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The abstract texture analysis is a popular compression test for finding texture properties of images. Above 35 IEEE research papers were reviewed related to texture analysis; papers were published in between 1992 to 2016. From those papers analyzed in depth and extracted common findings, strengths, weaknesses, gaps and also a comparison between suggested techniques. These suggested methods were used...
The monitoring of volumetric changes in the brain during neurological diseases is done with three dimensional structural MR images. Numerical methods are needed to evaluate the volumetric difference between healthy and diseased MR image groups. Voxel based morphometry is a numerical method used to perform inter-group and intra-group analysis of MR images. With this method, the volume differences between...
We consider the problem of domain shift in analyses of brain MRI data. While many different datasets are publicly available, most algorithms are still trained on a single dataset and often suffer the problem of limited and unbalanced sample sizes. In this work, we propose a surprisingly simple strategy to reduce the impact of domain shift - caused by different data sources and processing pipelines...
With the advent of more powerful computing devices, system automation plays a pivotal role. In the medical industry, automated image classification and segmentation is an important task for decision making about a particular disease. In this research, a new technique is presented for classification and segmentation of low-grade and high-grade glioma tumors in Multimodal Magnetic Resonance (MR) images...
Precise and objective segmentation of atrial scarring (SAS) is a prerequisite for quantitative assessment of atrial fibrillation using non-invasive late gadolinium-enhanced (LGE) MRI. This also requires accurate delineation of the left atrium (LA) and pulmonary veins (PVs) geometry. Most previous studies have relied on manual segmentation of LA wall and PVs, which is a tedious and error-prone procedure...
Detection of infarcted myocardium in the left ventricle is achieved with delayed enhancement magnetic resonance imaging (DE-MRI). However, manual segmentation is tedious and prone to variability. We studied three texture analysis methods (run-length matrix, co-occurrence matrix, and autoregressive model) in combination with histogram features to characterize the infarcted myocardium. We evaluated...
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...
Breast cancer is a significantly alarming health issue for women where Dynamic Contrast Enhanced Magnetic Resonance Imaging serves as a pivot in detection, diagnoses and treatment monitoring. In this paper the response given by breast cancer patients to Neoadjuvant Chemotherapy is analyzed with Magnetic Resonance Images of these patients taken before and after treatment. The MRI images are pre-processed...
In this Paper an Approach For brain tumour types detection and classification is proposed. This study was developed using agency system classification brain tumors. It includes sensor to get the input image. The agency program comprises preprocessing to reduce the salt and pepper noise. The detection of tumor is materialized by using multi-level threshold segmentation, opening and closing morphological...
This paper introduces automatic framework brain tumor detection, which detects and classify brain tumor in MR imaging. The proposed framework brain tumor detection is an important tool to detect the tumor and differentiate between patients that diagnosis as certain brain tumor and probable brain tumor due to its ability to measure regional changes features in the brain that reflect disease progression...
Abnormal growth of cells in proliferated and uncontrolled manner leads to brain tumor. Accurate segmentation of brain tumor plays a vital role for analyzes, diagnosis and planning for treatment. An efficient method for brain tumor segmentation from T1 weighted MRI images is performed in two phases based on histogram thresholding and region growing techniques. Though simple and quick, thresholding...
This paper presents a novel framework for brain tumor diagnosis and its grade classification based on higher order statistical texture features namely kurtosis and skewness along with selected morphological features. These features were extracted from segmented tumorous T2-weighted brain MR images, in order to distinguish high grade (HG) tumor from low grade (LG) tumor. Tumor classification is carried...
The brain is one of the vital organ of the body where it is the custodian of the involuntary and voluntary actions like walking, vision, memory. Now a days the most common brain disorders are Alzheimer's disease, Epilepsy (paralysis or stroke), tumors, brain tumors. Early diagnosis and proper treatment of brain tumors is required. The Computer Aided Diagnostic tools (CAD) can be used by the doctor...
Computer-aided schizophrenia diagnosis is a difficult task that has been developing for last decades. Since traditional classifiers have not reached sufficient sensitivity and specificity, another possible way is combining the classifiers in ensembles. In this paper, we take advantage of random subspace ensemble method and combine it with multi-layer perceptron (MLP) and support vector machines (SVM)...
This paper focuses on breast masses analysis from two different modalities: Magnetic Resonance Imaging (MRI) and Dual-Energy Contrast Enhanced Digital Mammography (DECEDM). After the segmentation step, a set of texture and shape features are extracted from both MRI and DECEDM. Then textural and morphological information extracted from the two modalities are combined in order to improve breast cancer...
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional,...
Computerized automatic recognition of brain tumors in magnetic resonance images (MRI) is a challenging task. Tumors are available at different location, size, shape, and texture of these lesions. Due to intensity similarities between brain lesions and normal tissues, the challenges for the researcher remain for developing progressive more algorithms in the tumor detection. Selection of single spectral...
This study aimed was to initiate an automated tumor diagnostic system based on T1 and T2 weighted magnetic resonance images (MRI). This system comprise of enhancement and segmentation as the initial steps to segment benign and malignant tumor or tissue by various image processing filtering and k-means algorithm. The textural and shape based features were extracted by wavelet transform and Zernike...
Glioma is one of the most common brain tumors with high mortality and its histological grading and typing is important both in therapeutic decision and prognosis evaluation. This paper aims at using the high-throughput image feature analysis method to estimate the histological grade and type of a patient by using Magnetic Resonance Imaging (MRI) instead of histological examination. The proposed method...
The goal of this work was to perform tissue characterization of atherosclerotic carotid plaques to assess their vulnerability to rupture. It was hypothesized that the echo envelope of radiofrequency signals of carotid arteries at 7.2 MHz is distributed locally according to homodyned K-distributions (HKD). Based on the statistical parameters of this distribution, two HKD parametric maps could be constructed,...
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