ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach

Rodrigues, Érick Oliveira; Conci, Aura; Liatsis, Panos

doi:10.1109/jbhi.2020.2999257

Cited by 54 publications

(31 citation statements)

References 71 publications

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“…However, region growing alone cannot produce sufficiently accurate segmentation results. In this paper, we use redesigned region growing coupled with machine learning-driven pixel classification (Rodrigues et al, 2020) to improve the adaptivity. The pixels belonging to vascular structures are updated in time as the per-pixel classification process advances.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

Sun

Wang

et al. 2022

Microsc Microanal

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In vivo transparent vessel segmentation is important to life science research. However, this task remains very challenging because of the fuzzy edges and the barely noticeable tubular characteristics of vessels under a light microscope. In this paper, we present a new machine learning method based on blood flow characteristics to segment the global vascular structure in vivo. Specifically, the videos of blood flow in transparent vessels are used as input. We use the machine learning classifier to classify the vessel pixels through the motion features extracted from moving red blood cells and achieve vessel segmentation based on a region-growing algorithm. Moreover, we utilize the moving characteristics of blood flow to distinguish between the types of vessels, including arteries, veins, and capillaries. In the experiments, we evaluate the performance of our method on videos of zebrafish embryos. The experimental results indicate the high accuracy of vessel segmentation, with an average accuracy of 97.98%, which is much more superior than other segmentation or motion-detection algorithms. Our method has good robustness when applied to input videos with various time resolutions, with a minimum of 3.125 fps.

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Section: Methodsmentioning

confidence: 99%

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

Sun

Wang

et al. 2022

Microsc Microanal

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“…In the following, the way the area-based methods work is explained step by step [ 47 , 48 ]: The number of initial seeds is the beginning of the algorithm With the use of these seeds, the regions start their growth, and the pixels that resemble the original pixels will be added to that area Once the growth of area stops, the subsequent grain is taken into consideration, and the next area growth continues The above-mentioned steps will be continued until all of the pixels that exist in the image belong to one area …”

Section: Extended Growth Region Methodsmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C‐Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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“…We are able to predict the category of new emerald stones after training with previously labeled emerald stones. Supervised learning is well established and vastly employed in fields such as computer science and engineering, and in a great number of sub-areas, such as robotics [7], computer vision [20,25], data mining and knowledge discovery [4], health care [21,27] and many others.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset

Pena

Crabi

Izidoro

et al. 2021

Pattern Anal Applic

Self Cite

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The grading of gemstones is currently a manual procedure performed by gemologists. A popular approach uses reference stones, where those are visually inspected by specialists that decide which one of the available reference stone is the most similar to the inspected stone. This procedure is very subjective as different specialists may end up with different grading choices. This work proposes a complete framework that entails the image acquisition and goes up to the final stone categorization. The proposal is able to automate the entire process apart from including the stone in the created chamber for the image acquisition. It discards the subjective decisions made by specialists. This is the first work to propose a machine learning approach coupled with image processing techniques for emerald grading. The proposed framework achieves 98% of accuracy (correctly categorized stones), outperforming a deep learning approach. Furthermore, we also create and publish the used dataset that contains 192 images of emerald stones along with their extracted and pre-processed features.

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ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach

Cited by 54 publications

References 71 publications

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C‐Means

Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset

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