Image Segmentation Of Detection Of Lump Using Algorithm

“Cancer is the uncontrolled growth of abnormal cells in the body. Cancer develops when the body’s normal control mechanism stops working. Old cells do not die and instead grow out of control, forming new, abnormal cells. These extra cells may form a mass of tissue, called a tumor. Some cancers, such as leukemia, do not form tumor”.(‘What is cancer?”) A tumor is the swelling of a part of the body, generally without inflammation, caused by an abnormal growth of tissue, whether benign or malignant. Benign, if the cells are benign they are not cancerous. It won't invade nearby tissues or spread to other areas of the body. Also, some types of benign tumors such as intestinal polyps are considered precancerous and are removed to prevent them becoming

It is very important in detection, diagnosis, treatment planning and follow-up of many diseases. For instance, the images can reveal the limits of a tumour of a cancer patient, allowing more precise surgery and radiation therapy. There are no known biological hazards of MRI because it uses radiation in the radiofrequency range unlike x ray and computed tomography. The radio waves being used are similar to waves used for radios and cell phones which are found all around us. Due to its long wavelengths, it does not damage tissue as it passes through. The use of harmless radiation provide a safe, effect and accurate imaging of any parts of the

These days tumours are an exceptionally disputed issue, a lot of individuals have diseases involving tumours. Here I want to talk about the different sorts of medicines used to decrease growth and which is the best medicine among them. First and foremost I want to present some fundamental information about cancer. A tumour is an ailment of the cells, which are the body's essential building pieces, cancer happens when unusual cells develop in an uncontrolled way (MedicineNet.com 2014). These unusual cells can harm or attack the encompassing tissues, or spread to different parts of the body, bringing on additional harm. The growth of these cells, in some cases, starts in one piece of the body before spreading to different areas. This

Most cancers form tumors, but not all tumors are cancerous. Some tumors are benign or noncancerous as those like freckles and moles which stop the growing process and do not spread to other parts of the body and therefor do not create new tumors. Malignant or cancerous on the other hand interfere with body functions and draw nutrients from the body tissues due to the tumors they create. Cancers continue to grow and spread by direct extension or through a process named metastasis, whereby the malignant cells travel through the lymphatic or blood vessels eventually forming new tumors in other parts of the body.

Cancer is a non-exclusive term for an expansive gathering of maladies that can influence any part of the body. Different terms utilized are threatening tumors and neoplasms. One characterizing highlight of disease is the fast formation of irregular cells that develop past their typical limits, and which can then attack abutting parts of the body and spread to different organs, the last procedure is alluded to as metastasizing. Metastases are the significant reason for death from

Another technique adopted by neuroscientists is the Magnetic Resonance Imaging (MRI). MRI unlike PET measures the brain structure rather than the activity. It is measured by the waves emitted from hydrogen atoms which are activated by the radio waves from the magnetic field that is passed over the head during the procedure (Pinel, 2011). This is detected by the scanner and is then altered into a structural image. MRI is a much more detailed scan compared to for example, CT scans, as they provide clearer images of the brain which makes it easier for neuroscientists to identify any abnormalities in brain structure. Furthermore, the technique has relatively high spatial resolution and it can produce images in three dimensions which can give a more advanced view into the brain (Martin, 2005). Moreover, MRI has led to the discovery of a number of brain abnormalities in schizophrenia. For example, an MRI study found that compared to healthy subjects, schizophrenic patients showed smaller volumes of grey matter in the temporal lobe and smaller volumes of white matter in the frontal lobe. Schizophrenic patients also had a higher volume of

This article is about MRI scan and how tremendously MRI’s can be used to identify illness throughout the body. Furthermore, the article talks about how the procedure of the MRI is used in hospitals on patients to know the seriousness of a certain diseases or the extent of damage caused by the disease. MRI images are used when accurate detail and precise image about a tumor's place are required. Dr. Simon Walker-Samuel one of the researchers involved in MRI study stated “We have developed a new state-of-the-art imaging technique of visualize and map the location of tumors that will help us assess the effectiveness of new cancer therapies. (Kalapurayil, 2013). Before undertaking the MRI scan, the patients are usually given an injection of glucose (sugar), this is done because the MRI device is alerted to find glucose. During the process of MRI exam, which takes between 3 to 10 minutes, the patient sits still until the number of MRI images and information are collected. One important fact about MRI scan is that the patient cannot take MRI under any circumstance if they have a heart pacemaker or pacing wires, cerebral aneurysm. The reason is because MRI involves magnets and anything that has a magnet in it such as pacemakers will interfere with the device and possibly kill the patient.

The Magnetic Resonance Imaging (MRI) scan is a structural scan, which means that it can show the structure of the brain but not the function of it. It is mostly used to look at the tissues of the brain to detect any damage done from accidents, brain tumors or any other abnormality that could affect the structure of the brain. Advantages of the MRI scan include the excellent quality and resolution of the images

Digital imaging technologies have become indispensable components for clinical procedures. Major advances in the field of medical imaging and computer technology have created opportunity for quantitative analyses of medical images and provided powerful techniques to probe the structure, pathology and function of the human body . In medical applications, skilled operators usually extract the desired regions that may be anatomically separate but statistically indistinguishable. It is subjected to manual errors and biases, which is time consuming, and has poor reproducibility. The problem faced in clustering is the identification of clusters in given data. A widely used method for clustering is based on K-means in which the data is partitioned into K number of clusters. In this method, clusters are predefined which is highly dependent on the initial identification of elements representing the clusters well. Several researchers in clustering has focused on improving the clustering process such that the clusters are not dependent on the initial identification of cluster representation.

Cancer is not a single disease but a class of diseases which are typically characterized by random and out-of-control growth in the human body cells. These random cell growth leads to invasion of other normal cells around them leading to their destruction. The cancerous cells divide in an uncontrollable fashion and form lumps or tissue masses known as tumors. These tumors affect the body part where they grow and disrupt their normal functioning. The cancerous cells also spread to other parts of the body through blood or lymph and cause further cell destruction.

Features are essential for any classification or analysis in image processing. There are many types of features which can be extracted from the images each gives its identical informations about the image. Here MA region supposed with properties such as shape, color and size which appears as a dark red colored circle shape. To identify MA and non- MA region feature vectors are formed for each candidate regions.

In this article an in-depth overview is presented on Computer aided diagnostic (CAD) system’s usage for liver cancer. Besides, in a broader sense highlighting the technical aspects developed for medical ultrasound images is also discussed. CAD system is a process that provides adequate information that helps to analyze the Ultrasound images and helps to accurately detecting different types of liver cancer. However, the system performance is still not significantly improved. In this paper, firstly, we categorize the CAD system according to the four primary stages including data preprocessing, lesion segmentation, feature extraction, selection, and Classifier. In each stage, we review specific methods that are commonly used in most of the algorithms proposed for computerized tissue characterization and discuss their advantages and drawbacks. Then, recent proposed algorithms is presented in summarize form that have shown clinical value or specific possibility to the computerized analysis of setback for ultrasound liver images. These techniques or their combinations are the ones that are mostly used in the past few decades by the majority of work published in the Computer aided diagnosis domain.

Abstract— Photographing the changes in internal breast structure due to formation of masses and microcalcification for detection of Breast Cancer is known as Mammogram, which are low dose x-ray images. These images play a very significant role in early detection of breast cancer. Usually in pattern recognition texture analysis is used for classification based on content of image or in image segmentation based on variation of intensities of gray scale levels or colours. Similarly texture analysis can also be used to identify masses and microcalcification in mammograms. However Grey Level Co-occurrence Matrices (GLCM) technique introduced by Haralick was initially used in study of remote sensing images. Up till now in breast cancer detection only first and second order GLCM features were mostly used, to the best of our knowledge there is no evidence of use of higher order GLCM features for detection of malignant masses in breast tissue images. In this paper we attempted up to 7th order and observed the results by analyzing the effects of higher order features in recognition of malignancy in

Lesion segmentation methods can be applied to different regions of the body. Lipková et al. (2017), used an unsupervised segmentation algorithm to find liver lesions in computed tomography (CT) scans. Researchers in this study agree that manual and semi-automated methods are impractical “with an intra- and interobserver variability up to 11 % in volume difference on liver CT scans” (p. 1). Furthermore, they oppose supervised methods because “a significant variation in lesions shape and structure compromise efficiency of supervised methods” (p. 2). In this experiment they use a phase separation mechanism called the Cahn-Hilliard separation (CHS) method. The Cahn-Hilliard equation removes noise from the image and separates healthy liver tissue from damaged liver tissue, and it can operate three-dimensional imaging data. They found that this method works effectively for small lesions, although it cannot effectively separate lesions from other areas in the image that have similar visual characteristics. For this reason, CHS only worked better than artificial neural networks (i.e. an assisted method) when the images lacked artifacts. Finally, CT scans generally produce less detailed images, and therefore MRI scans might change their results.

Segmenting or dividing a digital image into region of interests or meaningful structures in general plays a momentous role in quite a few image processing tasks. Image analysis, image visualization, object representation are some of them. The prime objective of segmenting a digital image is to change its representation so that it looks more expressive for image analysis. During the course of action in image segmentation, each and every pixel of the image segmentation is assigned a label or value. The pixels that share the same value also share homogeneous traits. The examples can include color, texture, intensity or some other features. Image segmentation can be defined as the technique to divide the an image f (x, y) into a non empty subset f1, f2, ...., fn which is continuous and disconnected. This step contributes in feature extraction. There are quite a few applications where image segmentation plays a pivotal role. These applications vary from image filtering, face recognition, medical imaging