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This paper proposes a new method for melasma pigmentary area segmentation utilizing re action-diffusion based level set model (RDLSM) together with local entropy thresholding. In the adopted level set model, a diffusion term is used to regularize the level set function while a reaction term with anticipated sign property is used to force the zero level set towards desired locations. Then local entropy...
Recognition of humans based on characteristic eye features has taken a significant place in security and identification systems in the past two decades. The complex pattern of the iris, that is unique to an eye, makes iris a great biometric descriptor. The first step in the process of the biometric identification based on iris is its localization in an image. The shape of the iris, its size, position,...
The paper proposes a novel image enhancement method based on histogram equalization called Quadrants Histogram Equalization with a Clipping Limit (QHECL). The proposed method consists of four steps: (i) The first step is to compute the median value of the input image, which is used to divide the input histogram into two sub histograms. (ii) We calculate the average brightness value of each sub histogram...
Hybrid compression techniques are used to improve the compression efficiency in case of medical images. In region of interest based hybrid compression, the image is divided into foreground which contains diagnostically important areas such as tumor in case of brain MRI images and the remaining is the background region. The technique performs lossless coding of ROI while the background is heavily compressed...
The present work contend with physical plasma and digital image processing and this process needs a high degree of safety. This paper sheds light on the plasma interactions with living cells studying the blood cells expose to cold Plasma using texture analysis, it is a new method to finding the changes which occur in the blood texture as a result from exposure the blood samples to the plasma for different...
In the paper, we present a method for evaluating the quality of tongue images in Traditional Chinese Medicine (TCM). First, we preprocess the original images to segment the tongue images. Second, geometric features, texture features and spectral entropy features and spatial entropy features based on Spatial-Spectral Entropy-based Quality (SSEQ) index of tongue images are extracted respectively to...
Supervoxel plays a significant role in video segmentation task in which moving objects in the video are detected and their external boundaries are obtained. This is due to the observation that hierarchies of supervoxel can process the video into a multiscale decomposition with more information for later analysis than other approaches using the concept of supervoxels to video segmentation. Most available...
Proper recognition of microscopic sperm cells in video images is an important step in diagnosis and treatment of male infertility. The small sizes of the sperm cells make their segmentation and detection an important stage in the microscopic images analysis. Histogram-based thresholding schemes are one of the common approaches for this purpose. This paper proposes a non-linear amplitude compression...
Microcalcifications are the earliest sign of breast carcinoma. Their typical size is about 1 mm, which is why it is difficult to detect for an expert. Therefore, a tool that eases their visualization becomes relevant. Segmentation gives the candidate areas that could contain microcalcifications. A preprocessing step can improve segmentation performance but the algorithm becomes database dependent...
This work compares Triangle, Maximum Entropy and Mean Peak thresholding methods to locate the optic disc in color fundus images. Localizing the optic disc is a significant task in an automated retinal image analysis process as is used on most vessel segmentation, disease diagnostic, and retinal recognition algorithms. The DIARETDBv1 dataset includes 89 retinal images are used to evaluate the 3 thresholding...
Image segmentation is one of the most common steps in digital image processing. It classifies a digital image into different segments. There are many algorithms for image segmentation such as thresholding, edge detection, and region growing, which finding a suitable algorithm for medical image segmentation is a challenging task. This is due to noise, low contrast, and steep light variations of medical...
To diagnose serious eyes diseases, ophthalmologists use the color retinal images of a patient acquired from the digital fundus camera, such as the diabetic retinopathy that affects the morphology of the blood vessels tree, so an automatic detection and extraction of blood vessels in retinal images is important. In this paper, we present a method to extract blood vessels and delineate vascular intersection...
In this paper, we propose an automatic thresholding method based on 2D Tsallis-Havrda-Charvat entropy and histogram of local binary patterns (LBP). Tsallis-Havrda-Charvat entropy is extracted from 2D histogram, which is calculated by using the LBP decimal value of a pixel and the average decimal value of its local neighborhood. Few parameters influenced the thresholding results. Therefore, an automatic...
Fusion of image segmentations using consensus clustering and based on the optimization of a single criterion (commonly called the median partition based approach) may bias and limit the performance of an image segmentation model. To address this issue, we propose, in this paper, a new fusion model of image segmentation based on multi-objective optimization which aims to avoid the bias caused by a...
Grading of cancer offers insight to the occurrence and progress of the disease. The course of treatment is planned depending on the grade of cancer. Segmentation of the glandular structure of tissue is a prerequisite for grading of colon, prostate and breast cancers. Manual segmentation method is time-consuming and suffers from the curse of observer bias. We propose an automated solution for gland...
The exact measure of mitotic nuclei is a crucial parameter in breast cancer grading and prognosis. This can be achieved by improving the mitotic detection accuracy by careful design of segmentation and classification techniques. In this paper, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques...
Image segmentation is important part of image processing applications. A given image is separated the different regions with homogeneous characteristics at image segmentation process. This paper will introduce an image segmentation approach that can be used in image processing applications. Recently Neutrosophic Set (NS) that use to evulate indeterminacy information, and metaheuristic algorithms are...
The key mechanical components of belt conveyor is lack of effective monitoring at present. The traditional monitoring methods such as visual inspection, temperature measurement have shortcoming, the huge workload, blind spots and other issues are difficult to solve. This paper proposes an inspection robot program with infrared thermometer for belt conveyor. Along the inspection track, the motor drive...
The function of visual attention mechanism is to acquire the useful visual information at the fastest speed. The Itti visual attention model commonly used at present has achieved good effects in natural image. In order to find the region of interest as soon as possible, this paper attempts to introduce visual attention mechanism into pulmonary nodules detection. However, the Itti model is more to...
Medical image processing plays an important role in supporting the diagnosis of various diseases. Brain magnetic resonance imaging (MRI) image is widely used to support the decisions from doctors who will decide if there are any issues in a brain. The essence of the MRI is segmentation which is the basic for damaged area selection, quantitative measurement and 3-dimensional reconstruction. In order...
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