Developing an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal

Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit

The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present s

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

Acute myeloid leukemia (AML) is heterogeneous disorders originated from the abnormalities in the proliferation and maturation of myeloid progenitors in bone morrow. There is a clinical correlation between immunity engines and disease progression, but this relationship is not completely clear yet. This study was designed to assess the full immune response in Iraqi patients diagnosed with AML. Patients and healthy volunteers were divided into three groups: newly diagnosed untreated, under chemotherapy treatment patients and control group. A significant reduction were seen in C4 and IFN-γ levels in both untreated and treated groups with no significant difference between untreated and treated groups. On the other hand, IL-2 and IL-8 levels inc

Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin

Chronic myeloid leukemia (CML) is a myeloproliferative disorders characterized by formation of Philadelphia chromosome. After disease development, several events may associate with the reduction of anti-tumor immunity. The present study was designed to investigate the immunological profile of innate and adaptive immune response in Iraqi patients with CML. Patients were grouped into untreated (UT), treated (T) with chemotherapy, while another apparently healthy individuals were recruited to represent the control (C) group. Methods: ELISA technique was used to estimate serum levels of GM-CSF, IL-1a, IL-8, IL2, INF-?, IL-4, and IL-10 while SRID was used to estimate serum levels of C4, IgM, IgA, and IgG. Results: Regarding to innate immune resp

ABSTRACT Wound is damage or disruption to the normal anatomical structure and function. Carrageenan is sulphated polysaccharide found in Gigartina, Chondrus and Eucheuma species in the red algal family. Having anticancer, anti-inflammatory and renewal of tissues. Our study aimed to detect the role of kappa carrageenan in the burned skin wound repair. Skin burn were performed in the right and left cheek of 20 male rats (aged 7-8 weeks weighing 300-350 g). Burned skin rats were categorized into two equal groups. Burned areas of right side were treated with a local application of 1 ml of kappa carrageenan solution once daily (treatment group) and the left side receive no treatment (control group). After 5, and 10 days, 5 rats from each

In this paper, a single-phase boost type ac-dc converter with power factor correction (PFC) technique is designed and implemented. A current mode control at a constant switching frequency is used as a control strategy for PFC converter. The PFC converter is a single-stage singleswitch boost converter that uses a current shaping technique to reshape the non-sinusoidal input current drawn by the bulky capacitor in the conventional rectifier. This technique should provide an input current with almost free-harmonics, comply with the IEC61000-3-2 limits, and a system operates with near unity power factor. The other function of the boost converter that should be
accomplished is to provide a regulated DC output voltage. The complete designed

Abstract

Robust controller design requires a proper definition of uncertainty bounds. These uncertainty bounds are commonly selected randomly and conservatively for certain stability, without regard for controller performance.  This issue becomes critically important for multivariable systems with high nonlinearities, as in Active Magnetic Bearings (AMB) System. Flexibility and advanced learning abilities of intelligent techniques make them appealing for uncertainty estimation. The aim of this paper is to describe the development of robust H₂/H_∞ controller for AMB based on intelligent estimation of uncertainty bounds using Adaptive Neuro Fuzzy Inference System (ANFIS).  Simulatio