size of the image.

The Geometric central moments [10] are defined as

 pq 

 

  ( xx 

) p ( y  y ) q f ( x, y ) dx dy (4)



All Rights Reserved © 2017 IJARTET

21

ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com

International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 4, Issue 12, December 2017

x 

where

M 10 M 01  and y  M 00 M 00

(5)

For a digital image, the Geometric central moments can be written as N 1 M 1

 pq   ( xi  x ) p ( y j  y ) q f ( xi , y j ) x y

(6)

i 0 j 0

restoration. The Combined Blur and Affine Moment Invariants (CBAMI) are given below [12].

Set1 = µ230 µ203 – 6 µ30 µ21 µ12 µ03 + 4 µ30 µ312 + 4 µ321 µ03 – 3 µ221 µ212)/ µ1000.

(15)

Set2 = µ250 µ205 – 10 µ50 µ41 µ14 µ05 +4 µ50 µ32 µ23 µ05 + 16 µ50 µ32 µ214 – 12 µ50 µ14 µ223 + 16 µ241 µ23 µ05 + 9 µ241 µ214 – 12 µ41 µ232 µ05 -72 µ32 µ41 µ14 µ23 + 48 µ323 µ41 +48 µ14 µ332 - 32 µ232 µ223) / µ1400 .

The normalized central moments [10] are defined as

 pq 

 pq  00

where

 

(7)

pq 1 for p  q  2, 3,.....

2

Hu [10] derived moment invariants using Geometric moments which are invariant to image rotation, translation and scale. These invariants are given below [10]. Ø1 = Ƞ20 + Ƞ02

(8)

Ø2 = (Ƞ20 - Ƞ02)2 + 4 Ƞ211

(9)

Ø3 = (Ƞ30 -3 Ƞ12)2 + (3Ƞ21 - Ƞ03)2

(10)

Ø4 = (Ƞ30 + Ƞ12)2 + (Ƞ21 + Ƞ03)2

(11)

Ø5 = (Ƞ30 -3 Ƞ12) (Ƞ30 + Ƞ12)[ (Ƞ30 - Ƞ12)2 – 3 (Ƞ21 - Ƞ03)2 + (3Ƞ21 + Ƞ03) (Ƞ21 + Ƞ03) [ 3(Ƞ30 - Ƞ12)2 - (Ƞ21 + Ƞ03)2 ] (12)

(16)

Set3 = (µ230µ05 µ12 - µ230µ03 µ14 - µ30µ221 µ05 - 2 µ30 µ21 µ12 µ14 +4 µ30 µ21 µ23 µ03 + 2 µ30µ212 µ23 – 4 µ30 µ12 µ32 µ03 + µ30µ203 µ41 + 3 µ321 µ14 - 6 µ221µ12 µ23 – 2 µ221µ03 µ32 + 6 µ21µ212 µ32 + 2 µ12 µ21 µ41 µ03 - µ21µ203 µ50 – 3 µ312 µ41+ µ212µ03 µ50) / µ1100.

(17) Set4 = (2 µ30 µ12 µ41 µ05 - 8 µ30 µ12 µ32 µ14 + 6 µ30 µ12 µ223 -µ30 µ03 µ50 µ05 + 3 µ30 µ03 µ41 µ14 – 2 µ30 µ03 µ23 µ32 - 2 µ221µ41 µ05 + 8 µ221µ32 µ14 - 6 µ221 µ223 + µ21 µ12 µ50 µ05 - 3 µ21 µ12 µ41 µ14 +2 µ21 µ12 µ32 µ23 + 2 µ21 µ03 µ50 µ14 – 8 µ21 µ03 µ41 µ23 +6 µ21 µ03 µ232 -2 µ212µ50 µ14 +8 µ212µ41 µ23 – 6 µ212 µ232) / µ1200.

(18)

Set5 = (µ30 µ41 µ23 µ05 - µ30 µ41 µ214 - µ30µ232 µ05 +2 µ30 µ32 µ23 µ14 - µ30 µ323 - µ21 µ50 µ23 µ05 + µ21 µ50 µ214 + µ21 µ41 µ32 µ05 - µ21 µ41 µ23 µ14 - µ21µ232 µ14 + µ21 µ32 µ223 + µ12 µ50 µ32 µ05 - µ12 µ50 µ23 µ14 - µ12µ241 µ05 + µ12 µ41 µ32 µ14 + µ12 µ41 µ223 - µ12µ232 µ23 - µ03 µ50 µ32 µ14 + µ03 µ50 µ223 + µ03µ241 µ14 -2 µ03 µ41 µ23 µ32 + µ03 µ332) / µ1300.

(19)

Ø6 = (Ƞ20 - 3 Ƞ02) [(Ƞ30 + Ƞ12)2 - (Ƞ21 + Ƞ03)2 + 4 (Ƞ30 + Ƞ12) Set6 = (µ270 µ207 – 14 µ70 µ61 µ16 µ07 + 18 µ70 µ5 2 µ25 µ07 (Ƞ21 + Ƞ03) (13) + 24 µ70 µ52 µ216 – 10 µ70 µ43 µ34 µ07 – 60 µ70 µ43 µ25 µ16 –234 µ61 µ52 µ25 µ16 + 40 µ61µ243 µ07 + 50 µ61 µ43 µ34 µ16 Ø7 = (3Ƞ21 - Ƞ03) (Ƞ30 + Ƞ12) (Ƞ30 - Ƞ12)2 -3(Ƞ21 + Ƞ03)2 + + 360 µ61 µ43 µ225 – 240 µ61µ234 µ25 + 360 µ252µ34 µ16 (Ƞ21 + Ƞ03) (Ƞ21 + Ƞ03)[3 (Ƞ30 + Ƞ12)2 - (Ƞ21 + Ƞ03)2 + 81 µ252 µ225 - 240 µ52µ243 µ16 – 990 µ52 µ43 µ34 µ25 (14) + 600µ52 µ334 +600µ343 µ25 – 375 µ243 µ234 ) / µ1800. (20) 2.2 Combined Blur and Affine Moment Invariants Jan Flusser et al. [12] derived combined blur and affine moment invariants. Combined invariants are capable of recognizing objects in the degraded scene without any

All Rights Reserved © 2017 IJARTET

22

ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com

International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 4, Issue 12, December 2017

III. PROPOSED METHOD The proposed technique for detection of TB in CXR is shown in Fig.1.

The first step is proposing which is used to remove background noise. In this work, we used median filtering for Image 1 Image 2 smoothing. Combine blur and affine moment invariants are computed on preprocessed image. Combine blur and affine moment invariants are selected as features because they are Fig.2 Some sample images used in the simulations . invariant with respect to image blur and affine distortions. In presence of these distortions also, the computed features are invariants. The extracted features are then applied for In order to test the invariance property of CBAMI under classification with and without TB features using Euclidean different distortions, we selected image as shown in Fig.3. distance classifier. Finally, the result is evaluated by expert radiologist.

IV. SIMULATION RESULTS In order to test the proposed approach, we considered The Montgomery County dataset. This dataset is collected online freely from the Department of Health and Human Services, Montgomery County, Maryland, USA. It consists of 138 frontal chest X-rays from Montgomery Countys Tuberculosis screening program, out of which 80 are normal cases and 58 are cases with manifestations of TB. These Xrays were captured with a Eureka stationary X-ray machine (CR), and are provided in Portable Network Graphics (PNG) format as 12-bit gray level images. The size of the X-rays is either 4,020x4,892 or 4,892x4,020 pixels. These images are resized to 450 x 450 pixels. Some of the database images are shown in Fig.2.

Original Test Image

Original Image Scaled by 50%

Original Image rotated by 200

Image 1

Original Image Blurred

CBAMI

Original Image

Half size

Rotated by 20 degrees

Blurred

by LPF

Set1

5.58e-22

5.58e-22

5.58e-22

5.59e-22

Set2

5.03e-32

5.02e-32

5.03e-32

5.02e-32

Set3

-5.27e-26

-5.27e-26

-5.25e-26

-5.27e-26

Set4

-5.46e-27

-5.46e-27

-5.46e-27

-5.46e-27

Set5

-1.14e-31

-1.14e-31

-1.14e-31

-1.13e-31

All Rights Reserved Š 2017Set6 IJARTET 2.86e+02

2.83e+02

2.84e+02

2.89e+02

Image 2 Fig 1

23

ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com

International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 4, Issue 12, December 2017

Fig.3. Images used for computing CBAMI

The computed CBAMI are entered in Table.1.

[4]

[5]

Table1. CBAMI invariants computed for images shown in Fig.3.

[6]

From the results, it is observed that these features are invariant with respect to affine distortions such as rotation, scale, translation as well as blur. The proposed approach is [7] applied on the database images and the the accuracy is calculated. The accuracy is 92%. For comparision, the same experiment is repeated with Geometric moment invariants as [8] features and observed that the accuracy is 88%. Hence, the CBAMI can be effectively used for extracting features of chest X ray images.

[9]

[10]

V. CONCLUSIONS

[11]

A. Harries, N. Hargreaves, J. Kwanjana, F. Salaniponi et al., “Clinical diagnosis of smear-negative pulmonary tuberculosis: an audit of diagnostic practice in hospitals in malawi,” The international journal of tuberculosis and lung disease, vol. 5, no. 12, pp. 1143– 1147, 2001.

J. Day, S. Charalambous, K. Fielding, R. Hayes, G. Churchyard, and A. Grant, “Screening for tuberculosis prior to isoniazid preventive therapy among hiv-infected gold miners in south africa,”The international journal of tuberculosis and lung disease, vol. 10, no. 5, pp. 523–529, 2006.

Sivaranjani, “ Analysis of Tuberculosis in chest using SVM classifier”, National Conference on Research Advances in Communication, Computation, Electrical Sciences and Structures, pp. 18-20, 2015 Ankitha B M , Sanjeev Kubakaddi and Dr.Bharathi S H, “Computer Aided Diagnosis of Tuberculosis usingFirst order Statistical approach”, IPASJ International Journal of Electronics & Communication (IIJEC), Volume 3, Issue 4, pp. 22-28, April 2015 Arun Chauhan, Devesh Chauhan and Chittaranjan Rout, “Role of Gist and PHOG Features in Computer-Aided Diagnosis of Tuberculosis without Segmentation”, PLOS ONE www.plosone.org 1, Volume 9, Issue 11, , November 2014 Pallavi T. P., Praveen Kumar P. S., “ Diagnosis of Tuberculosis by using first order statistics applied to chest radiograph”, International Journal of Sciences & Applied Research, IJSAR, 2(8), pp. 16-22, 2015 Hu. M.K, “ Visual Pattern Recognition by Moiments Invariants”, IRE Transaction on Information Theory, 8, 179-187, 1962 Jan Flusser and Tomas suk et al., “ Recognition of Blurred Images

by Method of Moments”, IEEE Transaction on Image Processing, vol.5, no.3, pp.533-537, March 1996 Tomas Suk and Jan Flusser, “Combined Blur and Affine Moment Invariants and their use in

This paper presents a method for detection of Tuberculosis [12] using CBAMI as features and Euclidean Distance as classifier. The proposed method computes Combined Blur BIOGRAPHY and Affine Moment Invariants (CBAMI) on X-ray image K.Durga Prasad (mail ID: and uses them as feature for detection of tuberculosis. The dpkolluru@gmail.com) received CBAMI are selected as features in this paper because they his M.Tech(DSCE) from are invariant to both affine distortions as well as blur. It is J.P.N.C.E, and B.Tech(ECE) observed from the simulation results that the detection from NimraCollege of Engg., accuracy is better. Our future work will be directed towards Currently he is working as use of neural network classifier or support vector machines Associate Professor at for classification.

Jayaprakash narayan college of engineering and has 16 years of Experience in teaching . His REFERENCES areas of interest include Digital Image Processing, Pattern [1] Anuj, Rahul Hooda and Ajay Mittal, “ TB Detection in Chest Recognition, Electro Magmetic Field,VLSI design, Antenna Radiograph Using Deep Learning Architecture”, 5th International Conferenceon Emerging Trends in Engineering, Technology, Science and wave propagation, Communication system .

[2]

[3]

and Managementpp.136-140,, IETE, August 2017 P. Quinco, S. B uhrer-Śekula, W. Branddao, R. Monte, S. L. Souza, V. Saraceni, M. Palaci, R. Dietze, and M. Cordeiro-Santos, “Increased sensitivity in diagnosis of tuberculosis in hiv-positive patients through the small-membrane-filter method of microscopy,” Journal of clinical microbiology, vol. 51, no. 9, pp. 2921– 2925, 2013.

J. Pouchot, A. Grasland, C. Collet, J. Coste, J. M. Esdaile, and P. Vinceneux, “Reliability of tuberculin skin test measurement,” Annals of internal medicine, vol. 126, no. 3, pp. 210–214, 1997.

2. M B Ramamurthy is in the academic filed for the past 36 years. Currently he is Professor ECE and Dean Academics in Jayaprakash Narayan College of Engineering, Mahabubnagar India 509001. He has 66 publicaions to his credit in International Journals and Conferences. He is senior

All Rights Reserved © 2017 IJARTET

24

ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com

International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 4, Issue 12, December 2017

member IEEE, life fellow IETE, life member IEI and life member ISOI. His areas of interest are Speech Processing, Signal Processing, Digital Image Processing and Communications.

3. Dr. A. Venkataramana received his B.E, M.E and Ph.D. degrees from Osmania University, Hyderabad. He has total 19 years of teaching experience. He worked as a Junior Telecom Officer and ITS officer in the Department of Telecom, G.O.I for a period of 4 years. He has published various papers in IEEE, international, national conferences and journals. He has received a number of prestigious awards A.P. State Government Best Lecturer in Polytechnics award, ISTE Best Polytechnic Teacher award, Bharath Siksha Ratan and Rashtra Ratna awards. He is presently working as faculty in the department of Electronics and Communication Engineering at Q.Q Government Polytechnic, Hyderabad. His areas of interests are Digital Image Processing, Pattern Recognition, Neural Networks and Applications of Moments for Image Processing Problems

All Rights Reserved Š 2017 IJARTET

25