Browsing by Subject "contrast enhancement"

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Open Access
Contrast enhancement of microscopy images using image phase information
(Institute of Electrical and Electronics Engineers, 2018) Çakır, Serdar; Kahraman, Deniz Cansen; Çetin-Atalay, Rengül; Çetin, A. Enis
Contrast enhancement is an important preprocessing step for the analysis of microscopy images. The main aim of contrast enhancement techniques is to increase the visibility of the cell structures and organelles by modifying the spatial characteristics of the image. In this paper, phase information-based contrast enhancement framework is proposed to overcome the limitations of existing image enhancement techniques. Inspired by the groundbreaking design of the phase contrast microscopy (PCM), the proposed image enhancement framework transforms the changes in image phase into the variations of magnitude to enhance the structural details of the image and to improve visibility. In addition, the concept of selective variation (SV) technique is introduced and enhancement parameters are optimized using SV. The experimental studies that were carried out on microscopy images show that the proposed scheme outperforms the baseline enhancement frameworks. The contrast enhanced images produced by the proposed method have comparable cellular texture structure as PCM images.
Open Access
Gadolinium leakage into subarachnoid space and cystic metastases
(2013) Elçin Yildiz, A.; Atli, E.; Karli Oǧuz, K.
Subarachnoid space (SAS) and cystic metastatic lesions of brain parenchyma appear hypointense on fluid-attenuated inversion-recovery (FLAIR) and T1-weighted magnetic resonance imaging (MRI) unless there is a hemorrhage or elevated protein content. Otherwise, delayed enhancement and accumulation of contrast media in SAS or cyst of metastases should be considered. We present hyperintense SAS and cystic brain metastases of lung cancer on FLAIR and T1-weighted MRI, respectively, in a patient who had been previously given contrast media for imaging of spinal metastases and had mildly impaired renal functions, and discuss the relevant literature. © Turkish Society of Radiology 2013.
Open Access
Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data
(2007) Woods, B.J.; Clymer, B.D.; Kurc, T.; Heverhagen J.T.; Stevens, R.; Orsdemir, A.; Bulan O.; Knopp, M.V.
Purpose: To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. Materials and Methods: 4D texture analysis was performedon DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). Results: The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with α = 0.05. The results show that an area under the ROC curve (Az) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. Conclusion: This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted. © 2007 Wiley-Liss, Inc.