Abstract - Image segmentation is the process of partition-ing a digital image into multiple segments or sets of pix-els. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The image segmentation methods may be classified into several types: image-based, model-based and hybrid methods. Purely image-based methods perform segmentation based only on information available in the image. These include thresholding, region growing, morphological operations, active contours level sets, watershed, fuzzy connectedness, and graph cuts (GCs) .Image based methods perform well on high-quality images. However, the results are not as good when the image quality is …show more content…
Further appl cations include radiotherapy planning [2] as well as cancer detection and staging [5]. Most previous work on auto-mated abdominal segmentation is based on statistical shape models or Semi automated segmentation involves the following two steps: 1) manually marking the region of interest; 2) automated calculation and segmentation of the marked ROI . probabilistic atlases. Usually, statistical models are learned on a training set and applied in combi-nation with post processing steps that are often specialized to a particular organ. CT images are often preferred by diagnosticians since they have high Signal-to-Noise ratio and good spatial resolution, thus providing accurate ana-tomical information about the visualized structures. These good image qualities, and the advances in the digital image processing techniques, motivate the great deal of research work aimed at the development of computerized methods for the automatic abdominal organ …show more content…
Let X = (X1 X2…Xn) denotes an image with N pixels to be partitioned into C clusters, where Xi represents multispectral (features) data. The algorithm is an iterative optimization that minimizes the cost function defined as follows: (1)
where Uij represents the membership of pixel Xi in the ith cluster, Vi is the ith cluster center, || || is a norm metric, and m is a constant. The parameter m controls the fuzziness of the resulting partition, and m=2 is used in this study. The cost function is minimized when pixels close to the centroid of their clusters are assigned high membership values, and low membership values are assigned to pixels with data far from the centroid. The membership function represents the probability that a pixel belongs to a specific
Lantada, Andres Diaz, Pilar Lafont Morgado, Julio Munoz-Garcia, Juan Manuel Munoz-Guijosa, Javier Echavarri Otero, Jose Luis Munoz Sanz, and Raquel Del Valle-Fernandez. “Development of Personalized Annuloplasty Rings: Combination of CT Images and CAD-CAM Tools.” Annals of Biomedical Engineering 38, 2009. 28 March 2011: 280-290.
The input of algorithm is Data points with n features and the number of clusters given by K. Initially K centroids are assigned randomly. The points in the dataset are assigned to a cluster based on Euclidean distance.
Recently, new advances have been made in PET technology. A pair of American scientists working in Switzerland came up with a combination PET/CT scanner, which effectively pairs the two techniques. This new combination will be very useful in cancer diagnosis. With the PET/CT, both anatomical and functional imaging can be done and reproduced on the same image. This will be helpful in pinpointing the location of tumors, and also for the early identification of tumors too small to be of concern in CT scanning.
The CT imaging system consists of a motorized table which moves the patient through a circular opening. While the patient is inside the opening, an X-ray source and a detector assembly within the system rotate around the patient. A single rotation typically takes a second or less. During rotation the X-ray source produces a narrow, fan-shaped beam of X-rays that passes through a section of the patient's body. Detectors in rows opposite the X-ray source register the X-rays that pass through the patient's body as a snapshot in the process of creating an image [3, 8].
Retinal vessel segmentation is important for the diagnosis of numerous eye diseases and plays an important role in automatic retinal disease screening systems. Automatic segmentation of retinal vessels and characterization of morphological attributes such as width, length, tortuosity, branching pattern and angle are utilized for the diagnosis of different cardiovascular and ophthalmologic diseases. Manual segmentation of retinal blood vessels is a long and tedious task which also requires training and skill. It is commonly accepted by the medical community that automatic quantification of retinal vessels is the first step in the development of a computer-assisted diagnostic system for ophthalmic disorders. A large number of algorithms for retinal vasculature segmentation have been proposed. The algorithms can be classified as pattern recognition techniques, matched filtering, vessel tracking, mathematical morphology, multiscale approaches, and model based approaches. The first paper on retinal blood vessel segmentation appeared in 1989 by Chaudhuri et al. [21]...
The problem of the twenty century is the problem of the color-line,- the relation of the
Mammography plays a vital role in early detection of breast cancers because it can show abnormalities in the breast up to two years before a patient or physician can feel them. The digital mammogram is analyzed with a combination of general image processing and computer vision algorithms combined with procedures which have been specially designed for the application. Mammography has provided reliable parameters for detecting breast tumor. Masses and calcifications are the most general breast deformities that may specify the occurrence of breast cancer. The other symptoms of breast cancer are architectural distortion and bilateral asymmetry, etc., Breast abnormalities are defined by an extensive range of features and may be easily overlooked or wrongly interpreted by radiologists while appraising large number of mammography images made available in screening programs. CAD and Computer Aided Diagnosis (CADX) tools are being designed to help the radiologists for providing an accurate diagnosis. CAD and CADX algorithms lend a hand in reducing the number of FPs and they help radiologists to make better decisions. This chapter gives a review of image processing algorithms that have been developed for detection of breast cancer.
...s in one direction to acquire a single image slice. For another slice to be imaged the x-ray tube would again rotate another 360 degrees but on opposite direction. However this has been changed over the years and has been implemented a Slip-Ring technology which replaced the old high tension cables. In this way the new CT scanner x-ray tube had the ability to rotate continuously around the patient and in the same time the table was moving through the gantry to acquire data in a form of a Spiral or Helical. Therefore the new generation of CT scanners is called Spiral or Helical CT. The advantages of the new scanners are that the volume data can be reconstructed in any other planes (sagittal, coronal and three dimensional images), also there is a short time scan therefore radiation to the patient is minimized and also the artefact caused by patient motion is reduced.
The proposed multimodel segmentation was tested with almost all combination of mass shapes and margins in CC and MLO views and the segmented abnormal region was verified with ground truth table images in which abnormality marked by radiologist in the DDSM database. Further feature extraction methods and classifier has to be developed for fully automated diagnosis CAD system. Further study has to be carried out to test the algorithm for the segmentation of micro calcifications.
The notation used herein is as follows. Let D, the data partition, be a training set ofclass-labeled tuples. Suppose the class label attribute has m distinct values defining m distinct classes, Ci (for i = , . . . , m). Let Ci,D be the set of tuples of class Ci in D. Let |D|and |Ci,D| denote the number of tuples in D and Ci,D, respectively.
Radiographers provide essential services to millions of people. They deal with patients of all types and ages, from the very young to the elderly, as well as patients with special needs, such as visual or hearing impairments. Diagnostic radiographers produce high-quality images of organs limbs and other body parts to allow a wide range of diseases to be diagnosed. According to (The College of Radiographers -Registered Charity No 272505) May 2008. As a diagnostic radiographer, I am not confined to work in the x-ray department. I will x-ray patients in the accident and emergency department, on the wards, in the intensive care unit and in the operating theatre when patients are too ill to visit the x-ray department. Diagnostic radiographers work as part of a team and may work alone, outside normal working hours to provide x-ray services 24 hours a day. Wide ranges of imaging methods are used. These include ultrasound, MRI and CT scanning. Ranges of dyes or contrast agents are sometimes used to show soft tissue organs that would not appear on standard x-ray examinations e.g. arteries, the bowel and kidneys. (Medical Physics page 159-188)
CTscans stands for “Computed Tomography”. It is a way of looking inside your body using a special camera. It is an advanced scanning x-ray and computer system that makes detailed pictures of horizontal cross-sections of the body, or the part of the body that is x-rayed. A CT scan is a diagnostic test that combines the use of x-ray with computer technology. A series of x-beams from many different angles are used to get these cross-sectional images of the patient’s body. In a computer, these pictures are assembled into a 3-dimentianal picture that can display organs, tissues, bones, and any such thing. It can even show ducts, blood vessels and tumors. One of the advantages of CT is that it clearly shows soft tissue structures (such as brain), as well as dense tissue structure (such as bone). The pictures of a Ctscanner are a lot more detailed than the pictures of a regular X-ray machine. It can make pictures of areas protected or surrounded by bones, which a regular X-ray machine can not. Because of this, a CT scanner is said to be 100 times as affective and clever as an ordinary X-ray, and can therefore diagnose some diseases a lot earlier and quicker. It is recent technology that has made it possible to accurately scan objects into a computer in three dimensions, even though the machines and ideas were developed in the 1970s. In the 70s doctors started to use this new type of machine that could give detailed pictures of organs that the older type of x-ray, machine could not give.
Images of human anatomy have been around for more than 500 years now. From the sketches created by Leonardo da Vinci, to the modern day Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scan, images have played a great role in medicine. Evolution in medical imaging brought together people from various disciplines such as Biology, Physics, Chemistry and Mathematics, a collaboration which has further contributed to healthcare as a whole. Modern day imaging improves medical workflows by facilitating a non-invasive insight into human body, accurate and timely diagnostics, and persistence of an analysis.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Correlation- based method: It uses richer gray scale information. It overcome problems of above method, it can work with bad quality data. But it has some of its own problems like localization of points.