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Everyday, an enormous number of medical images are produced by hospitals and medical imaging center for research, surgical and disease diagnostics. Therefore, compression is necessary for storing, managing and transferring these data to make storage manageable. Medical images have some parts which are more important called region of interest (ROI) with useful information for the diagnostic purpose...
Brain hemorrhage is a serious category of head injury that can have a fatal impact on brain function and performance. But sometimes the identification of cerebral hemorrhage can not be known immediately. So far, the identification of cerebral hemorrhage is done through CT Scan image observation that requires special skills. Therefore we need a certain method that can segment the CT Scan image quickly...
Tumour identification has always been a topic that interested researchers around the world. The most challenging phase in tumour identification based on brain MR image is the segmentation of the tumour contour which may contain many unwanted details. Intensity inhomogeneities often occur in real world images and may cause the difficulties in image segmentation. In order to overcome the difficulties...
Cerebral images include several artifacts, such as partial volume effect which limit the diagnostic potential of brain imaging. So, the main objective of this paper is to reduce the effect of partial volume averaging on the boundaries of the ventricles. We thus proposed a fuzzy-genetic brain segmentation scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes from brain...
We propose a new similarity measure, Combined Connectivity and Spatial Adjacency (CCSA), to be used in hierarchical agglomerative clustering (HAC) for automated segmentation of Self-Organizing Maps (SOMs, Kohonen [1]). The CCSA measure is specifically designed to assist segmentation of large, complex, functional image data by exploiting general spatial characteristics of such data. The proposed CCSA...
The most important goal in image segmentation is extraction and description structural configurations with based on few input features or knowledge of an expert. Traditional techniques for medical image segmentation involve more time complexity thereby developing an obstacle in real time application schemes. This paper presents various ways of medical image segmentation. Image segmentation can be...
Accurate estimation of volumes for cerebrospinal fluid (CSF) and brain before and after surgery (pre-op and post-op) plays an important role in analyzing treatment for hydrocephalus. This in turn, relies upon segmentation of brain imagery into brain tissue and CSF. Segmentation of preop images is a relatively straightforward problem and has been well researched. However, segmenting post-op CT-scans...
Brain Tumor which is also known as Intracranial Neoplasm is a vital brain disease. This is caused when abnormal cells are formed within the brain. The two essential types of tumor are Malignant or Cancerous tumor and Brain tumor. The patient does not recover when the growth of the abnormal cells is more than the 50% mark of the brain. The describes two different algorithms of image processing. The...
In this paper, we propose an end-to-end trainable Convolutional Neural Network (CNN) architecture called the M-net, for segmenting deep (human) brain structures from Magnetic Resonance Images (MRI). A novel scheme is used to learn to combine and represent 3D context information of a given slice in a 2D slice. Consequently, the M-net utilizes only 2D convolution though it operates on 3D data, which...
The brain is one of the most complex and integrated organ in the human body which directs our muscle movements, our breathing and internal temperature, furthermore every imaginative sight, perception, and diagram are derived by the brain. The brain's neurons are effected by internal and external stimulations. Those stimulations might have positive and negative influence on brain activity and structure...
Segmentation of the developing cortical plate from MRI data of the post-mortem fetal brain is highly challenging due to partial volume effects, low contrast, and heterogeneous maturation caused by ongoing myelination processes. We present a new atlas-free method that segments the inner and outer boundaries of the cortical plate in fetal brains by exploiting diffusion-weighted imaging cues and using...
The evolutionary success of ants and other social insects is considered to be intrinsically linked to division of labor and emergent collective intelligence. The role of the brains of individual ants in generating these processes, however, is poorly understood. One genus of ant of special interest is Pheidole, which includes more than a thousand species, most of which are dimorphic, i.e. their colonies...
The relation between normal and pathological aging and the cerebrovascular component is still unclear. In this context, the common marmoset, which has the advantage of enabling longitudinal studies over a reasonable timeframe, appears as a good pre-clinical model. However, there is still a lack of quantitative information on the macrovascular structure of the marmoset brain. In this paper, we investigate...
This study aims to analyse the current method in diagnosing early Alzheimer disease and offer a new method to improve the performance of bioinformatics techniques. It proposes a hybrid MRI image processing method to improve the image quality for Alzheimer disease classification. This hybrid method has four stages consisting of image pre-processing, segmentation, feature extraction, and classification...
Schizophrenia (SZ) is a neurological disorder, which affects linguistic, memory, consciousness and executive functions of the brain. Magnetic resonance imaging (MRI) is used to capture structural abnormalities in human brain regions. In this work, segmentation of ventricle region from Schizophrenic MR brain images was carried out using optimized energy minimization framework. The images considered...
One method of patch clamping on brain tissue slices in vitro requires a human operator to visually track a cell's boundary and delicately make contact with a cell's membrane using a micropipette's tip. This type of patch clamping may be automated with computer vision methods; yet this is challenging since it requires precision cell-boundary tracking in the presence of heavy noise and interference...
Brain Tissue segmentation is essential in surgical planning and in diagnosing neurological diseases. This paper enlightened a novel and automatic approach for the segmentation of White Matter, Gray Matter, and CSF tissues from the MR Images of the brain. This Multilevel Segmentation of MR Images of the brain uses the concept of Harmony Search Optimization (HSO). In comparison with the other evolutionary...
There are so many methods used for segmenting the human brain images. The physician are following the invasive method to identify cancer which gives more painful to the patients. CT (computer tomography) scan, MRI (medical reasoning imaging) and CAD (computer aided design) are helpful to analyzing the abnormalities of different parts. For example the Tumor cells, cancer cells and the fractures in...
This article suggests a new scheme to extract brain portion from T1-W Coronal Magnetic Resonance Images (MRI) of human head scans. We propose that the Richardson-Lucy (RL) deconvolution algorithm can be employed to improve the boundary detection. Gaussian type point spread function(PSF) is assumed for the RL algorithm. The improved image is then subjected to binarization, morphological erosion and...
This paper describes an automatic technique to segment the ventricles from brain MRI of young children. The segmentation approach involves skull stripping, extraction of the total cerebrospinal fluid (CSF), isolation of extra-cerebral CSF, removal of CSF in the sulci and fissures and final ventricle segmentation. The segmentation achieves a mean Dice ratio of 0.9246, sensitivity of 0.9363 and specificity...
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