Volume 27, Issue 4 (6-2020)
RJMS 2020, 27(4): 60-73 |
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alizadeh N, Pourasad Y. Detection and classification of breast masses using mammographically image processing. RJMS 2020; 27 (4) :60-73
URL: http://rjms.iums.ac.ir/article-1-6113-en.html
URL: http://rjms.iums.ac.ir/article-1-6113-en.html
Urmiya University of Technoology, Urmia, Iran , y.pourasad@uut.ac.ir
Abstract: (4468 Views)
Background: Breast cancer is the most common type of cancer and the second leading cause of cancer death among American women. In Iran, the rate of breast cancer is lower than in industrialized and western countries, but with the growing trend, it is predicted that breast cancer will become one of the most common cancers in the country in the future. Mammography is currently one of the most effective and popular methods for screening and diagnosing breast cancer. Breast cancer is the primary and most common disease found in women which causes second highest rate of death after lung cancer. The digital mammogram is the X-ray of breast captured for the analysis, interpretation, and diagnosis. Automatic detection of breast cancer in mammograms is a challenging task in Computer Aided Diagnosis (CAD) techniques. This article aims to provide an automated computer diagnostic system to help diagnose early breast cancer. First, breast cancer and the survival statistics of patients with it, breast imaging techniques, and the presence of symptoms that are present in the images are signs of the disease. In the following, by introducing important and efficient methods in designing automatic diagnostic systems and its structure in order to distinguish cancerous images from non-cancerous breasts, the results obtained from this research and validations have been presented.
Methods: Breast cancer, one of the most common cancers in women, has a high mortality rate. Providing a medical assistance system for early detection of abnormalities associated with this cancer will greatly assist pathologists in identifying the causes of the disease and increase performance and accuracy in diagnosis. Studying the background of research in this field to better understand the problem and how to design this system in different ways gives us a more accurate view of this issue and also defines the design challenges of such a system. The results obtained by the methods presented in this paper are abbreviated as BMD_ML. A total of 64 mammograms, 23 cancer images containing benign and malignant masses, and 41 non-cancerous images were used to evaluate the methods used in this paper, and the results were obtained from the inputs of these images. A combination of digital image processing methods, random statistics and machine learning methods is used to perform the pre-processing, segmentation and extraction of ROI, feature extraction and classification at the lowest error rate. CAD is used in mammography screening. Mammography screening is used to detect early breast cancer. The CAD system helps diagnose lesions and classify benign and malignant tumors. This system is mostly used in the United States and the Netherlands. The first CAD system for mammography was developed during a research project at the University of Chicago. CAD systems, despite their high sensitivity, have very few features; This makes the benefits of using CAD unclear. In this report we present a methodology for breast cancer detection in digital mammograms. Proposed methodology consists of three major steps like segmentation of breast region, removal of pectoral muscle and classification of breast muscle into cancerous and normal tissues.
Results: This article aims to provide an automated computer diagnostic system to help diagnose early breast cancer. First, breast cancer and the survival statistics of patients with it, breast imaging techniques, and the presence of symptoms that are present in the images are signs of the disease. Then, important and efficient methods in designing automatic diagnostic systems and its structure are introduced and the work done in the past is examined by researchers active in this field. Finally, the techniques used in this paper are presented in order to distinguish between cancerous and non-cancerous breast cancer images. Segmentation of breast muscle was performed by employing Otsus segmentation technique, afterwards removal of pectoral muscle is carried out by seed selection and region growing technique. In next step, Gray Level Co-occurrence Matrices (GLCM) was created form which several features were extracted. At the end, several classifiers were trained to classify breast region into normal and cancerous tissues. The proposed classifier reports classification accuracy of 100 % for ANN and 96.3 % for decision tree algorithms (C5.0 and CHAID). Proposed methodology was validated on Mini-MIAS database and results were compared with previously proposed techniques, which shows that proposed technique can be reliably apply for breast cancer detection. Classification includes the final stage of designing such a system. Machine learning techniques have good performance for classifying tissue features obtained from mammograms. Machine learning is generally divided into two categories, supervised and uncontrolled. Learning without supervision requires a large amount of data to train the network. The classification methods used in this dissertation are one of the supervised methods, so that when creating a feature vector matrix, a column is assigned to whether the data is cancerous or non-cancerous. This method both speeds up learning and compares classified data with the predetermined target value when testing them. In this paper, several machine learning methods will be used to classify methods for classifying and comparing diagnostic accuracy. In this paper, classifications are performed in SPSS modeler software.
Conclusion: Numerous methods for extracting features were provided in the Overview of Features Extraction section. The solution presented in this article is to use a GLCM matrix. The matrix of gray surfaces always gives rise to different combinations of the brightness of the pixels in the image. CAD systems, despite their high sensitivity, have very few features; This makes the benefits of using CAD unclear. It can concluded that CAD could not have a significant effect on cancer detection rates, but it would inadvertently increase the recall rate (ie, the FP rate); However, various studies have shown significant inconsistencies in recall effects. In the design of the CAD system, the partitioning and extraction of the feature are of special importance. It should be noted to what extent the extraction characteristics describe the segmented area. In the future, in order to increase the accuracy of classifications and commercialization of this system, flexible features can be extracted from the image, which in addition to the lack of overlap between the features can be very compatible with machine learning methods. It is also possible to classify between types of anomalies, and after diagnosing whether the image is cancerous or non-cancerous, the type of anomaly associated with it can be identified.
Methods: Breast cancer, one of the most common cancers in women, has a high mortality rate. Providing a medical assistance system for early detection of abnormalities associated with this cancer will greatly assist pathologists in identifying the causes of the disease and increase performance and accuracy in diagnosis. Studying the background of research in this field to better understand the problem and how to design this system in different ways gives us a more accurate view of this issue and also defines the design challenges of such a system. The results obtained by the methods presented in this paper are abbreviated as BMD_ML. A total of 64 mammograms, 23 cancer images containing benign and malignant masses, and 41 non-cancerous images were used to evaluate the methods used in this paper, and the results were obtained from the inputs of these images. A combination of digital image processing methods, random statistics and machine learning methods is used to perform the pre-processing, segmentation and extraction of ROI, feature extraction and classification at the lowest error rate. CAD is used in mammography screening. Mammography screening is used to detect early breast cancer. The CAD system helps diagnose lesions and classify benign and malignant tumors. This system is mostly used in the United States and the Netherlands. The first CAD system for mammography was developed during a research project at the University of Chicago. CAD systems, despite their high sensitivity, have very few features; This makes the benefits of using CAD unclear. In this report we present a methodology for breast cancer detection in digital mammograms. Proposed methodology consists of three major steps like segmentation of breast region, removal of pectoral muscle and classification of breast muscle into cancerous and normal tissues.
Results: This article aims to provide an automated computer diagnostic system to help diagnose early breast cancer. First, breast cancer and the survival statistics of patients with it, breast imaging techniques, and the presence of symptoms that are present in the images are signs of the disease. Then, important and efficient methods in designing automatic diagnostic systems and its structure are introduced and the work done in the past is examined by researchers active in this field. Finally, the techniques used in this paper are presented in order to distinguish between cancerous and non-cancerous breast cancer images. Segmentation of breast muscle was performed by employing Otsus segmentation technique, afterwards removal of pectoral muscle is carried out by seed selection and region growing technique. In next step, Gray Level Co-occurrence Matrices (GLCM) was created form which several features were extracted. At the end, several classifiers were trained to classify breast region into normal and cancerous tissues. The proposed classifier reports classification accuracy of 100 % for ANN and 96.3 % for decision tree algorithms (C5.0 and CHAID). Proposed methodology was validated on Mini-MIAS database and results were compared with previously proposed techniques, which shows that proposed technique can be reliably apply for breast cancer detection. Classification includes the final stage of designing such a system. Machine learning techniques have good performance for classifying tissue features obtained from mammograms. Machine learning is generally divided into two categories, supervised and uncontrolled. Learning without supervision requires a large amount of data to train the network. The classification methods used in this dissertation are one of the supervised methods, so that when creating a feature vector matrix, a column is assigned to whether the data is cancerous or non-cancerous. This method both speeds up learning and compares classified data with the predetermined target value when testing them. In this paper, several machine learning methods will be used to classify methods for classifying and comparing diagnostic accuracy. In this paper, classifications are performed in SPSS modeler software.
Conclusion: Numerous methods for extracting features were provided in the Overview of Features Extraction section. The solution presented in this article is to use a GLCM matrix. The matrix of gray surfaces always gives rise to different combinations of the brightness of the pixels in the image. CAD systems, despite their high sensitivity, have very few features; This makes the benefits of using CAD unclear. It can concluded that CAD could not have a significant effect on cancer detection rates, but it would inadvertently increase the recall rate (ie, the FP rate); However, various studies have shown significant inconsistencies in recall effects. In the design of the CAD system, the partitioning and extraction of the feature are of special importance. It should be noted to what extent the extraction characteristics describe the segmented area. In the future, in order to increase the accuracy of classifications and commercialization of this system, flexible features can be extracted from the image, which in addition to the lack of overlap between the features can be very compatible with machine learning methods. It is also possible to classify between types of anomalies, and after diagnosing whether the image is cancerous or non-cancerous, the type of anomaly associated with it can be identified.
Keywords: CAD system, Breast cancer, Segmentation, Feature extraction, ANN, Decision tree classifiers
Type of Study: Research |
Subject:
Internal Medicine
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