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Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT...
Vessel segmentation of digital retinal images plays an important role in diagnosis of diseases such as diabetics, hypertension and retinopathy of prematurity due to these diseases impact the retina. In this paper, a novel Size-Invariant Fully Convolutional Neural Network (SIFCN) is proposed to address the automatic retinal vessel segmentation problems. The input data of the network is the patches...
Hypertension poses a serious atherosclerotic risk as it causes both macro- and micro circulation damage. Nailfold capillaroscopy is a valuable yet simple tool to assess microcirculation of blood capillaries. This technique is important in detecting early occurrences of scleroderma spectrum disorders and evaluating Raynaud's Phenomenon. Here it is used in detecting hypertension in patients. Current...
A new algorithm for apple disease image segmentation is proposed. A fuzzy factor for weighted balance is introduced in the algorithm to describe the coefficient of spatial constraints between pixels in neighborhood. For enhancing the integrality of neighbor information, the space distance constraints and the spatial gray constraints are considered. The fuzzy factor in the neighborhood is used to keep...
Blood vessel extraction from retinal fundus images is an important task in developing the computer-aided diagnostic system for ophthalmologists. In this paper we have presented an algorithm for extraction of blood vessels of retinal fundus images and comparison of different moment invariants used for the extraction of features for the vessel pixels. The algorithm uses neural networks for distinguishing...
The extraction of retinal vessels plays an important role in the diagnosis and study of retinal diseases, such as Age-related Macular Degeneration (AMD), Diabetic Retinopathy, Retinopathy of Prematurity (ROP). Vessel diameters, tortuosity, branch lengths, angles, and bifurcations are essential to diagnosing these diseases. However, this is a challenging task due to high noise levels, the low contrast...
Blood vessel segmentation, that is, extraction of the center lines and corresponding local cylinder radii are important for the study of vascular diseases, and in the brain also important for the modeling and understanding of relationships between hemodynamics and electrical neural activity. Several image processing methods have been proposed for vessel extraction in many domains including those that...
Clinical research suggests that changes in the retinal blood vessels (e.g., vessel caliber) are important indicators for earlier diagnosis of diabetes and cardiovascular diseases. Reliable vessel detection or segmentation is a prerequisite for quantifiable retinal blood vessel analysis for predicting these diseases. However, the segmentation of blood vessels is complicated by its huge variations such...
Medical diagnosis is the major challenge faced by the medical experts. Highly specialized tools are necessary to assist the experts in diagnosing the diseases. Gestational Diabetes Mellitus is a condition in pregnant women which increases the blood sugar levels. It complicates the pregnancy by affecting the placental growth. The ultrasound screening of placenta in the initial stages of gestation helps...
At its simplest, volume calculation of MR Image segmented & further soft computed to estimate the affected intensity of Alzheimer's disease is dealt with this paper. It is concerned with Voxel Based Morphometry to render the first part segmentation. The result gives an active region which further needs an estimation to justify the diagnosis. As in this case the image is in form of voxels. When...
This paper presents a texture analysis method on digital chest radiograph to distinguish pneumoconiosis chest from normal chest. First, two lung fields are segmented from a digital chest X-ray image by the active shape model (ASM) method and regions of interest (ROIs) are selected in inter-rib areas along the outer and middle zones of the lung fields. Second, the chest image is preprocessed by multi-scale...
This paper addresses the detection and segmentation of vascular myocytes. The detection and segmentation of these cells are critical to the investigation of atherosclerosis among other cardiovascular diseases. Our approach to detection is unique in that it attempts to compute the underlying external energy in an active contour model. Isolines in this computed external energy can be employed to localize...
Fundus auto-fluorescence (FAF) imaging is a non-invasive technique for in vivo ophthalmoscopic inspection of age-related macular degeneration (AMD), the most common cause of blindness in developed countries. Geographic atrophy (GA) is an advanced form of AMD and accounts for 12-21% of severe visual loss in this disorder. Automatic quantification of GA is important for determining disease progression...
Automatic cell segmentation and tracking in optical microscope images plays a very important role in the study the behaviour of lymphocytes. The variable image contrasts, and especially variable cell densities are major factors to affect the successful cell detection rates. In this paper, two inner and outer cell contours edge detection based cell segmentation algorithms are proposed and used in parallel...
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