scholarly journals A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Yoo Na Hwang ◽  
Min Ji Seo ◽  
Sung Min Kim
Keyword(s):  
Level Set ◽  
Ground Truth ◽  
Skin Lesions ◽  
Low Contrast ◽  
Biomedical Image ◽  
Clinical Applicability ◽  
Model Combining ◽  
Proposed Model ◽  
Level Set Approach ◽  
Surrounding Skin

The segmentation of a skin lesion is regarded as very challenging because of the low contrast between the lesion and the surrounding skin, the existence of various artifacts, and different imaging acquisition conditions. The purpose of this study is to segment melanocytic skin lesions in dermoscopic and standard images by using a hybrid model combining a new hierarchical K -means and level set approach, called HK-LS. Although the level set method is usually sensitive to initial estimation, it is widely used in biomedical image segmentation because it can segment more complex images and does not require a large number of manually labelled images. The preprocessing step is used for the proposed model to be less sensitive to intensity inhomogeneity. The proposed method was evaluated on medical skin images from two publicly available datasets including the PH2 database and the Dermofit database. All skin lesions were segmented with high accuracies (>94%) and Dice coefficients (>0.91) of the ground truth on two databases. The quantitative experimental results reveal that the proposed method yielded significantly better results compared to other traditional level set models and has a certain advantage over the segmentation results of U-net in standard images. The proposed method had high clinical applicability for the segmentation of melanocytic skin lesions in dermoscopic and standard images.

Generative Design of Origami-Inspired Mechanisms With a Variational Level Set Approach

2020 ◽  
Author(s):  
Qian Ye ◽  
Xianfeng David Gu ◽  
Shikui Chen
Keyword(s):  
Level Set ◽  
3D Structure ◽  
Level Set Function ◽  
Shell Models ◽  
Proposed Model ◽  
Out Of Plane ◽  
Variational Level Set ◽  
Systematic Solution ◽  
Set Function ◽  
Level Set Approach

Abstract Origami has inspired the engineering design of self-assemble and re-configurable devices. Under particular crease patterns, a 2D flatten object can be transformed into a complex 3D structure. This work intends to find out a systematic solution for topology optimization of origami structures. The origami mechanism is simulated using shell models where the in-plane membrane, out of plane bending, and shear deformation can be well captured. Moreover, the pattern of the folds is represented implicitly by the boundaries of the level set function. The topology of the folds is optimized by minimizing a new multiobjective function, aiming to balance the kinematic performance with the structural stiffness as well as the geometric requirements. Besides regular straight folds, our proposed model can mimic crease patterns with curved folds. With the folding curves implicitly represented, the curvature flow are utilized to control the complexity of the generated folds. The effectiveness of the proposed method is demonstrated through the computational generation and physical validation of a thin-shell origami gripper.

scholarly journals A Novel Fuzzy Level Set Approach for Image Contour Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yingjie Zhang ◽  
Jianxing Xu ◽  
H. D. Cheng
Keyword(s):  
Differential Equation ◽  
Level Set ◽  
Energy Function ◽  
Level Set Methods ◽  
Contour Detection ◽  
Low Contrast ◽  
Probability Densities ◽  
Entropy Principle ◽  
Image Contour ◽  
Level Set Approach

The level set methods have provided powerful frameworks for image segmentation. However, to obtain accurate boundaries of the objects, especially when they have weak edges or inhomogeneous intensities, is still a very challenging task. Actually, we have studied the popular existing level set approaches and discovered that they failed to segment the images with weak edges or inhomogeneous intensities in many cases. The weak/blurry edges and inhomogeneous intensities cause uncertainty and fuzziness for segmentation. In this paper, a novel fuzzy level set approach is proposed. At first,S-function based on the maximum fuzzy entropy principle (MEP) is used to map the image from space domain to fuzzy domain. Then, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. A partial differential equation is derived for finding the minimum of the energy function. The proposed approach has been tested on both synthetic images and real images and evaluated by several popular metrics. The experimental results demonstrate that the proposed approach can locate the true object boundaries, even for objects with blurry boundaries, low contrast, and inhomogeneous intensities.

scholarly journals Liver Segmentation: A Weakly End-to-End Supervised Model

2020 ◽  
Vol 16 (09) ◽  
pp. 77
Author(s):  
Youssef Ouassit ◽  
Reda Moulouki ◽  
Mohammed Yassine El Ghoumari ◽  
Mohamed Azzouazi ◽  
Soufiane Ardchir
Keyword(s):  
Training Data ◽  
Liver Segmentation ◽  
Low Contrast ◽  
Abdominal Organs ◽  
Model Combining ◽  
Volumetric Assessment ◽  
Appropriate Treatment ◽  
Proposed Model ◽  
End To End ◽  
Images Processing

Liver segmentation in CT images has multiple clinical applications and is expanding in scope. Clinicians can employ segmentation for pathological diagnosis of liver disease, surgical planning, visualization and volumetric assessment to select the appropriate treatment. However, segmentation of the liver is still a challenging task due to the low contrast in medical images, tissue similarity with neighbor abdominal organs and high scale and shape variability. Recently, deep learning models are the state of art in many natural images processing tasks such as detection, classification, and segmentation due to the availability of annotated data. In the medical field, labeled data is limited due to privacy, expert need, and a time-consuming labeling process. In this paper, we present an efficient model combining a selective pre-processing, augmentation, post-processing and an improved SegCaps network. Our proposed model is an end-to-end learning, fully automatic with a good generalization score on such limited amount of training data. The model has been validated on two 3D liver segmentation datasets and have obtained competitive segmentation results.

Two-phase flow stability analysis using a level set approach

2017 ◽  
Author(s):  
Mamta Raju Jotkar ◽  
Daniel Rodriguez ◽  
Bruno Marins Soares
Keyword(s):  
Stability Analysis ◽  
Level Set ◽  
Two Phase Flow ◽  
Flow Stability ◽  
Phase Flow ◽  
Two Phase ◽  
Level Set Approach

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

scholarly journals Identification of Skin Lesions by Using Single-Step Multiframe Detector

2021 ◽  
Vol 10 (1) ◽  
pp. 144
Author(s):  
Yu-Ping Hsiao ◽  
Chih-Wei Chiu ◽  
Chih-Wei Lu ◽  
Hong Thai Nguyen ◽  
Yu Sheng Tseng ◽  
...  
Keyword(s):  
Ground Truth ◽  
Skin Lesions ◽  
Single Step ◽  
Single Shot ◽  
Accuracy Rate ◽  
Tissue Slices ◽  
Image Detection ◽  
Sensitivity Rate ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1-19
Author(s):  
Sreelakshmy I. J. ◽  
Binsu C. Kovoor
Keyword(s):  
Patch Size ◽  
Image Inpainting ◽  
Qualitative Evaluation ◽  
Natural Images ◽  
External Information ◽  
Image Editing ◽  
Model Combining ◽  
Proposed Model ◽  
The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

Voice And Stream: Perceptual And Computational Modeling Of Voice Separation

2008 ◽  
Vol 26 (1) ◽  
pp. 75-94 ◽  
Author(s):  
Emilios Cambouropoulos
Keyword(s):  
Computational Modeling ◽  
Specific Problem ◽  
Cognitive Task ◽  
Ground Truth ◽  
Musical Score ◽  
Auditory Stream ◽  
Multiple Voices ◽  
Proposed Model ◽  
Single Voice ◽  
Small Dataset

LISTENERS ARE THOUGHT TO BE CAPABLE of perceiving multiple voices in music. This paper presents different views of what 'voice' means and how the problem of voice separation can be systematically described, with a view to understanding the problem better and developing a systematic description of the cognitive task of segregating voices in music. Well-established perceptual principles of auditory streaming are examined and then tailored to the more specific problem of voice separation in timbrally undifferentiated music. Adopting a perceptual view of musical voice, a computational prototype is developed that splits a musical score (symbolic musical data) into different voices. A single 'voice' may consist of one or more synchronous notes that are perceived as belonging to the same auditory stream. The proposed model is tested against a small dataset that acts as ground truth. The results support the theoretical viewpoint adopted in the paper.

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