Manual review mr classification

DOC assigns inmates to facilities and programs on the basis of a classification system. Enclosed is a copy of DOC ' s Objective Classification Manual, which describes the classification system in great detail. The following information comes from the manual.

Classification is the ongoing process of collecting and evaluating information about each inmate to determine the inmate ' s risk and need for appropriate confinement, treatment, programs, and employment assignment, whether in a facility or the community. It tries to balance inmate, departmental, and public interest while preparing inmates for their return to society. The classification system is centrally managed by the director of offender classification and population management and locally managed by the head of the facility the inmate is assigned to.

The system is designed to objectively assess an inmate ' s security, custody, and treatment needs. It applies to all inmates regardless of legal status or sentence length.

DOC also assesses inmate needs in seven areas: medical; mental health; education; vocational and work skills; substance abuse; sex offender treatment; and family, residence, and community resources. An inmate ' s overall classification profile determines the appropriate facility assignment, supervisory approach, housing assignment, accessibility to the community, and program or job placement.

DOC tracks an individual throughout the term of his confinement. The system provides for scheduled reviews for security and custody changes and transfers among facilities and programs.

When one of these documents is the source document for derivative classification, the Declassify on line should read: Source document marked "OADR" Date of source insert date.

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Declassification is not automatically an approval for public disclosure. Marking Downgraded or Declassified Material: Classified information that is downgraded or declassified should be promptly and conspicuously marked to indicate the change.

Classification Pending: Material that you generate, and that you believe may be classified and for which no classification guidance is available, must be protected and handled as though classified at the appropriate level until a classification determination is obtained from the appropriate government organization. This material should be marked as follows:.

The derivative and warning notice markings need not be applied in this situation. Reproduction should be held to an absolute minimum until a classification determination is received. Reference 1. Classification and control markings and country designators authorized for use by the Intelligence Community are compiled in the Authorized Classification and Control Markings Register maintained by the Community Management Staff. Marking Classified Information Physically marking classified information with appropriate classification and control markings serves to warn and inform holders of the degree of protection required.

Overall Classification Markings The overall i. Portion Marking The title or subject of a classified document is marked with the appropriate classification abbreviation in parentheses -- TS , S , C , or U immediately following and to the right of the title or subject. Automated Information Processing Requirements Use of automated information systems to route and control access to information is forcing changes in how documents are marked.

Markings for the "Classified by," "Derived from," and "Declassify on" Lines All classified information will be marked to reflect the source of the classification, reason for the classification, and instructions for declassification or downgrading.

The majority of compounds are polydisperse and polycrystalline. However, actively targeted iron oxides, which tend to contain smaller superparamagnetic labels, are monodisperse and monocrystalline. Another group of particulate contrast agents are liposomes. Paramagnetic ions may either be encapsulated in the aqueous compartment of the liposomes or be linked to their lipid bilayers.

More sophisticated liposome compounds have been developed including phospholipid spin-labeled and amphipathic chelate complexes. The primary organ selected for developing passive targeting compounds vascular, hepatobiliary, and reticuloendothelial is the liver.

In addition to vascular structures, both hepatocytes and the RES may be targeted. By dynamic examinations, vascular structures as well as highly vascularized lesions are commonly highlighted with the conventional low molecular weight contrast agents. Mn-DPDP is a positive multipurpose agent, which taken up by hepatocytes Some contrast agents may also be capable of targeting other organs such as the spleen, pancreas, bone marrow, lymph nodes, adrenals, muscles and particularly the heart as well as inflammation and specific tumors.

However, they are not yet ready for use in clinical practice. The first MRI contrast agent to be used was ferric chloride in Over the past 3 decades, many contrast agents have been developed for use in clinical practice and some of them were withdrawn as result of safety concerns. The MRI contrast agents discovered to date may be classified into various groups according to a number of criteria: chemical composition, the presence of metal atoms, route of administration, magnetic properties, effect on the image, biodistribution and further applications.

As a result there are variations in the clinical implications, mechanisms of action, safety, pharmacokinetics and pharmacodynamics of these contrast agents. Currently, newer and safer MRI agents capable of targeting organs, sites of inflammation and specific tumors are under investigation in order to develop contrast agents with higher disease specificity. J Comput Tomogr. Prog Polym Sci. View Article : Google Scholar.

Contrast Media Mol Imaging. J Magn Reson Imaging. Mater Sci Eng C. J Mol Struct. Clin Radiol. Zhen ZP and Xie J: Development of manganese-based nanoparticles as contrast probes for magnetic resonance imaging. NMR Biomed. Magn Reson Med. PloS One. J Gastroenterol. Adv Mater. J Nanopart Res. Thunus L and Lejeune R: Overview of transition metal and lanthanide complexes as diagnostic tools.

Coordin Chem Rev. Chang CA: Magnetic resonance imaging contrast agents. Design and physicochemical properties of gadodiamide. Invest Radiol. Magn Reson Imaging.

Appl Magn Reson. Sci Rep. Magn Reson Q. Radiol Clin North Am. Acad Radiol. Relaxometry, biodistribution, and magnetic resonance imaging in nude mice grafted with human colorectal carcinoma. Jung CW and Jacobs P: Physical and chemical properties of superparamagnetic iron oxide MR contrast agents: ferumoxides, ferumoxtran, ferumoxsil. Eur J Radiol. Wiley Interdiscip Rev Nanomed Nanobiotechnol. J Comput Assist Tomogr.

Acta Radiol. November Volume 38 Issue 5. Sign up for eToc alerts. You can change your cookie settings at any time by following the instructions in our Cookie Policy. To find out more, you may read our Privacy Policy. I agree. Limitations: The most important limitations that make brain tumors segmentation remaina challenging task are the variety of the shape and intensity of tumors in addition to the probability of inhomogeneity of tumorous tissue.

The medical imaging technologies revolutionized medical diagnosis over the last 40 years allowing doctors to detect tumors earlier and improve the prognosis. Moreover, they give physicians the ability to investigate the internal structures and functions of the human body with a range of imaging systems and use them to plan treatments and surgeries 1 , 2.

MRI is the most commonly used system to diagnose brain tumors since it presents accurate details such as the type, position, and size of the investigated tumor. Additionally, it is capable of differentiating soft tissue with high resolution and is more sensitive detecting and visualizing subtle changes in tissue density and the physiological alternations associated with the tumor 3 - 7.

MRI has further role to help the clinicians move to precise lesion diagnosis instead of the indirect diagnosis using cerebral angiography 8. Furthermore, MRI is different from the other medical technologies due to its capability to use different image acquisition protocols to produce multiple images with different contrast visualization of the same tissue. These protocols may slightly vary and provide more valuable anatomical information to help the clinicians study the diseased brains precisely 3 , 5 , 8 , 9.

Generally, based on the advantages of the MRI, modalities can be categorized into T1-w images, which are anatomical images and beneficial for black hole detection, which looks as hypo-intense or dark area relative to the white matter WM intensities. On the other hand, T2-w images are suitable for tissue pathology and show well-defined tumor delineation. Moreover, the WM lesions are shown as hyper-intense or bright areas relative to the WM intensities. Therefore, T2-w images are particularly useful for pathological detection The main drawback of this modality is that the cerebrospinal fluid CSF , grey matter GM , and tumors have close intensities 9.

Clinically, T2-w and T1c-w are the first choice of brain tumor diagnosis methods, but using these two MRI modalities can produce difficulties to differentiate between the new and old tumors or tumors from non-tumoral lesions in addition to grading 6. The analysis of such types of MR images requires advanced computerized tools as well as digital image processing technology. Sometimes, using a contrast enhancement material is essential to clearly highlight the edges of a brain tumor in T1-w images.

This is important to distinguish and recognizesome types of brain tumors in T2-w and T1-w images 5 , 11 , One of the special challenges in MR images is the brain tumors identification since the existence of a brain tumor in MR images can be linked to a highly inhomogeneous signal that can be related to the signal strength of the normal tissue 6 , 7 , 13 , This happens at the boundary of brain tumor and surrounding normal tissue as a result of the influence of partial volumes PV Therefore, the PV blurs the MR images so much and leads to mixing in the intensity value of each voxel with its neighbors 3 , 12 , The current study aimed at providing a review of the automated brain tumors segmentation algorithms and presenting a thorough analysis of these algorithms.

The rest of the study was organized as follows: In sections 2 and 3, a summery about brain tumors segmentation and conclusion are demonstrated, respectively. There is a variety of medical imaging technologies employed to help the clinicians identify pathological conditions inside the body, congenital defects, functionality of the organs and vessels, broken bones, and tumors The increasing number of medical imaging technologies and massiveness of clinical data generation made it impossible to manually classify and segment the data in short time Therefore, computer algorithms are employed to help in specific tasks such as the detection and classification of tumors The computer applications that support medical imaging techniques use image processing algorithms for quantitative analysis to help clinicians who are currently assessing and diagnosing medical images visually.

However, these applications have some limitations in terms of time and accuracy. The reasons behind these limitations are inter-observer variations and error due to stress, oversight, and limited experience.

Hence, computer analysis provides great supports that can help for the subjective diagnosis, and thus, it is essential to improve diagnostic accuracy and confidence even for experts with high experience. Image segmentation is image processing of partitioning the input image into separate areas containing similar pixels attributes.

Extensive different brain tumor segmentation techniques are recently proposed due to quick progress in the medical imaging technology 19 , In general, segmentation techniques are classified based on the image information employed to implement the segmentation. This type of segmentation is also known as threshold-based methods. They are conceptually the simplest segmentation approach and commonly used in two-dimensional images. They only consider the intensity value of the current pixel and discard its neighboring pixels.

Most of pixel based techniques essentially depend on measuring thresholds from the histogram of an image 21 - If the object can be segmented by a single threshold, it is noted as global thresholding. However, if there are more than two objects, then the segmentation should be implemented using local thresholding 20 , 23 , 25 , The main problem of this type of segmentation is that only the intensity information is considered and the relationships between the pixels are neglected; therefore, some pixels do not attend the desired or the background regions.

In general, the threshold-based segmentation methods failed to exploit the provided information by MRI slice and in most cases are usually used to separate and eliminate the background of MRI slice 20 , 26 , This approach is based on dividing the image into regions according to predefined similarity criteria. It is also called region merging and starts with a single pixel or a group of pixels called seeds.

Neighbors of the seeds are checked and only the pixels that satisfy the similarity criteria to the same structure of interest are added The procedure is repeated until no more pixels are added to the structure of interest. The main characteristic of region growing method is the capability to segment similar regions and generate related regions The main disadvantage of region growing methods is the PV effect, which limits the accuracy of MR brain image segmentation.

Therefore, PV blurs the borders between different tissue, since the voxel may contain more than one kind of tissue types 20 , 25 , Region growing methods are more sensitive to noise, thus producing holes in the extracted regions Additionally, if the seed point is not properly chosen, the region grows outside the object of interest or merges with another region that does not belong to the desired object Edge-based segmentation is based on finding the differences instead of similarities between pixels to determine the close boundaries corresponding to the objects of an image Edge-based segmentation is computationally fast and does not need any prior information about image content It is developed to be strongly sensitive to the significant variations in grey level values, and determines if a pixel lies on an edge independently 2.

This approach can be used to overcome the effect of changing the size of the segmented object due to the unsuitable thresholding scheme used for segmentation The main limitation of edge-based segmentation is that the resulting edges do not enclose the object completely. To solve this problem, extra post-processing steps should be taken to link edges that correspond to a single boundary in order to combine these edges into chains to improve the representing edges in the image.

They are more sensitive to image noise, and if the images of the region features differ by only a small quantity between regions, detected edges may be broken 29 , 32 , The contour or snake models are types of the parametric deformable models, which are suitable to segment, match, and track the pathological structures in MR images by exploiting the derived constraints from MR images, and prior knowledge about the tumor location, size, and shape of these pathological areas 20 , 22 , These deformable models are defined as a set of curves directed by the impact of internal and external forces.

The effect of internal forces smooths the curves, while external forces are responsible to change the direction of the curves toward the edges of anatomical area.

Among all segmentation techniques, the deformable model was a successful and efficient technique. The deformable model is used for a wide range of applications, especially in medical fields, due to its capability to accommodate the variability of biological structures of different patients 10 , Jin et al. Additionally, due to the emersion of volumetric three-dimensional medical imaging data, the segmentation of this data is a challenging problem to extract the boundary features that belong to the same structure.

Previous studies commonly focused on segmenting each slice individually slice-by-slice , then merging them to obtain a three-dimensional volume or a continuous surface.