Background to Image Segmentation
Image Segmentations purpose is to partition an image into numerous segments, it is useful as it can simplify or change an image to make it easier to interrupt and more meaningful. (Wikipedia, 2013) Segmentation is based on a variety of measurements taken from an image, it is usually the first step of many aimed at overall image understanding. Image segmentation is based on measurements taken from the image which include grey level, colour, texture and motion.
Image Segmentation is used for various real world applications, these include object recognition, face detection, finger print scanning to name a few.
Real World Applications
Object Detection
Image Segmentation is used during human face detection. In the paper I researched a segmentation algorithm was created for use in human face detection in colour images, skin regions are extracted using a set of boundary rules based on skin colour distribution. The algorithm uses the hue and chrominance information of an image on top of the RGB properties to distinguish between skin and non skin pixels, thus segmenting the face it is focusing on. (Thakur and Paul et al., 2011, pp. 53--60)
Medical Imaging
Image Segmentation is also used in medical imaging, I will focus on shape based segmentation for this example. (Wikipedia, 2013) This is a method widely used in the medical industry, this method can be used on MRI images of the brain to segment different areas of the brain or segment areas of importance such as a tumour. It is also used to access a patient's cardiac function and diagnose cardiac disease, the left ventricle of the heart is segmented to gain data such as ejection fraction and stroke volume. (Tsai and Yezzi Jr et al., 2003, pp. 137--154...
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...comparison of various image edge detection techniques. International Journal of Image Processing (IJIP), 3 (1), pp. 1--11.
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Khurshid, K., Siddiqi, I., Faure, C. and Vincent, N. 2009. Comparison of Niblack inspired Binarization methods for ancient documents. pp. 72470--72470.
Tsai, A., Yezzi Jr, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W. and Willsky, A. 2003. A shape-based approach to the segmentation of medical imagery using level sets. Medical Imaging, IEEE Transactions on, 22 (2), pp. 137--154.
Feng, M. and Tan, Y. 2004. Adaptive binarization method for document image analysis. 1 pp. 339--342.
Farid, S. and Ahmed, F. 2009. Application of Niblack's method on images. pp. 280--286.
Images of parts of the Beowulf manuscript are scanned in 24-bit colour, both under visible and ultraviolet light. These images may be of entire pages, or just single words or letters. The resulting image files are huge: at a maximum size of 2320 by 3072 pixels each image takes up about 20-25 MB, however they can reveal even more information than would physical examination of the original manuscripts, for example allowing the detection of alterations to the manuscripts and revealing letters that have been obscured by repairs to eighteenth century fire damage.
This technique a significant difference in limit of detection and the required time for analysis when it compared with number of other technique.
From my shadowing experience, I learnt that attention to detail is crucial when taking diagnostic images where the positioning of the patient and cooperating with the patient to produce accurate images is a large part of the job. A level photography has helped with my attention to detail which is why I better understood the preparations taken by the radiographer when processing accurate diagnostic
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]...
In order to really understand where medical imaging is currently at one must have a basic understanding of where it came from. Twenty years ago radiologist would read and dictate the images on film. They were able to accomplish this by holding the film up to light boxes. This method provided no way for the radiologist to interact or manipulate the images
One of the most recently new advances in radiology is the use of magnetic resonance imaging (MRI). MRI has been around for the past century. It was at first called Nuclear Magnetic Resonance (NMR) and then it changed to MRI once there was an available image. Walter Gerlach and Otto Stern were the first scientists to start experimenting with the magnetic imaging. Their very first experiment was looking at the magnetic moments of silver by using some type of x-ray beam. The scientists then discovered this was by realizing that the magnetic force in the equipment and in the object itself. In 1975, the first image was finally created using and MRI machine. The scientists used a Fourier Transformation machine to reconstruct images into 2D. The first images ever use diagnostically was in 1980. This is when hospitals began to use them. At first the images took hours to develop and were only used on the patients that needed it most. Even though MRI has been around for a long time, it has advanced and has been one of the best imaging modalities recently (Geva, 2006).
Segmentation is the process of identifying different macro-groups of customers (i.e. segments) based on their common characteristics. The process of choosing a target segment, on which to focus marketing activities on, is a process named targeting.
Dubey, R.B., et al. “The Current CAD and PACS Technologies in Medical Imaging.” International Journal of Applied Engineering Research 4.8 (2009): 1439-1456. Academic Search Complete. Web. 20 Feb. 2011.
...omated detection of lines and points in the images and the use of smart markers in reference video recordings.
Segmentation is a procedure of splitting up the market into different groups of consumers who the same common needs and wants. There are different types of segmentation like geographical segmentation, behavioral segmentation, demographic segmentation, lifestyle segmentation. Lexus divided their vehicles into two categories they four wheel drives and two wheel drives.
the propose of image block motion Extraction is to use 2D image of foreground and background which extract the predominant lines and vanishing point of the background base on geometry method and motion, the background depth map is considered. The depth of the object have used the motion extraction to obtain the motion depth map and then divided according to the color of the object to give the object region segmentation depth map.
Fisher discriminants group images of the same space and separates images of different classes (Delac, Grgic, 2006). Images are projected from N2 dimensional space to C dimensional space that are projected onto a single line. Depending on the direction of the line, the points can either be mixed together, or separated (Batagelj, 2006).
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.
Medical tools in the modern day are almost all made with small, programmed computers inside. “Medical imaging is a vast field that deals with the techniques to create images of the human body. Many of the modern methods of scanning and imaging are largely based on computer technology” ("Importance of Computers in Medicine."). We have been able to apply many of the advanced medical imaging techniques, over the years, thanks to developments in computer science. Magnetic quality imaging uses computer software. To obtain high-resolution images, doctors ...
A line segment is a part of a line that is bounded by two distinct end points, and contains every point on the line between its endpoints. We used line segments in every aspect of designing our home. Every line we drew was a line segment. They were used to build the walls, roof, windows, doors, and everything else.