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A prime factor deciding the survival rate of a breast cancer patient is the accuracy with which the malignancy grade of a breast tumor is determined. A Fine Needle Aspiration (FNA) biopsy is a key mechanism for breast cancer diagnosis as well as for assigning grades to malignant cases. In this paper, based on published cytological malignancy grading systems, we propose six computer-aided grading frameworks...
Objective: To design the lip-color features and classification model and provide an automatic, quantitative method based on the lip detection in facial image. Methods: In this paper, We adopted the lip segmentation algorithm based on the three-dimensional mixture Skin Gaussian Model and color classification in SVM to solve this problem. Specifically, we used the GMM based iterations to confirm skin-color...
Mitochondria are organelles that play an important role in the cell's life cycle as the energy generating units. State-of-the-art imaging modalities, such as electron microscopy, allow researchers to study tissues, cells and sub-cellular organelles at high resolution. Recently, various works address the problem of segmenting mitochondria in electron microscopy images. Manual segmentation of mitochondria...
Melanomas are the most aggressive form of skin cancer. Due to observer bias, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion areas in the dermoscopy images. In this paper, we present a deep learning method for automatic skin lesion segmentation. We use a subset of the...
In this paper, we are proposing a 3D segmentation and interactive visualization workflow. The segmentation implementation uses a globally convex multiphase active contours without edges. This algorithm has been proven to be initialization independent due to their globally convex formulation and better than other approaches due to robustness to image variations and adaptive energy functionals. The...
In order to understand the underwater environment, it is essential to use sensing methodologies able to perceive the three dimensional (3D) information of the explored site. Sonar sensors are commonly employed in underwater exploration. This paper presents a novel methodology able to retrieve 3D information of underwater objects. The proposed solution employs an acoustic camera, which represents the...
The eye structure of insects, which is called a compound eye, has interesting advantages. It has a large field of view, low aberrations, compact size, short image processing time, and an infinite depth of field. If we can design a compound eye camera which mimics the compound eye structure of insects, compound images with these interesting advantages can be obtained. In this paper, we consider the...
Computer vision occupies an important position in the recognition of the image processing, in order to accurately get the grape disease and the degree of damage. In this paper, an image segmentation technique is proposed to improve the morphological watershed algorithm, and edge detection and threshold segmentation are used to realize the image in the normal parts and diseased parts of the grape....
The current animal sperm morphology analysis is mostly achieved through computer vision technology. By using image processing technique, the parameters of each sperm such as ellipticity, elongation of the head, mid-piece angle and percentage of acrosome can be calculated, then the quality of sperm can be evaluated. This system uses K-means algorithim to segment the sperm images, thinning algorithm...
In recent years there have been many proposals for automated applications in vegetable harvesting. There are two major challenges: estimation of yield and harvesting of fruit trees. This research proposes a new algorithm that allows accurate counting of the number of fruits on a tree to accurately estimate the output of a dragon fruit. The algorithm consists of the following main steps: image segmentation,...
Extracting and recognizing mathematical expressions of scientific documents are key steps in the process of mathematical retrieval system, where the documents contain different components such as text, tables, figures, and mathematical expressions. There are several methods proposed to handle the components of documents. Those methods have investigated the feature of components based on the segmented...
Brain tumors, especially high-grade gliomas, are one of the most lethal cancers for humankind today. Early and accurate diagnosis of tumor grading is the key for subsequent therapy and treatment. In the past, conventional computer-aided diagnosis relies on handcrafted features from magnetic resonance images (MRI), which are usually inaccurate and laborious. Recently, deep neural networks have been...
With the rapid adoption of smartphones and tablets, more and more remote medical diagnostic applications have mushroomed. Tongue Diagnosis (TD) is a kind of noninvasive diagnostic technique, which offers significant information for health conditions. However, it is rather tough to extract the tongue from a high-quality image, in which there is a definite large area of the tongue, to say nothing of...
Dense prediction is concerned with predicting a label for each of the input units, such as pixels of an image. Accurate dense prediction for time-varying inputs finds applications in a variety of domains, such as video analysis and medical imaging. Such tasks need to preserve both spatial and temporal structures that are consistent with the inputs. Despite the success of deep learning methods in a...
Segmenting curvilinear structures in retinal images is important in early diagnosing of some diseases and monitoring their progress. In this work, we proposed an automatic segmentation method to extract vascular network in CHASE data set. We utilized deep learning framework to build our layers that accept image patches as input and produce the segmented image as output. Our work characterized by its...
Local community detection (or local clustering) is of fundamental importance in large network analysis. Random walk based methods have been routinely used in this task. Most existing random walk methods are based on the single-walker model. However, without any guidance, a single-walker may not be adequate to effectively capture the local cluster. In this paper, we study a multi-walker chain (MWC)...
Since road markings are one of the main landmarks used for traffic guidance, perceiving them may be a crucial task for autonomous vehicles. In visual approaches, road marking detection consists in detecting pixels of an image that corresponds to a road marking. Recently, most approaches have aimed on detecting lane markings only, and few of them proposed methods to detect other types of road markings...
The perception of the environment is one the most important tasks related to automated driving systems navigation. Typically, the robot detects the road surface and obstacles to perform the local navigation control safely. However, working with image data brings several problems related to environment elements like shadows, light reflection, low-variance textures, etc., which could compromise the...
Evaluating the variations of lesion volume plays an important role in many medical applications. It helps radiologists to follow up patients and examine the effects of therapy. Several approaches were being proposed to come up with medical expectations. This work comes within this context. We present a new approach based on the local dissimilarity volume (LDV) that is a 3D representation of the local...
Currently, the prediction of fish species and catches is based on the experience of fishermen. Echo sounders can support fisheries; however, they cannot identify fish species. A system for the identification of fish species with machines has not been established. The purpose of this research is to propose a new method for the identification of fish species using echo sounders attached to a set-net...
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