A Research on Breast Cancer Detection using Mammography (original) (raw)

Digital Image Processing Technique for Breast Cancer Detection

International Journal of Thermophysics, 2013

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women's quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. As masses and benign glandular tissue typically appear with low contrast and often very blurred, several computer-aided diagnosis schemes have been developed to support radiologists and internists in their diagnosis. In this article, an approach is proposed to effectively analyze digital mammograms based on texture segmentation for the detection of early stage tumors. The proposed algorithm was tested over several images taken from the digital database for screening mammography for cancer research and diagnosis, and it was found to be absolutely suitable to distinguish masses and microcalcifications from the background tissue using morphological operators and then extract them through machine learning techniques and a clustering algorithm for intensity-based segmentation.

Comparative analysis of different techniques for breast cancer detection in Mammograms

We have studied the various methods for breast cancer detection and have found that there are several advantages and disadvantages of existing methods. As concluded from the above literature survey inaccuracy in the size of cancer and larger computational time to detect cancer are two major limitations of existing methods. We can work on these methods to improve detection accuracy and computational time.

Surveyon Different Techniques UsedFor Detection of Malignancy in Mammograms of Breast Cancer

—Breast cancer detection is still complex and challenging problem. Di agnosis of cancer tissues in mammograms is a time consuming task even for highly skilled radiol ogists as it contains low signal to noise ratio and a complicated structured background. Therefore, in digital mammogram there is still a need to enhance imaging, where enhancement in medical imaging is the use of computers to make i mage clearer. Studies show that relying on pure naked-eye observati on of experts to detect such diseases can be prohi biti vel y slow and inaccurate in some cases. Provi di ng automatic, fast, and accurate i mage-processing-and arti ficial Intelligence-based solutions for that task can be of great realistic significance. This paper discusses about different techni ques used to scans the whole mammogram and performs filtering, segmentation, features extracti on.

Breast cancer detection: A review on mammograms analysis techniques

10th International Multi-Conferences on Systems, Signals & Devices 2013 (SSD13), 2013

Breast cancer is the most common cancer among women over 40 years. Studies have shown that early detection and appropriate treatment of breast cancer significantly increase the chances of survival. They have also shown that early detection of small lesions boosts prognosis and leads to a significant reduction in mortality. Mammography is in this case the best diagnostic technique for screening. However, the interpretation of mammograms is not easy because of small differences in densities of different tissues within the image. This is especially true for dense breasts. This paper is a survey of the automatic early detection of breast cancer by analyzing mammographic images. This analysis could provide radiologists a better understanding of stereotypes and provides, if it is detected at an early stage, a better prognosis inducing a significant decrease in mortality.

A Review on Computer Aided Detection System for Mammographic Images

Digital image processing is important in numerous areas of research and development which employed to process digital images and generate useful characteristics from the data, which can then be used to take critical decisions with high accuracy. These techniques are also applied for applications in the medical field, specifically in the detection of Breast Cancer. Over the years due to limitations of human observations, computers have played a significant role in detecting early signs of cancer, this conclude a development of Computer Aided Detection System (CAD) of high accuracy. This paper presents a concise review on Computer Aided Detection System which can be developed as a useful decision support system for automatic detection of breast cancer, whereby the predictions from the CAD systems can be employed to enhance diagnosis from radiologists.

Digital Imaging in Mammography towards Detection and Analysis of Human Breast Cancer

International Journal of Computer Applications, 2010

Mammography is at present most popular and available method for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. The challenge is to quickly and accurately overcome the development of breast cancer, which affects more and more women through the world. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. Mammogram is one of the best technologies currently being used for diagnosing breast cancer. Breast cancer is diagnosed at advanced stages with the help of the mammogram image. In this paper, some simple segmentation processes have been develop to make a supporting tool to easy and less time consuming method of identification abnormal masses in mammography images. The identification technique is divided into four distinct parts i.e. preprocessing, selection, isolation and projection. The type of masses, orientation of masses, shape and distribution of masses, size of masses, position of masses, density of masses, symmetry between two pair etc are clearly sited after proposed method is executed on raw mammogram for easy and early detection of abnormality. The outcomes of the results are satisfactory and acceptable.

A Comparative Study on the Methods Used for the Detection of Breast Cancer

—Among women in the world, the death caused by the Breast cancer has become the leading role. At an initial stage, the tumor in the breast is hard to detect. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan which adopts X-ray method with high resolution film. so that it can detect well the tumors in the breast. This paper describes the comparative study of the various data mining methods on the detection of the breast cancer by using image processing techniques.

An Approach for Breast Cancer Mass Detection in Mammograms

Breast cancer is one of the major causes of death among women all over the world. An improvement of early detection and diagnosis techniques is very important for women's quality of life. Computer-Aided Detection (CAD) systems have been used for aiding radiologists in their decision in order to solve the limitations of human observers. This paper presents a methodology for mass detection in digital mammograms. This methodology begins with segmenting Regions of Interest (ROIs) using morphological operations and automatic thresholding. Features are extracted from the ROIs and Principal Component Analysis (PCA) is applied for reducing the features dimensionality. Finally, the methodology performs classification through Neural Networks (NNs). The proposed system was tested on several mammographic images extracted from DDSM database. Results showed that the proposed methodology provided more accuracy than other compared techniques.

Breast cancer detection using image processing techniques

2000

Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it's infant state. The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.

PERFORMANCE ANALYSIS IN COMPUTER AIDED DETECTION OF BREAST CANCER BY MAMMOGRAPHY

Breast cancer is one of the frequent and leading causes of mortality among woman, especially in developed countries. Early detection and treatment of breast cancer are the most effective method for detecting breast cancer at the early stage. Computer-aided-detection (CAD) system can plays a vital-role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. This paper aims to provide an overview of recent advances in the development of CAD systems and related techniques. Primarily we begin with a detailed introduction of some basic concepts related to breast cancer detection, then focus on the key CAD techniques developed recently for breast cancer, including comparative analysis on detection of masses, calcification, architectural distortion, and bilateral asymmetry in mammograms.