Journal of Medical Signals and Sensors
Volume:7 Issue: 2, Apr-Jun 2017

Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar–venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches.

Comprehension of the brain function can be helpful for therapy of neurodegenerative diseases. The brain consists of various types of neuron sets, which organize in three-dimensional complex networks and form neural circuits underlying different behaviors. The circuits act based on the patterns that encode the brain functions. Recognition of the neural patterns requires methods to manipulate the neurons. Electrical stimulation may be the most common method. However, it has significant drawbacks including failure to identify specific neurons in experiments. As an alternative, optical stimulation is a new method that acts in combination with genetic approaches. The novel, optogenetic technology makes it feasible to manipulate either the specific cell types or the neural circuits. This is associated with minimum tissue damages as well as side effects. In this study, a new technology has been introduced, and then its optical and genetical tools have been investigated.

Brain–computer interfaces enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single‑neuron activity from within the brain. One of the most challenging issues in this regard is the balance between the accuracy of brain signals from patients and the speed of interpreting them into machine language. The main objective of this paper is to analyze different approaches to achieve the balance more quickly and in a better way. To reduce the ocular artifacts, the symmetric prewhitening independent component analysis (ICA) algorithm has been evaluated, which has the lowest runtime and lowest signal‑to‑interference (SIR) index, without destroying the original signal. After quick elimination of all undesirable signals, two successful feature extractors – the log‑band power algorithm and common spatial patterns (CSPs) – are used to extract features. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during the imagination of the tongue, feet, and left right‑hand movement. Finally, three well‑known classifiers are evaluated, where the ridge regression classifier and CSPs as feature extractor have the highest accuracy classification rate about 83.06% with a standard deviation of 1.22%, counterposing the recent studies.

The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. In the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients’ matrix in each sub-band. We also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. We demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available OCT B-scans with and without non-neovascular age-related macular degeneration (AMD), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained.

Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologists in the diagnostic process can be so effective. Segmentation of WBCs is usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, kmeans clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell’s image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method.

Intensity modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D CRT) are two treatment modalities in prostate cancer, which provide acceptable dose distribution in tumor region with sparing the surrounding normal tissues. IMRT is based on inverse planning optimization; in which, intensity of beams is modified by using multileaf collimators and also compensators with optimum shapes in step and shoot (SAS) and compensator-based method, respectively. In the recent study, some important parameters were compared in two IMRT and 3D CRT methods. Prescribed dose was 80 Gy for both IMRT procedures and 70 Gy for 3D CRT. Treatment plans of 15 prostate cancer candidates were compared to target the minimum dose, maximum dose, V 76 Gy (for IMRT plans) V 66.5 Gy (for 3D CRT), mean dose, conformity index (CI), and homogeneity index (HI). Dose conformity in compensators-based IMRT was better than SAS and 3D CRT. The same outcome was also achieved for homogeneity index. The target coverage was achieved 95% of prescribed dose to 95% of planning target volume (PTV) in 3D CRT and 95% of prescribed dose to 98% of PTV in IMRT methods. IMRT increases maximum dose of tumor region, improves CI and HI of target volume, and also reduces dose of organs at risks.

Accurate and fast radiation dose calculation are essential for successful radiation radiotherapy. The aim of this study was to implement a new graphic processing unit(GPU) based radiation therapy treatment planning for accurate and fast dose calculation in radiotherapy centers. A program was written for parallel running based on GPU. The code validation was performed by EGSnrc/DOSXYZnrc. Moreover, a semi-automatic rotary asymmetric phantom was designed and produced using bone, lung and soft tissue equivalent materials. All measurements was performed using a Mapcheck dosimeter. The accuracy of code was validated using the experimental data which obtained from the anthropomorphic phantom as the gold standard. Findings showed that, compared with those ofDOSXYZnrc in the virtual phantom and for most of the voxels (>95%) less than 3% dose-difference or 3 mm distance-to-agreement was found.Moreover, considering the anthropomorphic phantom, compared to the Mapcheck dose measurements less than 5% dose-difference or 5 mm distance-to-agreement was observed. Fast calculation speed and high accuracy of GPU-based Monte Carlo method in dose calculation may be useful in routine radiation therapy centers as core and the main component of a treatment planning verification system.

Radiotherapy is one of the treatment options for locally advanced prostate cancer; however, with standard radiation doses, it is not always very effective. One of the strategies to improve the efficiency of radiotherapy is increasing the dose. In this study, to increase tumor local control rates, a new radiotherapy method, known as dose painting (DP), was investigated. To compare 3 dimensional conformal radiotherapy (3D-CRT) and intensity modulated radiotherapy (IMRT) plans with DP for prostate cancer. Twenty-four consecutive patients with locally advanced prostate cancer who underwent an multiparametric-magnetic resonance imaging (MP-MRI) (T2w, diffusion weighted image, dynamic contrast enhancement, and MRS) scan before a diagnostic biopsy from September 2015 to April 2016 were invited to take part in this study. The tumor local control probability (TCP) values for 3D-CRT, IMRT, and DP techniques were 45, 56, and 77%, respectively. The DP technique had a 37.5 and 71% higher TCP than IMRT and 3D-CRT, and these differences were statistically significant (P 0.001). The mean normal tissue complication probability (NTCP) values of the organ at risks for D-CRT, IMRT, and DP showed that there were statistically significant differences among them in three plans (P=0.01). DP by contours using MP-MRI is technically feasible. This study evaluated biological modeling based on both MP-MRI defined subvolumes and pathologically defined subvolumes. The MPMRI- guided DP results in better TCP/NTCP than 3D-CRT and IMRT.