EXTRACT THE BREAST CANCER IN MAMMOGRAM IMAGES

International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 02, February 2019, pp. 96-105, Article ID: IJCIET_10_02_012 Available online at http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=10&itype=02 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 IAEME Publication Scopus Indexed EXTRACT THE BREAST CANCER IN MAMMOGRAM IMAGES Department of Computer, College of Science, Mustansiriyah university, Baghdad, Iraq ABSTRACT Image processing techniques play a significant role in many areas in life, especially in medical images, where they play a prominent role in diagnosing many diseases such as detection of the brain tumor, breast cancer, kidney cancer, and the fractions. Breast cancer is a common disease, regardless of the type of this disease, whether it is benign or malignant, it is very dangerous and early detection may reduce the risk of the disease spreading in the body leading to death. This work presents an approach to detect breast cancer based on image processing algorithms, including image preprocessing, enhancement, segmentation, Morphological operations, and feature extraction to detect and extract the breast cancer region. Keywords: Brest cancer, Mammogram segmentation, Morphological operations. Cite this Article:, Extract the Breast Cancer in Mammogram Images, International Journal of Civil Engineering and Technology, 10(02), 2019, pp. 96 105 http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=10&itype=02 1. INTRODUCTION Breast cancer for both genders begins in the breast cells and expands to other parts of the body. In today's world, breast cancer is highly preponderant. It is the second most common type of cancer after lung cancer. Therefore, early diagnosis of cancer is crucial. Masses are the first sign of malignant cancer which determined by the spaces that can be realized by lesions and can be indicated by their constructional figures and marginal characteristics. The second most common sign of breast cancer is architectonic disfiguration which is accepted with progression in improving domain, a new term has been presented by the information technology (IT) as Medical Image Processing (MIP), which has an investigating distinct feature in different fields. Detecting of a mass as cancer in an infected breast becomes easy by using image-processing techniques. The mammogram image is the first test used for checking and diagnosing the breast cancer, therefore (analysis and processing images) proposed as keys to improving cancer diagnosis. http://www.iaeme.com/ijciet/index.asp 96 editor@iaeme.com

Cabrera et al. in [1] suggested an efficient analysis of mammogram images to reveal a tumor in the early stages depending on texture segmentation. Kral and Lenc in [2] proposed an algorithm for detecting cancer using mammogram images of the breast. The proposed algorithm effectively uses ((LBP) Local Binary Patterns) based features for classification in addition to thresholding. The algorithm evaluated on a subset of images selected from (MIAS) and (DDSM) databases that have achieved 84% of accuracy. Lenardo et al. in [3], proposed masses detection algorithm on digital mammograms images. They suggested using K-means algorithm to segment the image with a co-occurrence matrix for describing each segment texture. The idea of medical diagnosis of breast cancer is not new but a suitable approach for early detection of cancer is still a challenging one. With progression in computer sciences, IT contribution has introduced a new dimension termed as Medical Image Processing (MIP). By using image-processing techniques, it has become easier to discover cancer masses in an infected body. The computer-aided detection method described as a first preprocessing step, [4] removes any unwanted noise and enhances the features of the image. The second step is the segmentation, which achieved by dividing the image into small parts. Then, the classification technique applied to extract the useful features. Vasantha and Bharathi [5] tried to improve the filtering method to implement noiseremoving process. This technique achieved by applying low-pass and high-pass filters. The first filter process removes noise while distorts the edges, smooth the image but chokes the image information. Therefore, the second filter is useful to enhance image information. Nagi J. [6] suggested to use adaptive mean filter method which developed by processing a region inside a rectangle shape. Applying the aforementioned method makes the image smoother by filling the information of the image neighborhood without blurring the edges and maintaining image details. Shan et al. [7] suggested a promising method called mean filter, which takes the identical intensities from the neighborhood pixels and replaces each unclear pixel with that identical intensities. This method produces a smooth image, improves the image quality and eliminates the Gaussian noise. Thangavel et al. [8] proposed a histogram equalization method based on pre-processing the mammogram images and improving the gray-scale quality. This method used to enhance the contrast illumination of that image and obtain better views. Another approach proposed by Maitra et al. [9]. They suggested a seeded region growing method. The seed of the tumor region grows into large population providing connected regions. The method can be robust and restriction free, based on order of pixel processing. 2. GENERAL PRESENTATION The proposed method for the detection of breast cancer uses a two-stage pipeline wherein each stage an action is applied. These stages are summarized as follow: the selection of mammogram breast image and the processing operations will then applied using two suggested methods. The latter technical methods detect and extract the cancer region in the mammogram breast image. Figure 1 illustrates the block diagram of the proposed method. http://www.iaeme.com/ijciet/index.asp 97 editor@iaeme.com

Extract the Breast Cancer in Mammogram Images 3. METHODOLOGY Figure 1: Block diagram of the proposed method. 3.1. Manual Segmentation Technique In medical images, manual segmentation technique is commonly used. The specialist depends on the visual observation of the desired area. Although this method is rapid and easy to implement, it suffers from difficulties with applying it to complex shapes [10]. Figure 2 shows the implementation of manual extraction the cancer region. Figure 2: Manual Extraction of cancer region. 3.2. Detect and Extract the Cancer Region Automatically The major steps to detect and extract the cancer region from the mammogram breast image are: Enhance the breast image using Average filter Histogram equalization Binarization the thresholding mammogram image. Apply Morphological Operation Isolation the cancer region Extract the features of this region. http://www.iaeme.com/ijciet/index.asp 98 editor@iaeme.com

3.2.1. Enhancement the Image The Mammogram image like any other images suffered from the noise. The average filter is used to remove the unwanted noise. Figure 3 illustrates the applying of the average filter to the mammogram breast Image. Figure 3: Applying average filter to the input of mammogram image. 3.2.2. Histogram Equalization The essential step in detecting the cancer region in the mammogram is the histogram equalization, where the relative frequency of the pixels in a given image is shown. There is no uniform changeable of the image, due to external conditions, to a uniform shape. Figure 4 shows the implementation of the histogram equalization to the filtered image. Figure 4: Histogram equalization for the filtered image. 3.2.3. Binarization the Threshold Image To determine the boundary of the breast from the rest of the image, the filtered mammogram image needs to convert to a binary image. This technique achieved by Binarization the threshold image [11] as illustrated in Figure 5. http://www.iaeme.com/ijciet/index.asp 99 editor@iaeme.com

Extract the Breast Cancer in Mammogram Images Figure.5: Binarization the thresholded image 3.2.4. Morphological Operation During this step, a comparison takes place between regions and neighborhood while indexing each region with a binary image. A morphological technique named (Structural pixels) is used to evaluate a small shape or template. The structuring pixels are placed in different possible locations in the image and then matched to the pixels of the identical neighborhood. The process then starts by examining the pixel to classify if it is "fit" within the neighborhood, or else, it "hits" or intersects the neighborhood. The tumor region is represented by the region with the greatest intensity which has gone through subsequent detection by labeling the regions with numbers. 3.2.5. Isolation the Cancer Region The high-density in the image determine the isolation of the cancer region from the remaining mammogram image. Figure 6 shows the detection and extraction of the cancer region from the mammogram breast image. Figure 6: Detection and Extraction of the cancer Region in mammogram breast image. 4. RESULTS AND DISCUSSIONS The proposed application is created using MATLAB software [12, 13] as shown in Figure 7. The GUI allows users to load the mammogram breast image and then isolated the cancer region from the remaining image; the statistical features with histogram then extracted from this region. This procedure will open the way in the future to use these features to distinguish the cancer region either benign or malignancy. http://www.iaeme.com/ijciet/index.asp 100 editor@iaeme.com

Figure 7: GUI for Proposed System To measure the accuracy and the sufficient of this method, 400 mammogram breast images are considered for testing to localize the cancer region; the suggested method is successful in isolating the cancer region for 380 (95%) of images in a correct manner. However, the remaining (5%) of unsuccessful images is noticed either by the wrong way of taking the image or because of the high blurring ratio of mammogram breast image. Therefore, the obtaining results compared with the manual segmentation process; the overall error of detection process calculated based on Equation 4: (1) Where EC is the overall error, MC is the missed cancer regions, and FC is the false-positive of cancer parts detected. The percentage of error for this approach is calculated using Equation 2: % 100 (2) Where T: Total number of tested images. Figure 8 illustrates the accuracy yet the advantages gained by this approach in detecting the tumor region in the human breast Mammogram images. http://www.iaeme.com/ijciet/index.asp 101 editor@iaeme.com

Extract the Breast Cancer in Mammogram Images Figure 8: Detection of Cancer Region in the mammogram image. 3.2.6. Features Extraction for the Region of Interest (ROI) After detecting and extract the ROI the features are calculated which includes: Mean: One of the statistical features that studied for the Region of Interest (Cancer region) is the Mean. This feature calculated using Equation 3. http://www.iaeme.com/ijciet/index.asp 102 editor@iaeme.com

, 1,,! (3) Where g(x, y) is the filtered image, f(x, y) is the original image before being filtered, S is the set of coordinating pixels in the neighborhood of pixel(x, y) including the pixel(x, y) itself, and N is the total number of pixels in the neighborhood [12]. Variance: The second statistical features that extracted from the region of interest (Cancer region) are the Variance. This feature is calculating using Equation 4. " # % '# (4) The variance (σ 2 ), is defined as the sum of the squared distances of each term in the distribution from the mean (μ), divided by the number of terms in the distribution (N). Standard Deviation: An important statistical feature derived from the Region of Interest (Cancer region) is the Standard Deviation. This feature is calculating using Equation 5. 3/0./0 1 ()*+ -, #, 12 12 (5) The Histogram: The other feature extracted from the ROI is the Histogram. Then calculate the average of the four highest values for the frequencies in the histogram of the ROI. The histogram for the cancer region(roi) of the mammogram breast image is shown in Figure 9. Figure 9: Histogram for the cancer region. Figure 10 explained the role of the suggested method to isolate the ROI (Cancer region) in the mammogram breast image with the features extracted for this region. http://www.iaeme.com/ijciet/index.asp 103 editor@iaeme.com

Extract the Breast Cancer in Mammogram Images Figure 10: The Cancer Region with its Features. 5. CONCLUSION AND FUTURE WORK This approach introduced a new method to discover the breast cancer region from the mammogram images based on image processing techniques, which include enhancement, morphological operations, segmentation, feature extraction and isolation of the cancer region. Isolate the cancer region is based on the segment the image into several parts. The greatest intensity part represented the breast cancer region. Further work is to expand this technique in order to provide more accurate cancer classification by considering more breast cancer types. ACKNOWLEDGMENT The authors would like to thank Mustansiriyah University (www.uomustansiriyah.edu.iq), Baghdad-Iraq for its support in the present work. REFERENCES [1] Cabrera R., Guzman-Sepúlveda J. R., Torres-Cisneros D. A., May-Arrioja,J. Ruiz- Pinales,, "Digital Image Processing Technique for Breast Cancer Detection," International Journal of Thermophysics, vol. 34, no. 8, p. 1519, 2013. [2] kral P., lenc l., "LBP features for breast cancer detection," IEEE International Conference on Image Processing (ICIP), pp. 2643-2647, 2016. [3] Leonardo d. O., Braz G. Junior, Correa S. A., Cardoso de Paiva A., Marcelo G., "Detection of masses in digital mammogram using K-means and support vector machine," Electronic Letters on Computer Vision and Image Analysis, vol. 8, no. 2, pp. 39-50, 2009. [4] Sundaram K. M., Sasikala D., and Rani P. A., "A study on preprocessing a mammogram image using Adaptive Median Filter,," International Journal of Innovative Research in Science, Engineering, and Technology, vol. 3, no. 3, pp. 10333-10337, March 2014. [5] Vasantha M., Bharathi V.S., "Classification of Mammogram images using the hybrid feature,," European Journal of Scientific Research ISSN 1450-216, vol. 57, no. 1, pp. 87-96, 2011. [6] N. J., "Automated breast profile segmentation for ROI detection using digital Mammograms,," in IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur, 2010. [7] Shan J., Yanhui W. Ju, Guoa, Zhang L., and Cheng H. D., "Automated breast cancer detection and classification using ultrasound images: A the survey,," Pattern Recognition, vol. 43, pp. 299-317, 2010. http://www.iaeme.com/ijciet/index.asp 104 editor@iaeme.com

[8] Thangavel K. and Roselin T., "Mammogram mining with genetic Optimization of Anti- Miner parameters,," International Journal Of Recent Trends In Engineering, vol. 2, no. 3, 2009. [9] Maitra I. K., Nag S., and Bandyopadhyay S.K., "Automated digital mammogram segmentation for detection of abnormal masses using binary homogeneity enhancement algorithm, Indian Journal of Computer Science and Engineering, ISSN: 0976-5166, vol. 2, no. 3, 2011. [10] Banik S., Rangayyan M. and Boag G., Landmarking and Segmentation of 3D CT Images, Handbook, Morgan & Claypool publishers, 2009. [11] Hussain S. and Al-Khalidi F.,"EYES DETECTION IN THE HUMAN FACE", International Journal of Civil Engineering and Technology (IJCIET), Vol. 9, Issue 10,, pp. 1001 1007, Article ID: IJCIET_09_10_101, October 2018 [12] Gonzales R., Woods R., and Eddins L., Digital image processing using MATLAB, the United States of America: Handbook Pearson Education, 2004. [13] Al-Khalidi F., Bayati M., Alkinany S., "Tumor detection and extraction in the human brain," Journal of Engineering and Applied Sciences, final accepted in 6th of july 2018. http://www.iaeme.com/ijciet/index.asp 105 editor@iaeme.com