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5-Aminolevulinic Acid Induced Fluorescence Is a Powerful Intraoperative Marker for Precise Histopathological Grading of Gliomas with Non-Significant Contrast-Enhancement  [PDF]
Georg Widhalm, Barbara Kiesel, Adelheid Woehrer, Tatjana Traub-Weidinger, Matthias Preusser, Christine Marosi, Daniela Prayer, Johannes A. Hainfellner, Engelbert Knosp, Stefan Wolfsberger
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0076988
Abstract: Background Intraoperative identification of anaplastic foci in diffusely infiltrating gliomas (DIG) with non-significant contrast-enhancement on MRI is indispensible to avoid histopathological undergrading and subsequent treatment failure. Recently, we found that 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence can visualize areas with increased proliferative and metabolic activity in such gliomas intraoperatively. As treatment of DIG is predominantely based on histopathological World Health Organisation (WHO) parameters, we analyzed whether PpIX fluorescence can detect anaplastic foci according to these criteria. Methods We prospectively included DIG patients with non-significant contrast-enhancement that received 5-ALA prior to resection. Intraoperatively, multiple samples from PpIX positive and negative intratumoral areas were collected using a modified neurosurgical microscope. In all samples, histopathological WHO criteria and proliferation rate were assessed and correlated to the PpIX fluorescence status. Results A total of 215 tumor specimens were collected in 59 patients. Of 26 WHO grade III gliomas, 23 cases (85%) showed focal PpIX fluorescence, whereas 29 (91%) of 33 WHO grade II gliomas were PpIX negative. In intratumoral areas with focal PpIX fluorescence, mitotic rate, cell density, nuclear pleomorphism, and proliferation rate were significantly higher than in non-fluorescing areas. The positive predictive value of focal PpIX fluorescence for WHO grade III histology was 85%. Conclusions Our study indicates that 5-ALA induced PpIX fluorescence is a powerful marker for intraoperative identification of anaplastic foci according to the histopathological WHO criteria in DIG with non-significant contrast-enhancement. Therefore, application of 5-ALA optimizes tissue sampling for precise histopathological diagnosis independent of brain-shift.
Orbital metastatic primary mediastinal neuroendocrine tumor: a histopathological case report  [cached]
Hind Alkatan,Ayman Ayoubi
Eye Reports , 2012, DOI: 10.4081/eye.2012.e3
Abstract: Neuroendocrine tumors most frequently involve the gastrointestinal tract and bronchopulmonary system. Few cases of presumed primary neuroendocrine tumors in the orbit have been reported so far and most of the orbital cases are actually metastatic. We describe the unusual occurrence of this tumor in the orbit of a 16-year-old boy. The lesion was initially thought to be primary; however, the diagnosis of a metastatic orbital lesion was later supported by the histopathological appearance of his orbital biopsy, characteristic immunohistochemical profile and the presence of a primary mediastinal tumor. The patient did not have any symptoms suggestive of a carcinoid syndrome during the course of his disease. Unfortunately, tests showed lymph node involvement and distant metastatic lesions and he died from these a few months later while on palliative therapy.
Multidetector Computed Tomography Evaluation of Mediastinal Lesions with Histopathological Diagnosis  [PDF]
Venkateshwaran Arumugam, Hajimohammed Nazir, Konduru Varadarajulu Rajasekar
Open Journal of Respiratory Diseases (OJRD) , 2015, DOI: 10.4236/ojrd.2015.53006
Abstract: The evaluation of mediastinal abnormalities is a challenging radiographical problem. Cross-sectional imaging of the mediastinum by computed tomography now demonstrates precise anatomic details and is the imaging modality of choice for most mediastinal lesions. The following study was undertaken with the objectives of determining the mediastinal lesions affecting the mediastinum and to correlate the computed tomography findings with the histopathology reports. In this study, 50 patients clinically suspected with mediastinal mass lesions or who had chest radio-graph with a suspicious mediastinal lesion were evaluated from October 2013 to September 2014. In our study, we used varying computed tomography features of each mediastinal pathology types to predict a histological diagnosis of mediastinal lesions. Most of the lesions could be predicted with good sensitivity, specificity and diagnostic accuracy.
Review Of Various Image Contrast Enhancement Techniques  [PDF]
VIJAY A. KOTKAR, SANJAY S.GHARDE
International Journal of Innovative Research in Science, Engineering and Technology , 2013,
Abstract: Image Enhancement Is A Processing On An Image In Order To Make It More Appropriate For Certain Applications. It Is Used To Improve The Visual Effects And The Clarity Of Image Or To Make The Original Image More Conducive For Computer To Process. Contrast Enhancement Changing The Pixels Intensity Of The Input Image To Utilize Maximum Possible Bins. We Need To Study And Review The Different Image Contrast Enhancement Techniques Because Contrast Losses The Brightness In Enhancement Of Image. By Considering This Fact, The Mixture Of Global And Local Contrast Enhancement Techniques May Enhance The Contrast Of Image With Preserving Its Brightness. There Are Many Image Contrast Enhancement Techniques Such As HE, BBHE, DSIHE, MMBEBHE, RMSHE, MHE. BPDHE, RSWHE, GHE, LHE And LGCS. This Paper Focuses On The Comparative Study Of Contrast Enhancement Techniques With Special Reference To Local And Global Enhancement Techniques. Also Proposed Solution Is Identified To Apply To This Enhancement Technique. This Novel Method Will Use In Many Fields, Such As Medical Image Analysis, Remote Sensing, HDTV, Hyper Spectral Image Processing, Industrial X-Ray Image Processing, Microscopic Imaging Etc.
Analysis of Contrast Enhancement Techniques For Underwater Image  [PDF]
Balvant Singh ,,Ravi Shankar Mishra , Puran Gour
International Journal of Computer Technology and Electronics Engineering , 2011,
Abstract: Image enhancement is a process of improvingthe quality of image by improving its feature. The underwaterimage suffers from low contrast and resolution due to poorvisibility conditions, hence an object identification becometypical task. In this paper comparative analysis of variouscontrast enhancement techniques for such underwater imagesis presented. The performance of contrast limited adaptivehistogram equalization method is compared with contraststretching, and histogram equalization method. Forcomparing the performance, mean square error and SNR usedas parameters. The method is tested on various type ofunderwater image environment.
Survey of Contrast Enhancement Techniques based on Histogram Equalization
Manpreet Kaur,,Jasdeep Kaur,Jappreet Kaur
International Journal of Advanced Computer Sciences and Applications , 2011,
Abstract: This Contrast enhancement is frequently referred to as one of the most important issues in image processing. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. This paper presents a review of new forms of histogram for image contrast enhancement. The major difference among the methods in this family is the criteria used to divide the input histogram. Brightness preserving Bi-Histogram Equalization (BBHE) and Quantized Bi-Histogram Equalization (QBHE) use the average intensity value as their separating point. Dual Sub-Image Histogram Equalization (DSIHE) uses the median intensity value as the separating point. Minimum Mean Brightness Error Bi-HE (MMBEBHE) uses the separating point that produces the smallest Absolute Mean Brightness Error (AMBE). Recursive Mean-Separate Histogram Equalization (RMSHE) is another improvement of BBHE. The Brightness preserving dynamic histogram equalization (BPDHE) method is actually an extension to both MPHEBP and DHE. Weighting mean-separated sub-histogram equalization (WMSHE) method is to perform the effective contrast enhancement of the digital image.
CONTRAST ENHANCEMENT FOR PCA FUSION OF MEDICAL IMAGES  [cached]
Dr. S. S. Bedi
Journal of Global Research in Computer Science , 2013,
Abstract: : Image Fusion is one of the major research fields in image processing. Image fusion process can be defined as the integration of information from a number of registered images without the introduction of distortion. It is often not possible to get an image that contains all relevant objects in focus. One way to overcome this problem is image fusion, in which one can acquire a series of pictures with different focus settings and fuse them to produce an image with extended depth of field which helps in clinical diagnosis. Image fusion techniques can improve the quality and increase the application of these data. The proposed paper uses multi-image Contrast enhancement for PCA fusion of medical images. The objective of this paper is to propose a technique for fusion of human brain MRI images based on Principal Component Analysis and to improve the visibility of medical images by applying contrast enhancement existing techniques. The PCA fusion technique adopted here improve resolution of the images. The PCA algorithm builds a fused image of several input images as a weighted superposition of all input images. The resulting images contains enhanced information as compared to individual images and also apply Contrast Enhancement technique to improve visibility of medical image details without introducing unrealistic visual appearances and/or unwanted artefacts. It also gives the quality comparison study of original medical images before fusion, after applying PCA and various existing techniques for contrast enhancement for those medical images. Keywords: Adaptive Histogram Equalization, Contrast Enhancement, Histogram Equalization, Image fusion, Principal Component Analysis
Contrast Enhancement through Clustered Histogram Equalization  [cached]
Shen-Chuan Tai,Ting-Chou Tsai,Yi-Ying Chang,Wei-Ting Tsai
Research Journal of Applied Sciences, Engineering and Technology , 2012,
Abstract: This study proposed a contrast enhancement algorithm. Some methods enhance images depending on only the global or the local information, therefore it would cause over-enhancement usually and make the image look unnatural. The proposed method enhances image based on the global and local information. For the global part, we proposed mapping curves to find the new average, maximum and minimum intensity to try to suit the concept of Human Visual System (HVS) for obtaining the better perceptual results. For the local part, we utilized fuzzy c-means clustering algorithm to group image and we can obtain the information of intensity distribution and pixel number from each group. Then we calculate weights according to the information and enhance images by Histogram Equalization (HE) depending on the weights. The experiment results show that our method can enhance the contrast of image steadily and it causes over-enhancement with lower probability than other methods. The whole image not only looks natural but also shows detail texture more clearly after applying our method.
Non-topographical contrast enhancement in the olfactory bulb
Thomas A Cleland, Praveen Sethupathy
BMC Neuroscience , 2006, DOI: 10.1186/1471-2202-7-7
Abstract: We describe a novel neural circuit mechanism, non-topographical contrast enhancement (NTCE), which enables contrast enhancement among high-dimensional odor representations exhibiting unpredictable patterns of similarity. The NTCE algorithm relies solely on local intraglomerular computations and broad feedback inhibition, and is consistent with known properties of the olfactory bulb input layer. Unlike mechanisms based upon lateral projections, NTCE does not require a built-in foreknowledge of the similarities in molecular receptive ranges expressed by different olfactory bulb glomeruli, and is independent of the physical location of glomeruli within the olfactory bulb.Non-topographical contrast enhancement demonstrates how intrinsically high-dimensional sensory data can be represented and processed within a physically two-dimensional neural cortex while retaining the capacity to represent stimulus similarity. In a biophysically constrained computational model of the olfactory bulb, NTCE successfully mediates contrast enhancement among odorant representations in the natural, high-dimensional similarity space defined by the olfactory receptor complement and underlies the concentration-independence of odor quality representations.Primary olfactory sensory neurons (OSNs) line the nasal epithelium and respond to the presence of odors that diffuse through the nasal mucus layer and bind to olfactory receptors expressed on OSN cilia. Each OSN expresses only one or a few species of olfactory receptor, which define the molecular receptive range [1], or chemical receptive field, of that OSN. The axons of several thousand OSNs expressing the same receptors converge onto a few discrete glomeruli in the olfactory bulb, within which they form glutamatergic synapses with the dendrites of mitral cells, periglomerular cells, and external tufted cells. Both mitral and external tufted cells also form excitatory synapses onto periglomerular cells within the same glomerulus, whereas the
Large contrast enhancement of graphene monolayers by angle detection  [PDF]
Victor Yu,Michael Hilke
Physics , 2009, DOI: 10.1063/1.3247967
Abstract: Exfoliated graphene monolayers are identified by optical inspection. In order to improve the monolayer detection, we investigate the angle dependence of the optical contrast of graphene on a 90nm SiO$_2$/Si substrate. We observe a significant enhancement of the visibility of graphene by changing the polarization and the angle of optical incidence. This method can be used to detect graphene on new substrate designs such as GaAs/AlAs based materials, which have a much cleaner surface.
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