Advantages And Disadvantages Of Image Segmentation

From the digitized content, the user and instituitions can get the benefit. When it make available for use with the licensing of the structure. There are many institutions started to make the collections that are available. The digitisation unable to make use of the materials and calculate the output because the historical materials does not belong to them but the digitized does not given a license to reuse it. According to Wissenburg, 1996, it have been stated that the higher education institutions asked to provide online tutorials and readings in various aspects of history, embedding digitised primary sources

ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There are many researches which show that brain store information as pattern. Some of these patterns are very complicated and allows us to recognize from different angles. This paper gives a review of the artificial neural network and analyses the techniques in terms of performance.

These 16 features included 12 features calculated based on the 6 multispectral bands, which is mean value and standard deviation of these bands. In addition, we chose intensity, texture-variance, texture-mean, and NDVI (Normalized Difference Vegetation Index) for classifications. Finally, training samples were selected for each classification category based on the previously segmented and merged objects

In accession to the binary images, the proposed method may be tested on discrete color images also. These type of

The goal of the feature extraction and selection is to reduce the dimension of the data. In this experiment the dimension of the AVIRIS and HYDICE images reduced to 20 from 220 and 191 respectively using PCA. From the PCA analysis we can see that image of principal component 1 is brightest and sharpest than other PCA image which is illustrated in figure-2.

X-rays — or radiography, as it is known in medicine — allow for looking inside a body and identifying everything from broken bones to swallowed objects. The tool is vital to the ability to diagnose and treat injuries.

The feature extraction basically deals with the ABCD rule. This method is able to provide a more objective and reproducible diagnostic of skin cancers in addition to its speed of calculation.It is based on four parameters: A (Asymmetry) concerns the result of evaluation of lesions asymmetry, B (Border) estimates the character of lesions border, C (Color) identifies the number of colors present in the investigated lesion, and D (Diameter).

The segmentation of biological structure of the brain plays a crucial role in neuro imaging analysis. Major purpose of Brain tumor segmentation is to categorize the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). MR (Magnetic resonance) images are used to produce images of soft tissues that connect, support, or surround other structures and organs of the body of human body are . The objective of this review paper is to present a complete overview for MRI brain tumor segmentation. In this paper various techniques for segmentation have been discussed & it is found that hybrid clustering methodology results better in minimal

Image segmentation is a very important and challenging task that is needed for the purpose of diagnosing brain tumors and other neurological diseases. Brain tumors have different characteristics such as size, shape, location, and image intensities. They may deform neighbouring structures and if there is edema with the tumor, intensity properties of the nearby region change [1]. In adults, the most common and cancer-causing tumor type is glial tumors that have a high mortality rate. They occur in the glial cells of the brain and show a rapid growth by extending into the healthy brain tissues. Image segmentation is the separation of an image into segments called classes or subsets, according to one or more characteristics or features, and enhancing

Arthropods, annelids, and chordates exhibit examples of true segmentation. Segmentation, being the repetition of structural units including pieces of the body, evolved numerous times. This article ultimately delves into it’s origins and seeks to determine whether segments within animals evolved independently or whether they were derived from a common ancestor. Three hypothesis were developed to examine both similarities, differences, and variance between the processes between 3 phyla. Different animals within each of the categories were studied to collect data and support these hypotheses. Ultimately, it was concluded that there is still not enough data needed to develop a strong answer on how exactly segments evolved in the various phyla.

Magnetic resonance imaging is one of the common clinical method for the diagnosis of brain disorders, evaluation of desease progression and follow-up treatment. In other hand, any neurological disorder frequently has specific effects on brain tissues structure so, segmentation of brain tissues gives information of its current severity. Although, manual segmentation of brain MRI is still a highly used method, it is a time consuming task and subject to high intra_ and inter_ observer variability so, automated brain tissue segmentation method is required. In this study, we proposed a dictionary learning and sparse presentation based method for automated segmentation of healthy brain tissue including white matter, gray matter and cerebrospinal

Besides the tumour heterogeneity, the boundaries of the tumour may be composite and visually unclear to detect at the earlier stages. Some tumour may collapse the adjacent structures in the brain. Furthermore, artefacts and noise in the brain tumour images complicates the obscurity in tumour detection. Hence developing an efficient and automatic image segmentation approach is necessary to provide a better tumour detection performance especially in MRI brain

Medical imaging helps doctors in diagnosing and detecting diseases that attack the inside of the body without surgery. Mammogram image is a medical image of the inner breast imaging. Diagnosis of breast cancer needs to be done in detail and as soon as possible for determination of next medical treatment. The aim of this work is to increase the objectivity of clinical diagnostic by using fractal analysis. This study applies fractal method based on 2D Fourier analysis to determine the density of normal and abnormal and applying the segmentation technique based on K-Means clustering algorithm to image abnormal for determine the boundary of the organ and calculate the area of organ segmentation results. The results show fractal method based on

Intensity inhomogeneity correction is often a necessary preprocessing step enabling better Newborn MRI image segmentation. On the other hand, correct segmentation makes intensity inhomogeneity correction rather trivial. Intensity inhomogeneity correction and segmentation can thus be viewed upon as two intertwined procedures. Newborn brain MRI exhibit higher intensity variability for each tissue and low intensity contrast compared to adult brain MRI. These two factors severely hamper the estimation of intensity

My research focuses on medical imaging and image processing to understand structure and brain function of both healthy and diseased brain. Specifically, some of my interests and expertise include: 1) developing improved MRI relaxometry methods and their applications towards diseased brain, 2) investigating the relationship between brain structure and function and cognitive performance in both healthy and diseased brain such as multiple sclerosis (MS), 3) investigating the neurophysiological bases of brain white matter signals; 4) developing potential biomarker for multiple sclerosis using iron sensitive MRI measures.