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Analysis of Breast Cancer Using Image Processing Techniques Using MATLAB
Introduction
Breast cancer accounts for the second most cancer diagnosed among women and the second most cancer deaths in the world. In fact, more than 11000 women die each year, all over the world, because of this disease. Some researches has been oriented to make automatic the diagnosis at the step of mammographic diagnosis, some others treated the problem at the step of cytological diagnosis .Medical automatic diagnosis is still considered as a hard task. In fact, medical diagnosis requires an expert able to cope with the uncertain cases only by eyeing the visible symptoms. Such performances are difficult to achieve using an automatic system for diagnosis. Breast cancer is a vital problem needing quick handling and treatments. Clinicians wish to avoid using these further treatments unless the risk of recurrence is high, as the side-effects may be unpleasant or dangerous. Cost is another consideration as some treatments are very expensive. The risk of recurrence must be estimated using information available at the time of initial treatment. There is no great agreement among clinicians as to the best way of integrating all the different data. Since prediction is not always easy new variables are suggested frequently. If all possible information were to be collected for every patient the cost would be prohibitive. The primary task under investigation in this paper is to develop techniques which will extract the maximum benefit from a minimal subset of such information and provide a quantitative method for use by clinicians.
The task is essentially one of using data on prognostic factors (such as age, size of tumor/ cancer of breast) at the time of tumor/cancer removal to predict the risk of recurrence. Prognostic factors are of two types: those giving information about the patient such as age and those giving information about the tumour/ cancer and its growth rate such as size or number of affected auxiliary lymph nodes.
Signs of cancer in a mammogram
There are several signs of cancer to look for in a mammogram. The primary signs are local distortion of glandular tissue and existents of malignant microcalcifications. These signs may appear alone or together. Skin thickening, skin distortion, retraction of the nipple is considered as secondary signs. The attenuation of tumor may vary from depending upon type of tumor. Benign tumors are usually rounded and have distinct border. While malignant tumors tend to be more irregularly in shape, often speculated and have diffused borders. Normally two breast of one woman are very alike , and cancer of both breast is relatively rare .These facts help radiologist in the search of cancer in a mammogram. Microcalcification are microscopic grains of calcium produced be the cells as the result of some benign or malignant process. Most calcification is the result of some benign process.The may for instance be the rest of broken down cells,a cyst or milk. Benign tumors & malignant calcification differs in shape density and distribution
However, it may be found that the predictions possible from the available information are limited because patients with very similar values on all the factors have greatly varying times to relapse. While this problem can be identified by analysis it cannot be addressed by it—the reason for it may be that the risk of relapse depends on some unmeasured factor or is simply subject to great random variation. From a statistical viewpoint, an important feature of the data is the presence of censoring, so that some of the times to relapse are not known exactly, only that they are greater than a certain value. This occurs either because follow-up is no longer available on the patient for some reason (maybe they have died of an unrelated cause), or simply because no relapse has yet occurred, when the survival time is known only to be as long as the total time elapsed since surgery. We assume that censoring times are essentially non-informative, so that for example censoring does not routinely occur just before a relapse.
Definition of medial terms
• Mammograms: An X ray of the breast
• Radiologist:A medically skilled person who reviews the mammograms
• Malign: Cancerous, Dangerous.
• Benign: Non Cancerous- Does not spread to other part of body.Palpable.Something that can be touched.
• Atteneuation:A measure of a medium ability to absorb radiation.
MATLAB & Image Processing
Imaging, mainly due to its impact on medicine and biology, has been selected as one of the greatest achievements of the twentieth century by the National Academy of Engineering. In the last several decades, medical imaging systems have advanced in quantum leaps. There have been substantial improvements in characteristics such as sensitivity, resolution, and acquisition speed. Multislice, 64-Slice, and very soon, 256-Slice computer tomography (CT) scanners, for instance, allow the visualization of the entire coronary tree, even atherosclerotic plaques within the coronaries with extremely high accuracy and detail. Similar advances have occurred in other medical imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET).
Substantial effort has been put into the integration of different modalities. These systems are also called hybrid systems. The integration of CT and PET scanners has enabled physicians to localize biochemical activity (functional) with a high degree of certainty in the human body and will significantly impact molecular imaging, which can be defined as in vivo imaging of biochemical or molecular activity in the organ. There is also significant development in small animal imaging modalities. In vivo and in vitro molecular imaging has already been contributing to the advancement of the study of the genome and efficacy of new drugs. With the help of imaging, now biologists can get a snapshot of almost the entire range of genomic activity (expression or disexpression of genes) within a diseased tissue in a matter of days. It will not be long before physicians can visualize, in vivo, the biochemical processes triggered by a disease. All of this may soon result in a paradigm shift in healthcare. It may open up the possibility of designing drugs as per a patient’s individual genetic profile.
Advanced techniques of image processing and analysis find widespread use in biology and medicine. In medical and biological fields, image data are ubiquitously used in clinical as well as scientific studies to infer details regarding the process under investigation whether it be a disease process or a biological or biochemical process. Today, perhaps, health care institutions alone produce the largest amount of image data, which are used in diagnosis and treatment of patients. Information provided by medical images has become an indispensable part of today’s patient care. As the number of images produced increases, utilization and handling of image data are becoming an increasingly formidable task for engineers, scientists, and medical physicists.
There are two main issues that concern the field of image processing and analysis applied to medical applications. These are the following:
Improving the quality of the acquired image data
Extraction of information (i.e., feature) from medical image data in a robust, efficient, and accurate manner
Image enhancement techniques such as noise filtering, contrast and edge enhancement; and image restoration techniques that focus on removing degradations in images, all fall within the former category, whereas image analysis methods deal primarily with the latter issue.
The sheer size of images in medical applications has been increasing rapidly with the advent of imaging technologies; hence, transfer and storage issues are also challenging tasks. The main goal of developing efficient image data compression techniques is to address these two issues
Unlike the images produced in industrial applications, the images generated in medical and biological applications are complex and vary substantially from application to application. In addition, as one can imagine, the field of image processing and analysis has to tackle a diverse and complex set of problems. Because this is such a vast subject, we focus on certain topics that we consider important in the fields of medicine and biology.
Some concepts in image processing and analysis are theory-intensive and may be difficult for beginners to grasp. Explaining complex topics in image processing through examples and MATLAB algorithms is the principal aim of this book. While working on this book, we tried to strike a balance between theory and practice. We wanted to keep it neither shallow nor complex, so that readers from diverse fields would comprehend without difficulty. Image processing techniques in general are ad-hoc in the sense that they are optimized and tailored to solve a particular problem in hand, although they are based on solid mathematical theories. That is, they are not applicable to a wide range of applications or situations.