Sharif Digital Repository

In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

This paper presents a neural network-based method for automatic classification of magnetic resonance images (MRI) of brain under three categories of normal, lesion benign, and malignant. The proposed technique consists of six subsequent stages; namely, preprocessing, seeded region growing segmentation, connected component labeling (CCL), feature extraction, feature Dimension Reduction, and classification. In the preprocessing stage, the enhancement and restoration techniques are used to provide a more appropriate image for the subsequent automated stages. In the second stage, the seeded region growing segmentation is used for partitioning the image into meaningful regions. In the third... 

Glioma is one of the most frequent primary brain tumors in adults that arise from glial cells. Automatic and accurate segmentation of glioma is critical for detecting all parts of tumor and its surrounding tissues in cancer detection and surgical planning. In this paper, we present a reliable classification framework for detection and segmentation of abnormal tissues including brain glioma tumor portions such as edemas and tumor core. This framework learns weighted features extracted from the 3D cubic neighborhoods regarding to gray-level differences that indicate the spatial relationships among voxels. In addition to intensity values in each slice, we consider sets of three consecutive... 

Central Nervous System (CNS) malignant tumors are a leading cause of death worldwide with a high mortality rate. While numerous strategies have been proposed to treat CNS tumors, the treatment efficacy is still low mainly due to the existence of the Blood–Brain Barrier (BBB). BBB is a natural cellular layer between the circulatory system and brain extracellular fluid, limiting the transfer of drug particles and confining the routine treatment strategies in which drugs are released in the blood. Consequently, direct drug delivery methods have been devised to bypass the BBB. However, the efficiency of these methods is not enough to treat deep and large brain tumors. In the study at hand, the... 

Fuzzy functions are used to identify the structure of system models and reasoning with them. Fuzzy functions can be determined by any function identification method such as Least Square Estimates (LSE), Maximum Likelihood Estimates (MLE) or Support Vector Machine Estimates (SVM). However, estimating fuzzy functions using LSE method is structurally a new and unique approach for determining fuzzy functions. By using this approach, there is no need to know or to develop an in-depth understanding of essential concepts for developing and using the membership functions and selecting the t-norms, co-norms and implication operators. Furthermore, there is no need to apply fuzzification and... 

Reduced graphene oxide nanomesh (rGONM), as one of the recent structures of graphene with a surprisingly strong near-infrared (NIR) absorption, is used for achieving ultraefficient photothermal therapy. First, by using TiO2 nanoparticles, graphene oxide nanoplatelets (GONPs) are transformed into GONMs through photocatalytic degradation. Then rGONMs functionalized by polyethylene glycol (PEG), arginine-glycine-aspartic acid (RGD)-based peptide, and cyanine 7 (Cy7) are utilized for in vivo tumor targeting and fluorescence imaging of human glioblastoma U87MG tumors having ανβ3 integrin receptors, in mouse models. The rGONM-PEG suspension (1 μg mL -1) exhibits about 4.2- and 22.4-fold higher NIR... 

Purpose: The aim of this study was to compare diffusion tensor imaging (DTI) isotropic map (p-map) with current radiographically (T 2/T 2 -FLAIR) methods based on abnormal hyper-signal size and location of glioblastoma tumor using a semi-automatic approach. Materials and methods: Twenty-five patients with biopsy-proved diagnosis of glioblastoma participated in this study. T 2, T 2 -FLAIR images and diffusion tensor imaging (DTI) were acquired 1 week before radiotherapy. Hyper-signal regions on T 2, T 2 -FLAIR and DTI p-map were segmented by means of semi-automated segmentation. Manual segmentation was used as ground truth. Dice Scores (DS) were calculated for validation of semiautomatic... 

Introduction: This report describes the synthesis of 2-Amino-2-deoxy(S- benzoylthioacetyl)-D-glucose (S-Bz-TA-DG), radiolabeled with [ 99mTc(CO)3(OH2)3]+ complex with a procedure including deprotection of the benzoyl group, characterization by HPLC using a C18 reverse phase column and preliminary biodistribution study in normal mice. Methods: [99mTc(CO) 3(H2O)3]+ complex was used to label TA-DG with 99mTc. This complex was prepared using up to 46 mCi of Na99mTcO4 in 1mL saline. The radiochemical purity (>95%) was determined by TLC in normal saline solution as the mobile phase. Radio-HPLC analysis of [99mTc]-(TA-DG) at pH=9.5-10, revealed that labeling with 99mTc resulted in the formation of... 

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a... 

Content-based image retrieval (CBIR) has been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture and shape or the semantic meaning of the images. A CBIR system can be used to locate medical images in large databases. This paper presents a CBIR system for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the support vector machine (SVM) learning method. This system can retrieve similar images from the database in two groups: normal and tumoural. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination... 

Intracranial pressure (ICP) is derived from cerebral blood pressure and cerebrospinal fluid (CSF) circulatory dynamics and can be affected in the course of many diseases. Computer analysis of the ICP time pattern plays a crucial role in the diagnosis and treatment of those diseases. This study proposes the application of Linninger et al.s [IEEE Trans. Biomed. Eng. , vol. 52, no. 4, pp. 557565, Apr. 2005] fluidsolid interaction model of CSF hydrodynamic in ventricular system based on our clinical data from a group of patients with brain parenchyma tumor. The clinical experiments include the arterial blood pressure (ABP), venous blood pressure, and ICP in the subarachnoid space (SAS). These...