Computer Aided Detection of Mammographic Lesions (original) (raw)

Computer-Aided Breast Cancer Detection Using Mammograms: A Review

IEEE Reviews in Biomedical Engineering, 2013

The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.

An Automated Computer Aided Breast Cancer Detection System}

… International Journal on Graphics, Vision and …

Each year, 182,000 women are diagnosed with breast cancer and 43,300 die. One woman in eight either has or will develop breast cancer in her lifetime. If detected early, the five-year survival rate exceeds 95%. Mammography can detect very early breast tumors, when they are too small to be felt. In fact, most of the breast cancers detected by screening are at this very early stage, when they are relatively easy to cure. The responsibility of the physician in breast cancer screening involves the interpretation of images of the breast for the identification of potential abnormalities, and their categorization with respect to growth. Such images include mammograms, contrast-enhanced magnetic resonance images (MRI) and ultrasound images. We present a Computer Aided Detection system to suit the need of a of Computer Aided Detection (CAD) in cancer screening for civilizing image excellence and detecting abnormalities in a diversity of modalities associated with cancer screening that is, mammography in breast cancer. The objective is the development of an online automated process expert of cataloging suspicious regions, supplementing and humanizing the physician's ability to spot abnormalities.

Breast tumor diagnosis in digital mammograms

2019

Breast cancer has been classified as the most common cancer in most part of the world [1]. Breast cancer is caused by the growth of the abnormal cells in the breast. Breast cancer not only develops in women but also on men. However, the incidents of breast cancer in women are more common than men. Breast cancer is dangerous and may take away one’s life if there is no early detection and treatment are not done to remove the cancer cell present in the breast. Although the prevention methods for breast cancer may be unclear, it is found out that the earlier the detection and treatment conducted to the patients, the higher the survivability of the patients. Digital mammography is a specific type of breast imaging that uses low-dose x-rays to detect cancer early especially before women experience any symptoms [2]. The early signs of breast cancer can be detected in mammograms. Hence, digital mammograms have been classified as one of the best methods to detect breast cancer. In the studie...

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.

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.