breasts symmetrically. If the Hotspot is present in areas like armpit, neck and inframammary folds where the temperature is naturally expected to be more, then ...
RESEARCH PAPERS ARTICLE
HOTSPOT DETECTION IN INFRARED BREAST THERMOGRAMS FOR DISEASE IDENTIFICATION By A.T. WAKANKAR *
S. LALITHAKUMARI **
G.R. SURESH ***
* Research Scholar, Sathyabama University, Chennai, India. ** Associate Professor, Department of EIE, Sathyabama University, Chennai, India. *** Professor, Department of Electronics and Communication Engineering, Rajalakshmi College of Engineering, Chennai, India. Date Received: 30/06/2017
Date Revised: 01/07/2017
Date Accepted: 05/07/2017
ABSTRACT For ages, human body temperature has been used as an indicator for judging the health status. Over the years, the science of Medical Thermography has evolved to measure body surface temperatures that help in making relevant judgments about diseases. Unusual temperature patterns or the values above permissible limits indicate abnormality. In most of the cases, such temperature differences indicate a high chance of inflammation, infection or malignancy. Localized increase in temperature is termed as Hot spot and is indicative of such abnormalities. This paper develops an algorithm to detect the highest temperature region in breast thermogram to predict the breast disease. Thermal image is captured using infrared camera. The temperature data are processed to find the hotspot. The location and shape of the hotspot is detected. The thermograms are further segmented in left and right part manually. Statistical parameters are calculated from temperature data of segmented region. Significant differences in these parameters is observed for healthy and sick cases showing asymmetry. The presence of asymmetric hotspot suggests the further follow up. Results are validated by a radiologist confirming the performance of the algorithm. Keywords: Breast Disease, Thermography, Thermal Analysis, Hotspot Detection, Asymmetry Analysis. INTRODUCTION
tests that are very safe. Thermography, is non-invasive,
Breast cancer has been known for decades to be the
non-contact skin surface temperature screening test that
most common type of cancer among women. The
is economic, quick and does not inflict any pain on the
incidence of breast cancer in India is on the rise and is
patient [1]. Human body emits infrared rays in proportion
rapidly becoming the number one cancer in women [1].
to the body temperature. Thermal camera converts this
Breast cancer can be treated effectively only if it is
Infrared (IR) energy into electrical signals by the imaging
detected at an earlier stage. A range of imaging
sensor in the camera and displays on a monitor as a color
methods like mammography, ultrasound and MRI are
image that represents the variations of the temperature
available for breast cancer detection [3].
values [5, 9]. The examination of female breast with IR
Mammography is considered as the Gold standard
camera for diagnosing the breast cancer is based on the
i m a g i n g t e c h n o l o g y f o r b r e a s t c a n c e r. B u t,
fact that cancer tissue metabolizes more actively than
mammography is structural imaging modality that
other normal tissues and thus has a higher temperature.
exposes the breast to Xray radiations on compressed
The heat thus produced is conveyed to the skin surface
breasts. Due to high compression of breasts and
resulting in a higher temperature in the skin directly over
repeated exposure to toxic Xray mammography is not
the malignancy than in other regions [2]. Hence, the
ideal for patients [4]. Infrared Imaging is fast emerging as an option for breast
temperature distribution is altered, causing thermal asymmetry between the right and the left breasts. Thus,
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RESEARCH PAPERS ARTICLE the cancer cells can be imaged as hotspots in the
cases. The patients with higher LTI amplitudes have higher
infrared images [3]. In a normal healthy subject, the skin
carcinomous possibility. It determined the optimal LTI
temperature pattern is remarkably consistent showing
amplitude threshold of 1˚C for breast cancer detection
bilateral symmetry. The asymmetric pattern indicates the
from the Receiver Operating Characteristic (ROC) curve
deviation from the normal brought out by pathological
based on the rule of Youden's index maximization [6].
changes [2].
2. Methodology
The main objective of this work is to evaluate the feasibility
Breast Thermograms are captured by thermal camera and
of thermography as a breast imaging modality. Hotspot
are processed for hotspot detection. Hot spot detection
Detection Algorithm is developed in order to determine
provides the highest temperature area extraction, in order
the spots with highest temperature which indicates the
to determine the presence of lumps in the breast. The
presence of abnormality.
breast thermograms are further segmented manually in left
1. Literature Review
and right part. Statistical parameters like mean, median,
The first use of thermography was by Gorman in 1939 that
variance, skewness, kurtosis and entropy are calculated.
evaluated changes in vascular structure in breasts during
The significant difference is observed for healthy and
pregnancy with infrared photography. Lawson 1956, 57
abnormal cases. The results of algorithm are validated by a
and Amalrie 1957 initially published research regarding
radiologist. Camera software 'SMARTVIEW' is used to view
thermography and breast cancer. In 1963, Lawson and
and analyze the image.
Chughtai demonstrated empirical evidence that breast
2.1 Image Acquisition
malignancy alters regional skin surface temperature.
Infrared Camera FLUKE TiX 560 having a spectral
V. Umadevi has developed an ITBIC interpretation system
response of 8 μm to 14 μm and IR Resolution of 320X240 is
for characterization of breast thermal images. This is a tool
used for image acquisition at Magnolia Breast Care
for differentiation of breast thermal images as normal or a
Center, Pune under the guidance of Dr. Shekhar Kulkarni.
case for follow-up, which is helpful for initial and mass
Recommended acquisition protocol is followed to
screening. Statistical analysis was carried out on the
improve accuracy. The test was carried out in AC room,
results of ITBIC interpreter which resulted in Positive
which was darkened during the test and the temperature
Predictive Values (PPV) of 80%, Negative Predictive Values
is adjusted between 18 to 22 degrees Celsius. Total 20
(NPV) of 95.6%, Sensitivity of 66.7% and Specificity of
images are acquired, out of which 7 are sick and 13 are
97.7% [7]. Iman Zare, Ahmad Ghafarpour, et al described
normal cases.
that thermography is suitable compared to the
Thermal images captured by an infrared camera can be
ultrasound diagnosis in detecting the breast tissue
viewed using different color palettes. Breast Thermogram
diseases such as cystic masses and hypo echo masses
in Different Color Palates are shown in Figure 1.
by adopting asymmetr y technique. This study emphasizes that thermography should not be used for the first time diagnosis. This technology needs accurate clinical evaluation and it is likely that thermography can be a part of breast screening, detecting and etc. in near future [10]. Xianwu tang, Haishu Ding, et al described the morphological measurement of local temperature increase (LTI) amplitudes in breast infrared thermograms for breast cancer detection. By applying morphological approaches, they obtained LTI amplitudes, which have significant difference between benign and malignant 22
Figure 1(a), 1(b), 1(c) & 1 (d) is representation of the breast thermal image when viewed in different color palettes. Each breast thermogram is best viewed in a certain temperature scale and in a specific color palette. The blue red colour scale is selected in our work. Specific camera compatible software 'SMARTVIEW' is used for viewing the thermal images. Sample screenshot of SMARTVIEW is shown in Figure 2. 2.2 Sample Thermograms Figure 3 shows the thermograms of volunteers with
No. 4 l December 2016 - February 2017 i-manager’s Journal on Pattern Recognition, Vol. 3 l
RESEARCH PAPERS ARTICLE abnormal breast and with normal breast respectively. The
The thermogram is further analyzed through software by
color bar present on the thermograms gives information
selecting marker. The minimum, maximum and average
about temperature distribution indicating the coolest part
temperature of the selected area is shown in Figure 4. It
as blue and the warmest part of the as red as shown in figure
can be observed that, the left breast of volunteer is having
[8]. For abnormal cases, higher temperature indicated by
more temperature as compared to right breast. The
red color and the bilateral asymmetry can be seen.
average temperature difference of around 1˚C is observed for abnormal cases. This particular subject had undergone mammography and the lump was detected. 2.3 Hotspot Detection Each thermal image is a two dimensional matrix of size 320*240, which is the representation of breast surface
(a)
(b)
temperature. It has coordinates x and y and T(x,y) is the temperature of that pixel. The Hotspot detection algorithm consists of two parts: Body Boundary detection and extraction of highest temperature area in the thermogram. Then both the outputs are combined to create a single image which is easy to interpret [7]. The
(c) (d) Figure 1. Breast Thermogram in Different Color Palates 1a) Blue Red 1b) High Contrast 1c) Hot Metal 1d) Grayscale
flowchart of the process is as shown in Figure 5. The temperature file of the whole breast thermogram is exported as an excel file. This file is read in MATLAB which gives a matrix of 320*240 values. The minimum temperature Tmin value is found out indicating the ambient temperature. Any value less than Tmin is made zero and shown as black. While values greater than Tmin are made 1, indicating the body surface temperature. Edge detector algorithm is then applied to this new matrix to detect body contour. Figure 6(a) and 6(b) shows the output of Background and Edge detection.
Figure 2. Screenshot of SMARTVIEW
To detect the hot spot, highest value of temperature Tmax is found out from the original temperature matrix. Again these values are compared with a threshold of (Tmax-1)°C. The threshold of 1°C The threshold of 1°C is selected after
Figure 3. Sample Thermograms
Figure 4. Difference in Average Temperature
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RESEARCH PAPERS ARTICLE analyzing the image through SMARTVIEW where we can
generates the temperature data of segmented region.
see that the abnormal area is having a higher average
Asymmetry analysis is then performed using different
temperature of around 1°C. Any value above the
statistical parameters. The experimental results show that
threshold is marked as 1 and values less than threshold
there is a significant difference in these parameters for
makes zero. With this logic, areas of highest temperature is
healthy and sick subjects and thus they can be used for
extracted. Finally, the output matrices generated by body
detection of breast abnormality. Figure 8 shows the
boundar y identification algorithm and highest
process of segmentation.
temperature area extraction algorithm are combined
2.5 Feature Extraction for Asymmetry Analysis
together to generate a processed image. Figure 7 (a) and 7 (b) show the output of hotspot detection and final combined image. The processed image is easy to interpret and understand.
Feature Extraction is performed on segmented thermograms which includes a comprehensive set of features like Skewness, Kurtosis, Median, Variance, Standard Deviation, and Mean to completely study the
2.4 Segmentation
nature of temperature distribution in Region of Interest
The breast thermograms are segmented in Left and Right
(ROI).
breast manually using crop function in SMARTVIEW which
3. Results & Discussion The results are treated as 'Normal' case if the Hotspot does not appear in any of the breasts OR it is present in both the breasts symmetrically. If the Hotspot is present in areas like armpit, neck and inframammary folds where the temperature is naturally expected to be more, then also it is considered as 'Normal'. The thermogram is considered as 'Abnormal' if the hotspot appears only in any one of the breast part.
(a)
(b)
Figure 6. (a) Background Detection, (b) Edge Detection
(a) Figure 5. System Flowchart
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(b)
Figure 7. (a) Hotspot Detection, (b) Final Output
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RESEARCH PAPERS ARTICLE that the thermal distribution of ROI for Normal Case is almost symmetrical in both the breasts as compared to the distribution of ROI for Abnormal Case which is distinctly asymmetrical in Left & Right breast as shown in Figure 11 (b). 3.4 Results of Feature Extraction The statistical parameter values variy between contralateral breasts in case of abnormality and is nearly same for symmetric cases. A graphical representation of the parameters is plotted for more precise presentation of the evaluated parameters and for showcasing the symmetry between the contralateral breasts. The Figure 8. Process of Segmentation
horizontal axis of the graphs represents the different statistical parameters, whereas the vertical axis represent
3.1 Abnormal Case Breast Thermograms
its values. Figure 12 (a) shows the graph of the first three
Highest temperature area appears only in left part of the
parameters, i.e. Skewness, Kurtosis, and Median. Figure 12
breast and not in right part of the breast as shown in Figure
(b) shows the graph of the remaining three parameters i.e.
9 (a) and (b). The other hotspots present at neck, armpit
Variance, Std. Deviation and Mean. Figure 12 (a) and (b)
and centre of breast are to be ignored since the
shows the graphs of parameters for normal thermograms.
temperature of these areas is naturally high. In both
Thus, almost no deviation in the values of the contralateral
thermograms, highest temperature area appears only in
breasts is clearly observed from the graph. Figure 13 (a)
any one of the breasts, which is a sign of asymmetry, and
and (b) presents the graphs for abnormal case.
thus treated as abnormal cases.
Thus, the deviation in the values of the contralateral
3.2 Normal Case Breast Thermograms
breasts is clearly observed from the graph.
Highest temperature area appears below left and right
Conclusion
parts of the breast, because of folds across the breast as shown in Figure 10 (a) and (b). No hotspot is present in the breast area. Symmetry exists in appearance of highest temperature area of both left and right parts of the breast, which implies that these are normal case thermograms.
This work presents an approach which deals with analysis of breast thermograms based on hotspot identification for abnormality detection. Experimental results of Hotspot detection show that the proposed approach is able to accurately detect the location and shape of the hot
3.3 Histogram
region in the thermogram. The presence of such hotspot
Feature Analysis is based on Histogram Generation.
is an indication of lump and needs further follow-up.
Figure 11 (a) shows the histogram of Normal Case. It shows
Asymmetry analysis based on statistical features supports
(a)
(b)
Figure 9. (a) Abnormal Case 1, (b) Abnormal Case 2
(a)
(b)
Figure 10. (a) Normal Case 1, (b) Normal Case 2
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RESEARCH PAPERS ARTICLE
(a)
(a)
(b) (b) Figure 11. (a) Histogram of Normal Case, (b) Histogram of Abnormal Case
Figure 10. Statistical Parameters Representation for Sick Case (a) Skewness, Kurtosis and Median Parameters (b) Varience, Std Deviation and Mean Parametrs
the abnormality detection. The results are validated by a doctor's diagnosis and are cross checked by camera software where individual pixel temperatures can be monitored. It is difficult to identify deep tumors since they don't show remarkable skin temperature difference in image. To improve decision making in all such type of cases, more data points are necessary for drawing relevant conclusions. Acknowledgment
(a)
The author would like to thank Dr. Shekhar Kulkarni for his valuable guidance and support during image acquisition. References [1]. Kapoor, P., & Patni, S. (2012). Image segmentation and asymmetry analysis of breast thermograms for tumor detection. International Journal of Computer Applications, 50(9) 40-45.
(b) Figure 12. Statistical Parameters Representation for Healthy Case (a) Skewness, Kurtosis and Median Parameters, (b) Varience, Std Deviation and Mean Parameters
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ABOUT THE AUTHORS Asmita T. Wakankar is currently pursuing her Ph.D at Sathyabama University, Chennai. She has done her ME (Biomedical Instrumentation) from Govt. College of Engineering, Pune in 2002. She is working as an Assistant Professor at Instrumentation & Control Department, Cummins College of Engineering, Pune. She has published 25 papers in journals and conferences. Her areas of interest are Biomedical Instrumentation and Image Processing.
Dr. S. Lalithakumari is working as an Associate Professor in the Department of EIE of Sathyabama University, Chennai. She has completed graduation from Bharathidasan University, Thiruchirappalli (1993) in Electronics and Communication Engineering Discipline. She obtained her post Graduation in Power Electronics from Bharathidasan University, Thiruchirappalli (1999). She obtained her Ph.D from Sathyabama University, Chennai (2014). She has teaching experience of about 18 years. She has published around 30 papers in reputed journals and has filed 5 patents so far. Her research interest includes Non Destructive Testing, Image & Signal Processing and Soft Computing.
Dr. G.R. Suresh is currently working as a professor in Rajlakshmi Institute of Technology, Chennai. He received his Bachelor of Engineering Degree in Electronics and Communication from Manonmaniam Sundaranar University in 1997, Master of Engineering degree in communication systems from Madurai Kamaraj University, Madurai in 2000 and PhD degree in the Faculty of Information and Communication Engineering from Anna University, Chennai in 2010. He has more than 19 years of experience in teaching at under graduate and graduate level. He has published more than 62 Research papers in Journals and Conferences. He has received the Computer Engineering division prize from the Institution of Engineers (India) for the year 2009. He is an active reviewer in the journals, IET image Processing and International journal of Electronics. His research area includes Medical Signal & Image processing, Speech processing and WSN in Telemedicine. He is a member of IEEE, IET and life member of ISTE.
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