Non Destructive Diagnostic Tool for Early Detection of Breast Cancer

Non Destructive Diagnostic Tool for Early Detection of Breast Cancer More Info at Open Access Database www.ndt.net/?id=15104 Josephine Selle Jeyanathan 1, A. Shenbagavalli 2, B. Venkataraman 3 and M. Jayashree 4 1, 2 National Engineering College, Kovilpatti -628 503, India 3 Indira Gandhi Centre for Atomic Research, Kalpakkam 603 102, India 4 Department of Atomic Energy Hospital, Kalpakkam 603 102, India Email: josieselle@gmail.com Abstract - Several years of research and advances in the field of medical thermography has proved its potential to detect breast cancer in the nascent stages itself. Being the most common disease in India, one among eight women surely develops cancer over her lifetime. Though technologies in gold standard tool i.e. X-ray Mammography is advancing, it can detect the tumour only when it attains a certain size. Moreover, Indian women have denser breasts which becomes difficult to be screened unless they are above the age 40. A non-invasive at the same time a zero radiation screening tool is need of the hour for early detection of breast cancer. Thermography is capable of indicating changes in thermal patterns from the body and distinguishes whether it is symmetric or asymmetric. Asymmetric vascular patterns indicate abnormal characteristics on the breast which is useful in segmenting the region of interest. One of the best ways to carry out this type of analysis is through deriving the features that describe the asymmetric patterns. They include first order statistical features, moments and texture features. There are many computer aided detection tools under research to help the radiologist in interpreting the thermal breast images. This paper includes a similar method involving feature extraction that is used to describe a pattern which can clearly signify various disorders in the breast. I. INTRODUCTION Cells are the building blocks of organs and tissues, cancer is caused due to the diseases in such cells. Cells in different parts of the body have different roles to play but most reproduce themselves in the same way. Cells constantly shed and die, but new cells are produced to replace them. Cells normally divide in an orderly and controlled manner. If the process gets out of control, the cells tend to divide and start developing into a lump called as tumour. Namely there are two types of tumours; benign or malignant. A malignant tumour is thus called Cancer. A biopsy decides whether the tumour is malignant or benign. In a benign tumour the cells do not spread to other parts of the body and so are not cancerous. However, if they continue to spread to other sites, they may cause a problem by pressing on the surrounding organs. A malignant tumour consists of cells that have the ability to spread beyond the original area. If the tumour is left undetected, it may spread further and destroy surrounding tissues. In India, Breast Cancer is the second most common cancer (after cervical cancer) with an estimated 1, 15,251 new diagnoses every year [2]. Breast cancer accounts for 22.2 percent of all new cancer diagnoses and 17.2 percent of all cancer deaths among women in India. Fig.1. Survival rate Breast cancer in urban area of India is three times higher than in rural parts of the country. According to Indian Council of Medical Research (ICMR), the average age group from 25 years to 80 years old fall more prone to breast cancer. The death rates are gradually decreasing since 2000, due to the advance in treatment, earlier detection through screening, and increased awareness. The best way to prevent cancer is by early screening [1], which include breast self exams (BSE) monthly, clinical breast exams every three years and mammographic screening annually starting at age of 40. A clinical breast exam (CBE) is performed by the clinician and involves a systematic examination of the breast skin and tissue. The clinician looks for any lump or swelling, skin irritation or dimpling, nipple pain or retraction, redness or scaliness of the nipple or breast skin or discharge other than breast milk. The breast cancer patient has an 85% chance of cure if the cancer is diagnosed early. The oncologists calculate a breast cancer's development in terms of five consecutive stages. As the stage in which the cancer is found increases, there is a decrease in survival rate, as shown in Fig.1. Hence, early screening would make real difference as the treatment becomes simpler and more effective. II. THERMAL INFRARED IMAGING The concept of thermography which is also called as thermal infrared imaging is derived from the evolution of infrared spectrum by Sir William Herschel in AD 1800. The principle of thermal imaging lies between the relationship between the temperature of the objects and the intensity of infrared radiation. Universally all objects emit radiations in the IR region of the spectra as the function of its temperature. As an object gets hotter, it gives off more intense infrared radiation, and it radiates at a shorter wavelength [3].

At moderate temperatures (above 200 F or 93.3 degree Celsius), the intensity of the radiation gets high enough so that the human body can detect that radiation as heat. At high temperatures (above 500 F or 260 degree Celsius), the intensity increases so that the radiation crosses over the threshold to the red end of the visible light spectrum. Human eye cannot capture IR rays; hence, a Digital Infrared Thermal Imaging (DITI) camera is used to obtain infrared images called as thermogram [4]. This device converts the infrared radiation emitted from the skin surface into electrical impulses that are visualised in colour on a monitor. The body temperature is graphically mapped which is termed as a thermogram. The spectrum of colours is calibrated by the amount of infrared radiation being emitted from the body surface. Since the normal body shows perfect thermal symmetry, subtle abnormal temperature asymmetry can be easily identified. As temperature is the best indicator of health, in whatever part of the body, excess of heat or cold is felt, the disease can be easily discovered. A thermal imaging setup is shown in Fig.2. Hairong Qi et al introduced an approach using canny edge detector to detect the edges and hough transform for detecting the four feature curves namely; right and left upper boundary curves and two lower parabolic boundary curves of the breasts which is useful in segmentation, to analyse the specific region. Bezier Histogram was used to compute curvature information to indicate the asymmetry in the patterns [13]-[16]. A series of research by J. F. Head et al was carried out where they calculated the mean temperature difference for the four quadrants and the whole breast. The difference greater than 0.5 degree C for whole breast and 1.0 degree C for quadrants were considered asymmetric and abnormal. Later they also proposed an automated asymmetric analysis which includes automated segmentation and pattern classification. The automated approach used unsupervised learning technique namely the K-means clustering to classify the segmented pixels into different clusters. The asymmetric variation was identified based on pixel distribution in the same cluster [17] [24]. The features played an important role both in supervised as well as unsupervised learning algorithms. For an effective CAD tool, it is necessary to find the appropriate parameters which can classify the abnormalities correctly. IV. PROPOSED WORK This work consists of calculating fifteen features and for a normal as well as an abnormal breast thermogram. Following that, asymmetric analysis is made by cumulative histograms for both the images with respect to pixel area and temperature scaling. Fig.2. Thermal Imaging Setup Thermography is of two types namely, passive and active thermography. In passive thermography, the camera is simply pointed at the test piece and from the thermal image a temperature map is constructed. Whereas, active thermography involves heating the surface of the object rapidly using an external heat source and observing how the temperature decays with time. Passive thermography has paved way for diagnosis and detection of breast cancer by indicating the physiological information of tumor in terms of higher temperature which relates to the changes in the metabolism. III. LITERATURE SURVEY Various research papers and literatures have talked about thermal imaging and its viability in the field of medicine. The very first finding took place in identifying the asymmetric breasts [1]. Snyder et al discovered that by analysing the contra lateral breasts would make it easy to detect the suspicion region. For that it was necessary to perform some segmentation process for individual breast. The breasts regions were made into four quadrant and and each quadrant was used to calculate the temperature difference [6]-[9]. A. Feature Extraction In this section we have carried out texture analysis for normal and abnormal images. The left and right side of the breasts were manually segmented. The features include mean, standard deviation, variance, smoothness index, entropy, median, skewness, kurtosis, energy, cluster shade, cluster performance, area, perimeter, eccentricity and Fourier features. Mean: The mean represents the average value of the pixels. It is the ratio of summation of all of the samples to N, total number of samples. It is given as: Mean=, (1) p(i,j) Standard deviation: Standard deviation shows how much variation exists from the mean value. It is defined as the square root of the variance. It is given as: Standard Deviation= (p(i,j) S, ) (2) Entropy: The statistical measure of randomness that can be used to characterize the texture of the input image is known as entropy which is given as;

123!"#$%&' = ),*3((),*)+,-. /((),*)0 (3) Median: The numerical value that separates the higher half of a data sample from the lower half is called as median. The median of an image can be found by arranging all the pixels from lowest value to highest value and selecting the middle pixel value. If there is an even number of pixels, then there is no single middle value; the median is then usually defined to be the mean of the two middle values. Skewness: Skewness is defined as the measure of asymmetry of the probability distribution of a real-valued random variable. The skewness value can be positive or negative, or even undefined. The tail on the left side of the probability density function represents a negative skew. The tail on the right side of the probability density function represents a positive skew. A zero value indicates that the values are evenly distributed on both sides of the mean. Negative skew: The mass of the distribution is concentrated on the right of the figure. It has relatively few low values. It can also be called as left-skewed, left-tailed, or skewed to the left. Positive skew: The right tail is longer; the mass of the distribution is concentrated on the left of the figure. It has relatively few high values. The distribution is said to be rightskewed, right-tailed, or skewed to the right. The skewness of a random variable X is the third standardized moment, denoted γ 1 and defined as: Skewness= 3 7 8 123 ),*3 (((),*) 9 3 ) 8 ((),*) (4) Kurtosis: In probability theory and statistics, kurtosis is a measure of the "peakness" of the probability distribution of a real-valued random variable. In a similar way to the concept of skewness, kurtosis is a descriptor of the shape of a probability distribution and, just as for skewness, there are different ways of quantifying it for a theoretical distribution and corresponding ways of estimating it from a sample from a population. Kurtosis= < = @2, (I(i,j) S )? I(i,j) (5) Energy: Energy is like the "information present on the image". Energy is used to describe a measure of information when formulating an operation under a probability framework such as MAP (maximum a priori) estimation in conjunction with Markov Random Fields. Energy= D E = K23 H,I3 FG(H,I)J. (6) Cluster shade: By using cluster shade, it is possible to reduce the number of operations by expressing this texture feature in terms of the image pixel values contained in a given M N neighborhood containing G gray levels from 0 to G 1, where f(m, n) is the intensity at sample m, line n of the neighborhood. V Cluster shade= OH P Q + I P S T 8 H,I3 U(H,I) (7) V Cluster Prominence= OH P Q + I P S T Z H,I3 U(H,I) Area: It is a Scalar. It is defined as the actual number of pixels in the region. (This value might differ slightly from the value returned by bw area, which weights different patterns of pixels differently.) a [$\] = ^ _(`) b dx (8) Perimeter: It is also scalar value. It is defined as the distance around the boundary of the region. Region props compute the perimeter by calculating the distance between each adjoining pair of pixels around the border of the region. If the image contains discontinuous regions, region props returns unexpected results. The following figure shows the pixels included in the perimeter calculation for this object. Eccentricity: Scalar that specifies the eccentricity of the ellipse that has the same second-moments as the region. The eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 and 1. (0 and 1 are degenerate cases; an ellipse whose eccentricity is 0 is actually a circle, while an ellipse whose eccentricity is 1 is a line segment.) This property is supported only for 2-D input label matrices. B. Asymmetric Analysis Table 1 show the quantitative features for both normal and abnormal images. The prominent asymmetry is indicated by the cumulative histogram which is plotted against pixel area and temperature values. Fig.3. Shows the cumulative histogram between the right and left breasts variation in which it shows a contrast difference in variation of the temperature in contralateral breasts. It is observed that one curve is obviously steeper than the other. The steeper curve indicates the abnormal variation in the right side of the breast. By using the features such as variance, skewness and mean temperature values, the abnormality can be profoundly found for further analysis. I. CONCLUSION Thermography has been explored as an effective screening tool for breast cancer. The Infrared Imaging technique is capable of detecting the onset of any abnormality in the body with respect to the changes in temperature distribution due to the metabolic imbalance. With the latest image processing algorithms being implemented, it is necessary to take in account the various other abnormalities that go unnoticed in and around breast areas which also could act as artifacts. Hence, right segmentation technique and features should be derived which helps in producing a robust

computer aided tool. Thus the interpretation with the help of CAD tool becomes easy to detect diseases like breast cancer where the reason for the occurrence is not totally known and helps increasing the survival rate among the patients since the early diagnosis of the disease is more curable than in a later stage. TABLE I FEATURES DERIVES FOR NORMAL AND ABNORMAL BREAST THERMOGRAMS NORMAL ABNORMAL Features Right Left Right Left Mean 181.44 180.25 140.98 158.86 Standard Deviation 15.39 15.90 27.49 27.51 Variance 3.92 3.98 5.24 5.24 Smoothness Index 0.99 0.99 0.99 0.99 Entropy 5.22 5.27 6.50 6.54 Median 187 187 139 164 Kurtosis 9.71 9.62 4.06 4.85 Skewness 9.08 9.12 3.97 4.70 Energy 32923.63 32493.1 19877.72 25237.82 Cluster shade 0.09 0.07 0.09 0.13 Cluster Prominence 0.09 0.07 0.08 0.13 Area 9766 13242 3416 6124 Perimeter 535 913 722 941 Eccentricity 1.07 0.74 1.04 0.56 Fourier Feature 0.42 0.19 0.08 0.086 (a) (b) Fig.3. (a) Cumulative Histogram for right and left normal heat patterns (b) Cumulative Histogram for right and left abnormal heat patterns REFERENCES [1] Radhika Sivaramakrishna, Foreword: Imaging Techniques Alternative to Mammography for Early Detection of Breast Cancer, Technology in Cancer Research & Treatment, vol. 4, No. 1, February 2005. [2] Anita Khokhar, Breast cancer in India: where do we stand and where do we go? Asian Pacific Journal of Cancer Prevention, vol. 13, pp. 4861-4866, 2012. [3] F. Jones. A reappraisal of the use of infrared thermal image analysis in medicine. IEEE Transactions on Medical Imaging, vol. 17, issue 6, pp.1019 1027, December 1998. [4] Hairong Qi, Detecting Breast cancer from thermal infrared images by asymmetry analysis, DAMD17-01-1-0640, February 2003. [5] E. Y. K. Ng and N. M. Sudarshan. Numerical computation as a tool to aid thermographic interpretation. Journal of Medical Engineering and Technology, 25(2):53 60, March/April 2001. [6] G. A. Hay. Medical Image: Formation, Perception and Measurement. The Institute of Physics and John Wiley & Sons, 1976. [7] J. Watmough. The role of thermographic imaging in breast screening, discussion by cr hill. In Medical Images: formation, perception and measurement 7th L H Gray Conference: Medical Images, pages 142 158, 1976. [8] V.Umadevi.S.Suresh,S.V Raghavan Improved infrared thermography based image construction for biomedical applications using markov chain Monte Carlo method Annual International Conference,IEEE EMBS, September 2009. [9] J. R. Keyserlingk, P. D. Ahlgren, E. Yu, N. Belliveau, and M. Yassa. Functional infrared imaging of the breast. IEEE Engineering in Medicine and Biology, pages 30 41, May/June 2000. [10] M. Gautherie. Atlas of breast thermography with specific guidelines for examination and interpretation (Milan, Italy: PAPUSA). 1989. [11] Y. K. Ng, L. N. Ung, F. C. Ng, and L. S. J. Sim. Statistical analysis of healthy and malignant breast thermography. Journal of Medical Engineering and Technology, 25(6):253 263, November/December 2001. [12] W. E. Snyder, H. Qi, l. Elliot and C. X. Wang. Increasing the effective resolution of thermal infrared images, IEEE engineering in Medicine and Biology Magazine, 19(3): 63-70, May/June 2000.

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